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    [{"authors":null,"categories":null,"content":"Berkeley Lab Computing Sciences Research performs extensive research in cybersecurity. Its mission-driven emphasis focuses on security for science, including high-performance computing, high-throughput networking environments, and research intrumentation; security of cyber-physical systems, notably in the power grid; and security of nuclear arms control monitoring systems. These projects include collaborations with numerous other academic, National Lab, and industry partners. Recent research sponsors have included the Department of Energy (DOE) Advanced Scientific Computing Research (ASCR) and Cybersecurity for Energy Delivery Systems (CEDS) research programs, the National Nuclear Security Administration (NNSA), the National Science Foundation (NSF) Secure and Trustworthy Computing (SaTC) program and Office of Advanced Cyberinfrastructure (OAC), the U.S. Department of Homeland Security’s Science and Technology Directorate, and the National Security Agency. Berkeley Lab’s cybersecurity goals are to research, develop, evaluate, adapt, and integrate advanced security and privacy solutions that enable or improve scientific workflows that may otherwise not be possible due to real or perceived security restrictions that, using today’s solution, impose onerous usability and/or performance constraints, thereby hindering effective solutions.\nBerkeley Lab has had a leadership role in security in scientific computing environments and research cyberinfrastructure for many years, including the development of the Zeek (Bro) Network Security Monitor, as well as leading several DOE-sponsored activities related to defining a cybersecurity research program within the DOE. Berkeley Lab is the lead institution of Trusted CI, the NSF Cybersecurity Center of Excellence.\nRecent highlights of LBNL Computing Sciences’ cybersecurity research activities include:\nLeading studies into scientific data integrity, scientific data confidentiality, and software assurance in science, operational technology in science, and building security into NSF Major Facilities by design. ⇒ The latter is directly impacting design, construction, and operations of the California Coastal Research Vessel, the NSF Regional Class Research Vessels, U.S. Antarctic Program’s $1B icebreaker, and Ocean Observatory Initiatives’ replacement of hundreds of underwater autonomous vehicles.\nDeveloped definitions and research roadmaps for hardware/software co-design of future HPC systems, high-throughput networks, and networked scientific instruments to build cybersecurity in by design. ⇒ Led directly to HPC cybersecurity elements of DOE funding solicitations and has been central to NIST HPC Security working group.\nDevelopment of secure computation architectures optimized for scientific computing to ensure trustworthiness of scientific data from the edge to the HPC center.\nDevelopment and application of differential privacy to power grid and vehicle mobility data and applications ⇒ The DOE Office of Cybersecurity, Energy Security, and Emergency Response (CESER) is seeking to deploy the former operationally and the latter has already enabled mobility research otherwise not possible due to data sharing restrictions.\nDeveloped the first practical approaches to integrate physics of operational technology in the power grid with intrusion detection to ensure their secure operation. ⇒ Now broadly used in applied research efforts globally, and appear in DOE funding solicitations and Congressional budget appropriations.\nCo-leading the Open Science Cyber Risk Profile (OSCRP) working group — an approach to help research cyberinfrastructure operators understand cyber risks. ⇒ Now a recommended reference in all NSF CICI solicitations since 2018 and the NSF Research Infrastructure Guide (RIG) (21-107, Dec. 2021).\nCodification of the “Medical Science DMZ” — a “network design pattern” for enabling secure, high-volume, high-throughput transfer of sensitive data, such as data subject to HIPAA or CUI regulations. ⇒ Now used by companies and research institutions globally, including the NSF Global Research Platform.\nRecent News Cybersecurity Center of Excellence Receives Five-Year, $6M/Year Award From NSF [expanded announcement] — Oct. 3, 2024\nAnnouncing publication of the Operational Technology Procurement Vendor Matrix — Dec. 15, 2023\nBerkeley Lab Leading the Way with New Cybersecurity Projects — Nov. 6, 2023\nUpdates on Trusted CI’s Efforts in Cybersecurity by Design of NSF Academic Maritime Facilities — Jul. 24, 2023\nRegistration Open for 3rd HPC Security Workshop at NIST NCCoE — Feb. 3, 2023\nAnnouncing the 2023 Trusted CI Annual Challenge: Building Security Into NSF Major Facilities By Design — Jan. 25, 2023\nPublication of the Trusted CI Roadmap for Securing Operational Technology in NSF Scientific Research — Nov. 16, 2022.\nOpen Science Cyber Risk Profile (OSCRP) Updated with Science DMZ, Software Assurance, Operational Technology, and Cloud Computing Elements — Nov. 1, 2022\nScientific …","date":1372636800,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1372636800,"objectID":"2525497d367e79493fd32b198b28f040","permalink":"","publishdate":"2017-01-01T00:00:00Z","relpermalink":"","section":"authors","summary":"Berkeley Lab Computing Sciences Research performs extensive research in cybersecurity. Its mission-driven emphasis focuses on security for science, including high-performance computing, high-throughput networking environments, and research intrumentation; security of cyber-physical systems, notably in the power grid; and security of nuclear arms control monitoring systems. These projects include collaborations with numerous other academic, National Lab, and industry partners. Recent research sponsors have included the Department of Energy (DOE) Advanced Scientific Computing Research (ASCR) and Cybersecurity for Energy Delivery Systems (CEDS) research programs, the National Nuclear Security Administration (NNSA), the National Science Foundation (NSF) Secure and Trustworthy Computing (SaTC) program and Office of Advanced Cyberinfrastructure (OAC), the U.S. Department of Homeland Security’s Science and Technology Directorate, and the National Security Agency. ","tags":null,"title":"Cybersecurity Research for Science and Energy at the Berkeley Lab","type":"authors"},{"authors":[],"categories":null,"content":" Click on the Slides button above to view the built-in slides feature. Slides can be added in a few ways:\nCreate slides using Wowchemy’s Slides feature and link using slides parameter in the front matter of the talk file Upload an existing slide deck to static/ and link using url_slides parameter in the front matter of the talk file Embed your slides (e.g. Google Slides) or presentation video on this page using shortcodes. Further event details, including page elements such as image galleries, can be added to the body of this page.\n","date":1906549200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1906549200,"objectID":"a8edef490afe42206247b6ac05657af0","permalink":"https://secpriv.lbl.gov/talk/example-talk/","publishdate":"2017-01-01T00:00:00Z","relpermalink":"/talk/example-talk/","section":"event","summary":"An example talk using Wowchemy's Markdown slides feature.","tags":[],"title":"Example Talk","type":"event"},{"authors":null,"categories":null,"content":" Principal Investigators and Site Leads (October 2024 Onwards) Jim Basney (Co-PI, Senior Advisor, and Site Lead at UIUC/NCSA [PI and Director, 2022-Sept. 2024]) Carolyn Ellis (Co-PI and Site Lead at ASU) Mary Ann Leung (Site Lead at SHI) James A. Marsteller (Co-PI and Site Lead at PSC) Barton Miller (Site Lead at UW-Madison) Sean Peisert (PI and Director, Site Lead at LBNL) Scott Russell (Deputy Director, IU) Kelli Shute (Executive Director, Co-PI, and Site Lead at IU)\nAdditional LBNL Staff Daniel Arnold Daniel Gunter Drew Paine Damian Rouson\nLBNL Project Alumni Reinhard Gentz Jason R. Lee\nFormer PIs and Site Leads: Kathy Benninger (Former Site Lead at PSC) Dana Brunson (Former Co-PI / Site Lead at Internet2) Randal Butler (Former Co-PI at UIUC) Von Welch (Former PI and Founding Director, IU)\nThe mission of Trusted CI, the National Science Foundation Cybersecurity of Excellence, is to improve the cybersecurity of NSF computational science and engineering projects, while allowing those projects to focus on their science endeavors.\nAs the National Science Foundation Cybersecurity Center of Excellence, Trusted CI draws on expertise from multiple internationally recognized institutions, including Indiana University, the University of Illinois, the University of Wisconsin-Madison, the Pittsburgh Supercomputing Center, and Berkeley Lab. Drawing on this expertise, Trusted CI collaborates with NSF-funded research organizations to focus on addressing the unique cybersecurity challenges faced by such entities. In addition to our leadership team, a world-class Trusted CI Advisory Committee adds its experience and a critical eye to the center’s strategic decision-making.\nBerkeley Lab is the lead institution for Trusted CI. Berkeley Lab’s additional roles in Trusted CI have included co-leading the Open Science Cyber Risk Profile (OSCRP) working group – a cross-disciplinary group of computer security professionals and scientific researchers that worked to develop a document designed to help researchers understand the cyber risks to their work. It has also led studies into scientific data integrity, scientific data confidentiality, software assurance in science, the security of operational technology in science, and building security into NSF Major Facilities by design.\nThis project is supported by award # ACI-1547272, OAC-1920430, and OAC-2241313 from the National Science Foundation’s Office of Advanced Cyberinfrastructure.\nRead more at the Trusted CI web site.\nNews about from LBNL’s involvement with this center: Cybersecurity Center of Excellence Receives Five-Year, $6M/Year Award From NSF [expanded announcement] — Oct. 3, 2024\n“Mind the gap: Speaking like a cybersecurity pro,” Science Node, February 10, 2017.\nSelected Trusted CI blog posts involving LBNL’s personnel: Announcing publication of the Operational Technology Procurement Vendor Matrix — Dec. 15, 2023\nSAVE THE DATE: Announcing the 2023 NSF Cybersecurity Summit, Oct 24-26, 2023 in Berkeley, CA — Mar. 21, 2023\nRegistration Open for 3rd HPC Security Workshop at NIST NCCoE — Feb. 3, 2023\nSean Peisert, Publication of the Trusted CI Roadmap for Securing Operational Technology in NSF Scientific Research — Nov. 16, 2022.\nSean Peisert, Open Science Cyber Risk Profile (OSCRP) Updated with Science DMZ, Software Assurance, Operational Technology, and Cloud Computing Elements — Nov. 1, 2022\nSean Peisert, Findings of the 2022 Trusted CI Study on the Security of Operational Technology in NSF Scientific Research — July 15, 2022.\nSean Peisert, Berkeley Lab’s Sean Peisert Tapped to Take on Deputy Director Role — June 28, 2022\nSean Peisert, Announcement of Trusted CI Director Transition — June 27, 2022\nSean Peisert, “Better Scientific Software (BSSw) Helps Promote Trusted CI Guide to Securing Scientific Software,” May 13, 2022.\nElisa Heyman Pignolo, Barton Miller, and Sean Peisert, “Trusted Cyberinfrastructure Evaluation, Guidance, and Programs for Assurance of Scientific Software,” May 13, 2022.\nSean Peisert, “Announcing the 2022 Trusted CI Annual Challenge on Scientific OT/CPS Security,” Jan. 5, 2022.\nSean Peisert, “Publication of the Trusted CI Guide to Securing Scientific Software,” Trusted CI Blog Post, Dec. 14, 2021.\nSean Peiert, “Findings Report of the 2021 Trusted CI Annual Challenge on Software Assurance Published,” Trusted CI Blog Post, Sept. 29, 2021\nSean Peiert, “Trusted CI new co-PIs: Peisert and Shute,” Trusted CI Blog Post, Aug. 3, 2021\nSean Peisert, “Initial Findings of the 2021 Trusted CI Annual Challenge on Software Assurance,” Trusted CI Blog Post, Aug. 3, 2021\nSean Peisert, “Announcing the 2021 Trusted CI Annual Challenge on Software Assurance,” Trusted CI Blog Post, Mar. 30, 2021.\nSean Peisert, “Data Confidentiality Issues and Solutions in Academic Research Computing,” Trusted CI Blog Post, September 10, 2020.\nAnurag Shankar, “Trusted CI Completes a Highly Successful Engagement with UC Berkeley,” Trusted CI Blog Post, July 21, 2020.\nSean Peisert, “Fantastic …","date":1726012800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1726012800,"objectID":"dd54f73550de55da58845ff8187e42b2","permalink":"https://secpriv.lbl.gov/project/nsf-trustedci/","publishdate":"2024-09-11T00:00:00Z","relpermalink":"/project/nsf-trustedci/","section":"project","summary":"The mission of Trusted CI is to improve the cybersecurity of NSF computational science and engineering projects, while allowing those projects to focus on their science endeavors.  [Sean Peisert](https://www.cs.ucdavis.edu/~peisert/) is the Director- and PI-Designate of Trusted CI.","tags":["research cyberinfrastructure","software assurance","cyber-physical systems","secure systems","maritime"],"title":"Trusted CI, the National Science Foundation Cybersecurity of Excellence","type":"project"},{"authors":null,"categories":null,"content":"Background The Mitigation via Analytics for Grid-Inverter Cybersecurity (MAGIC) project will develop secure Artificial Intelligence/Machine Learning (AI/ML) tools to both detect and mitigate cyber attacks on aggregations of Distributed Energy Resources (DER) in electric power distribution systems and microgrids. In so doing, MAGIC will facilitate detecting cyber attacks on DER in their earliest stages and ameliorating the effect of attacks immediately.\nObjectives Develop secure AI/ML algorithms to detect cyber attacks on aggregations of DER and distinguish attacks from normal operating conditions. Extend a reinforcement learning framework developed in previous RMT/CEDS projects to mitigate the effect of cyber attacks on DER in a wide array of operating conditions. Integrate the attack detection and mitigation algorithms into a commercially available substation/microgrid management platform for algorithm demonstration. Create an open source simulation tool allowing electric utilities to determine rules to detect and mitigate cyber attacks designed to severely disrupt normal grid operations or cause voltage instabilities. Develop a software test harness to assess the security of AI/ML algorithms for electric grid attack detection and mitigation. Project Description The Supervisory Parameter Adjustment for Distribution Energy Storage (SPADES) project will develop the methodology and tools allowing Energy Storage Systems (ESS) to automatically reconfigure themselves to counteract cyberattacks against both the ESS control system directly and indirectly through the electric distribution grid. This research will begin with an effort to analyze the stability of both ESS control systems and the interaction of the ESS control system and the electric grid, to determine what parameters an attacker would change if a given device (or multiple devices) were to be compromised. Then, the research team will develop a supervisory control framework that utilizes adaptive control and reinforcement learning techniques to adjust ESS control system parameters and ESS active and reactive power injections to actively defend against a variety of cyberattacks. The supervisory control framework will be validated in Hardware-in-the-Loop (HIL) experiments where an independent red team will attempt to alter control parameters of an ESS to prevent the device from providing grid services. The reinforcement learning defensive algorithms will be integrated into the National Rural Electric Cooperative Association (NRECA) Open Modeling Framework (OMF), thereby allowing defensive strategies to be tailored on a utility specific basis. The major outcomes of this project will be the tools to isolate the component of the ESS control system that has been compromised during a cyberattack as well as policies for changing the control parameters of ESS to mitigate a wide variety of cyberattacks on both the ESS device itself and the electric distribution grid.\nThis project is supported by the U.S. Department of Energy’s Office of Cybersecurity, Energy Security, and Emergency Response (CESER) Risk Management Tools and Technologies (RMT) Program.\nPrincipal Investigator: Daniel Arnold (PI; LBNL)\nSenior Personnel: Sean Peisert (LBNL) Ryan King (NREL) Lisa Slaughter (NRECA) Anna Scaglione (Cornell Tech) Bruno Leao (Siemens)\nPress regarding this project: DOE Press Release: “DOE Announces $39 Million in Research Funding to Enhance Cybersecurity of Clean Distributed Energy Resources” - Sept. 12, 2023\nBerkeley Lab leading the way with new cybersecurity projects - Nov. 6, 2023\nMore information is available on other Berkeley Lab research projects focusing on cybersecurity in general, as well as specifically on cybersecurity for energy delivery systems.\n","date":1708992000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1708992000,"objectID":"095cb5092c40023fa03280c0c560cecc","permalink":"https://secpriv.lbl.gov/project/rmt-magic/","publishdate":"2024-02-27T00:00:00Z","relpermalink":"/project/rmt-magic/","section":"project","summary":"Project MAGIC will develop artificial intelligence and machine learning algorithms to detect and mitigate cyber attacks on aggregations of Distributed Energy Resources (DER).  The developed algorithms will be demonstrated in hardware-in-the-loop testing and integrated into an open source simulation tool.  It is funded by DOE CESER's RMT program and is led by [Daniel Arnold](https://eta.lbl.gov/people/daniel-arnold).","tags":["power grid","machine learning"],"title":"Mitigation via Analytics for Inverter-Grid Cybersecurity (MAGIC)","type":"project"},{"authors":null,"categories":null,"content":"Project Summary This project aims to develop, apply, and test a technique for enabling collective defense of distribution grids with significant penetration of distributed energy resources (DER) and responsive loads (particularly Electric Vehicles), by leveraging a privacy-preserving method of data sharing without exposing raw data that might contain personally identifiable information (PII) from individual consumers or buildings or that might otherwise be considered national security information that could be leveraged by adversaries to more effectively compromise and potentially destabilize portions of the electric grid. We envision creating a software platform to allow utilities to share relevant cybersecurity information with one another in a manner that does not compromise the privacy of customers in their service territories. In doing so, we hope to reduce the reluctance of utilities to share information that can be used to harden other networks by reducing privacy-related liabilities associated with grid operational technology (OT) data.\nThis project is supported by the U.S. Department of Energy’s Office of Cybersecurity, Energy Security, and Emergency Response (CESER) Risk Management Tools and Technologies (RMT) program.\nDOE Press Release: “DOE Announces $39 Million in Research Funding to Enhance Cybersecurity of Clean Distributed Energy Resources,” September 12, 2023.\nPrincipal Investigator: Sean Peisert (PI; LBNL)\nCo-Leads: Daniel Arnold (Co-PI; LBNL) Anna Scaglione (Co-PI; Cornell Tech) Aram Shumavon (Co-PI; Kevala) Parth Pradhan (Kevala) Ryan Cryar (NREL)\nPostdocs: Dr. Hang Liu (Cornell Tech)\nGraduate Students: Andrew Campbell (Cornell Tech)\nAdditional Researchers Dr. Tong Wu (University of Central Florida)\nPast Researchers: Nikhil Ravi (Cornell Tech)\nPartners: Cornell Tech Kevala, Inc. National Renewable Energy Laboratory (NREL)\nPress regarding this project: Berkeley Lab Leading the Way with New Cybersecurity Projects — Nov. 6, 2023\nPresentations relating to this project: Sean Peisert, “LBNL Electric Grid Cybersecurity research and Trustworthy Scientific Cyberinfrastructure,” Networking and Information Technology Research and Development (NITRD) Cyber Security and Information Assurance (CSIA) Interagency Working Group (IWG) Meeting, May 23, 2024.\nPublications resulting from this project: Hang Liu, Anna Scaglione, and Sean Peisert, “Privacy Leakage in Graph Signal to Graph Matching Problems,” Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, Seoul, South Korea, April 14–19, 2024.\nNikhil Ravi, Anna Scaglione, Sean Peisert, and Parth Pradhan, “Differentially Private Communication of Measurement Anomalies in the Smart Grid,” arXiv preprint arXiv:2403.02324, 4 Mar 2024.\nMore information is available on other Berkeley Lab research projects focusing on cybersecurity in general, as well as specifically on cybersecurity for energy delivery systems.\n","date":1695772800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1695772800,"objectID":"35eb7784318c164a7ebe7965285c7c4f","permalink":"https://secpriv.lbl.gov/project/ceser-shielders/","publishdate":"2023-09-27T00:00:00Z","relpermalink":"/project/ceser-shielders/","section":"project","summary":"This project aims to develop, apply, and test a technique for enabling collective defense of distribution grids with significant penetration of distributed energy resources (DER) and responsive loads, by leveraging a privacy-preserving method of data sharing without exposing raw data that might contain personally identifiable information (PII) or that might otherwise be considered national security information that could be leveraged by adversaries to more effectively compromise and potentially destabilize portions of the electric grid.   It is funded by DOE CESER's RMT program and is led by [Sean Peisert](https://www.cs.ucdavis.edu/~peisert/).","tags":["differential privacy","power grid","machine learning","cyber-physical systems","data privacy","AI"],"title":"Privacy-Preserving, Collective Cyberattack Defense of DERs","type":"project"},{"authors":null,"categories":null,"content":"Final Project Report Overview The SPADES project concluded work in July of 2023. The final report from the SPADES project is included below:\nFinal Project Report\n","date":1688169600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1688169600,"objectID":"257aadaa51fb4cad4323a82d1e2110ff","permalink":"https://secpriv.lbl.gov/talk/ceds_spades_y3_report/","publishdate":"2023-07-01T00:00:00Z","relpermalink":"/talk/ceds_spades_y3_report/","section":"talk","summary":"Final Project Report Overview The SPADES project concluded work in July of 2023. The final report from the SPADES project is included below:\nFinal Project Report\n","tags":["power grid","machine learning"],"title":"Supervisory Parameter Adjustment for Distribution Energy Storage (SPADES) - Year 3 Report","type":"talk"},{"authors":null,"categories":null,"content":"Project Summary It is essential to have the ability to verify that software operating on arms control monitoring equipment is within agreed parameters and has not undergone modification. Existing approaches to ensuring the absence of tampering are outdated and are easily defeated by a motivated adversary. Specifically, we use a coverage-guided, gray-box fuzzing approach to test the response of binaries to a wide range of inputs to increase confidence that the binary is behaving as expected. Our approach brings modern software analysis tools to bear to address today’s challenges. Success in our project would enable identification of a critical mass of alterations of a software’s logic that have the potential to affect the output of the nuclear monitoring software, and provide a different result to inspectors.\nThis project is supported by the National Nuclear Security Administration Office of Defense Nuclear Nonproliferation Research and Development.\nPrincipal Investigators: Sean Peisert (PI; LBNL) Barton Miller (Co-PI; University of Wisconsin-Madison)\nSenior Personnel: Joshua Boverhof (LBNL) Elisa Heymann Pignolo (University of Wisconsin-Madison) Jayson Vavrek (LBNL)\nCollaborators: Jay Brotz (Sandia) James Davis (Sandia)\nStudents: Lawrence Su (University of Wisconsin-Madison)\nFormer Students: Luozhong Zhou (B.S. 2024, University of Wisconsin-Madison → M.S. program, MIT)\nPublications resulting from this project: Jayson R. Vavrek, Luozhong Zhou, Joshua Boverhof, Elisa R. Heymann, Barton P. Miller, and Sean Peisert. Differential Fuzz Testing to Detect Tampering In Sensor Systems and its Application to Arms Control Authentication, arXiv preprint 2404.05946, 9 Apr 2024.\nMore information is available on other Berkeley Lab research projects focusing on cybersecurity.\n","date":1675209600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1675209600,"objectID":"547f948aa0dbbdbc52e6981d266a4444","permalink":"https://secpriv.lbl.gov/project/nnsa-fuzzing/","publishdate":"2023-02-01T00:00:00Z","relpermalink":"/project/nnsa-fuzzing/","section":"project","summary":"This project aims verify that software operating on arms control monitoring equipment is within agreed parameters.  It is funded by the National Nuclear Security Administration Office of Defense Nuclear Nonproliferation Research and Development and is led by [Sean Peisert](https://https://www.cs.ucdavis.edu/~peisert/).","tags":["nuclear treaty assurance","cyber-physical systems"],"title":"Using Fuzz Testing to Detect Software Tampering","type":"project"},{"authors":null,"categories":null,"content":"Principal Investigators: Sean Peisert Venkatesh Akella Jason Lowe-Power\nResearch Staff: Farzad Fatollahl-Fard Dr. Paul Hargrove\nAffiliated Graduate Students: Kaustav Goswami (UC Davis)\nPast Researchers: Ayaz Akram (UC Davis / LBNL)\nScientific data today is at risk due to how it is collected, stored, and analyzed in highly disparate computing systems. We believe that in order to solve the problems described above that future HPC hardware and software solutions should be co-designed together with security and scientific computing integrity concepts designed and built into as much of the stack from the outset as possible.\nThis project is developing new architectures appropriate to the performance and usage needs of scientific computing to secure scientific data from the edge to the HPC center. This includes includes sensor and edge systems that collect and process of that data takes place outside protection boundaries of traditional HPC centers, including against attacks such as ransomware and physical attacks against the computing system. Our approach will address the gaps left by existing solutions for scientific workflows to address the specific power, performance, and usability, and needs from the edge to the HPC center.\nThis project is supported by the US Department of Energy’s Office of Science’s Advanced Scientific Computing Research (ASCR) program under the following grants:\n“Toward a Hardware/Software Co-Design Framework for Ensuring the Integrity of Exascale Scientific Data,” PI: Sean Peisert, 2015.\n“Cybersecurity for Edge-to-Center Scientific Computing in Advanced Wireless Environments,” PI: Sean Peisert, Co-PIs: Venkatesh Akella and Jason Lowe-Power, 2021.\nIt is also funded by NNSA DNN research:\n“Data Enclaves for Secure Computing (DESC): Enabling Secure Nuclear Treaty Verification in Hostile Environments,” PI: Sean Peisert, Co-PIs: Venkatesh Akella, David Archer, and Jason Lowe-Power, 2024.\nIt is also funded by LBNL Contractor Supported Research.\nSee also the hardware-software security page at DArchR, the UC Davis Architecture Research group.\nPress regarding this project: Berkeley Lab Cybersecurity Specialist Highlights Data Sharing Benefits, Challenges at NAS Meeting — Dec. 4, 2018\nCRD’s Peisert to Discuss Data Sharing at National Academies’ COSEMPUP Meeting — Nov. 5, 2018\nLab Experts Help Coordinate ISC18, World’s First, Largest Computing Conference - June 21, 2018\nPublications resulting from this project: Ayaz Akram, Venkatesh Akella, Sean Peisert, and Jason Lowe-Power, “SoK: Limitations of Confidential Computing via TEEs for High-Performance Compute Systems,” Proceedings of the 2022 IEEE International Symposium on Secure and Private Execution Environment Design (SEED), Sept. 26–27, 2022.\nAyaz Akram, Venkatesh Akella, Sean Peisert, and Jason Lowe-Power, “Enabling Design Space Exploration for RISC-V Secure Compute Environments,” Proceedings of the Fifth Workshop on Computer Architecture Research with RISC-V (CARRV), (co-located with ISCA 2021) June 17, 2021\nSean Peisert, “Trustworthy Scientific Computing,” Communications of the ACM (CACM), 64(5), pp. 18–21, May 2021.\nAyaz Akram, Anna Giannakou, Venkatesh Akella, Jason Lowe-Power, and Sean Peisert, “Performance Analysis of Scientific Computing Workloads on General Purpose TEEs,” Proceedings of the 35th IEEE International Parallel \u0026amp; Distributed Processing Symposium (IPDPS), May 17–21, 2021.\nAyaz Akram, “Trusted Execution for High-Performance Computing,” Proceedings of the 15th EuroSys Doctoral Workshop (EuroDW), 2021. video\nAyaz Akram, “Architectures for Secure High-Performance Computing,” Proceedings of the Young Architect Workshop (YArch) held in conjunction with the International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), April 2021. video\nAyaz Akram, Anna Giannakou, Venkatesh Akella, Jason Lowe-Power, and Sean Peisert, “Performance Analysis of Scientific Computing Workloads on Trusted Execution Environments,” arXiv preprint arXiv:2010.13216, 25 Oct 2020.\nPresentations: Sean Peisert, “Trustworthy Scientific Cyberinfrastructure,” NASEM Cyber Resilience Forum Summer 2023 Meeting, San Francisco, CA, August 31, 2023.\nKeynote: “Usable Computer Security and Privacy to Enable Data Sharing in High-Performance Computing Environments,” 3rd High-Performance Computing Security Workshop, NIST National Cybersecurity Center of Excellence (NCCoE), Rockville, MD, March 16, 2023. NIST IR 8476 Workshop Report\nKeynote: “Usable Computer Security and Privacy to Enable Data Sharing in High-Performance Computing Environments,” Interdisciplinary Symposium on Responsible Innovation: Intersection of Privacy and Artificial Intelligence, Center for Data Science and AI Research (CeDAR), University of California, Davis, March 10, 2023.\n“Responsible Innovation at the Intersection of Privacy and Artificial Intelligence (AI),” (panel; with Eric Dang, Darci Sears, moderators; Tom Kemp, and Richard Arney) Interdisciplinary …","date":1675036800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1675036800,"objectID":"6bb59f2882615d56f676b40caff5f7ba","permalink":"https://secpriv.lbl.gov/project/desc/","publishdate":"2023-01-30T00:00:00Z","relpermalink":"/project/desc/","section":"project","summary":"This project will develop secure computation architectures to ensure trustworthiness of scientific data while addressing the gaps left by existing solutions for scientific workflows to address the specific power, performance, and usability, and needs from the edge to the HPC center. It is led by [Sean Peisert](https://www.cs.ucdavis.edu/~peisert/), [Venkatesh Akella]( https://faculty.engineering.ucdavis.edu/akella/), and [Jason Lowe-Power](https://faculty.engineering.ucdavis.edu/lowepower/).","tags":["HPC","network","research cyberinfrastructure","secure systems","confidential computing","edge","nuclear treaty assurance"],"title":"Data Enclaves for Scientific Computing","type":"project"},{"authors":null,"categories":null,"content":"Project Summary Data is frequently not shared by organizations because that data is considered by the organization to be in some way sensitive. For example, there may be laws or regulations prohibiting sharing due to personal privacy or national security issues, or the organization owning the data may also consider that data to be a proprietary trade secret. In any case, that data cannot or will not be released in raw form, and so alternative approaches are needed if that data is to be shared at all.\nToday, data is often not shared at all, or if it is shared, it is done so in ways that require people processing or analyzing that data to access the data in highly secured, non-networked environments set up to prevent any data from being exfiltrated either physically from a building or certainly from a network. This is the reason why much research is hindered. Sometimes data is shared through processes of “anonymization” in which data is typically either masked or made more general. Unfortunately, these techniques have repeatedly been shown to fail, typically by merging external information containing identifiable information with quasi-identifiers contained in the dataset in order to identify “anonymized” records in the dataset.\nThis project aims to develop a method of leveraging a variety of hardware and software apparoaches, in concert with privacy-preserving technologies, such as differential privacy, for the scientific analysis of sensitive data, in order to provide significantly greater confidence to the owner of a set of sensitive data that the data will not be exposed or altered, and also reduce the liability exposure of the data center to assertions of security negligence or insider attacks by providing an environment in which even they cannot access the raw data, all without significant negative impacts to usability or performance. The environment that we envision that is is both secure and usable, and also has protections against “insiders” such as system administrators leverages techniques that are relatively new, and just becoming practically useful for these purposes.\nThis project is supported by Berkeley Lab Contractor Supported Research funding.\nPrincipal Investigator: Sean Peisert (PI; LBNL)\nSenior Personnel Chen-nee Chuah Jane Macfarlane Michael Zhang\nGraduate Students Ammar Haydari\nPast Researchers Hamdy Elgammal Reinhard Gentz\nPast Students: Archit Garg Chitrabhanu Gupta Jinyue Song Jayneel Vora\nPress regarding this project: Scientific Data Division Summer Students Tackle Data Privacy - Sept. 15, 2022\nSummer Students Tackle COVID-19 - Oct. 21, 2020\nPublications resulting from this project: Ammar Haydari, Chen-Nee Chuah, Michael Zhang, Jane Macfarlane, and Sean Peisert, “Differentially Private Map Matching for Mobility Trajectories,” Proceedings of the 2022 Annual Computer Security Applications Conference (ACSAC), Austin, TX, December 5-9, 2022.\nHector Garcia Martin, Tijana Radivojevic, Jeremy Zucker, Kristofer Bouchard, Jess Sustarich, Sean Peisert, Dan Arnold, Nathan Hillson, Gyorgy Babnigg, Jose Manuel Marti, Christopher J. Mungall, Gregg T. Beckham, Lucas Waldburger, James Carothers, ShivShankar Sundaram, Deb Agarwal, Blake A. Simmons, Tyler Backman, Deepanwita Banerjee, Deepti Tanjore, Lavanya Ramakrishnan, and Anup Singh, “Perspectives for Self-Driving Labs in Synthetic Biology,” arXiv preprint arXiv:2210.09085, 14 Oct 2022.\nAmmar Haydari, Michael Zhang, Chen-Nee Chuah, Jane Macfarlane, and Sean Peisert, “Adaptive Differential Privacy Mechanism for Aggregated Mobility Dataset,” arXiv preprint arXiv:2112.08487, 10 Dec 2021.\nLuca Pion-Tonachini, Kristofer Bouchard, Hector Garcia Martin, Sean Peisert, W. Bradley Holtz, Anil Aswani, Dipankar Dwivedi, Haruko Wainwright, Ghanshyam Pilania, Benjamin Nachman, Babetta L. Marrone, Nicola Falco, Prabhat, Daniel Arnold, Alejandro Wolf-Yadlin, Sarah Powers, Sharlee Climer, Quinn Jackson, Ty Carlson, Michael Sohn, Petrus Zwart, Neeraj Kumar, Amy Justice, Claire Tomlin, Daniel Jacobson, Gos Micklem, Georgios V. Gkoutos, Peter J. Bickel, Jean-Baptiste Cazier, Juliane Müller, Bobbie-Jo Webb-Robertson, Rick Stevens, Mark Anderson, Ken Kreutz-Delgado, Michael W. Mahoney, James B. Brown, “Learning from Learning Machines: a New Generation of AI Technology to Meet the Needs of Science,” arXiv preprint arXiv:2111.13786, 27 Nov 2021.\nPresentations: Sean Peisert, “Trustworthy Scientific Cyberinfrastructure,” NASEM Cyber Resilience Forum Summer 2023 Meeting, San Francisco, CA, August 31, 2023.\nKeynote: “Usable Computer Security and Privacy to Enable Data Sharing in High-Performance Computing Environments,” 3rd High-Performance Computing Security Workshop, NIST National Cybersecurity Center of Excellence (NCCoE), Rockville, MD, March 16, 2023. NIST IR 8476 Workshop Report\nKeynote: “Usable Computer Security and Privacy to Enable Data Sharing in High-Performance Computing Environments,” Interdisciplinary Symposium on Responsible Innovation: Intersection of Privacy …","date":1674086400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1674086400,"objectID":"e9bdbbbb3ac6994f45d1011277c1fce7","permalink":"https://secpriv.lbl.gov/project/csr-private-data/","publishdate":"2023-01-19T00:00:00Z","relpermalink":"/project/csr-private-data/","section":"project","summary":"This project aims to produce methods, processes, and architectures applicable to a variety of scientific computing domains that enables querying, machine learning, and analysis of data while protecting against releasing sensitive information beyond pre-defined bounds.  It is supported by LBNL CSR funds and is led by [Sean Peisert](https://www.cs.ucdavis.edu/~peisert/).","tags":["differential privacy","machine learning","medical","data privacy","research cyberinfrastructure","secure systems","transportation","confidential computing","AI"],"title":"Privacy-Preserving Data Analysis for Scientific Discovery","type":"project"},{"authors":null,"categories":null,"content":"Project Summary Numerous DOE-relevant processes are becoming automated and adaptive, using machine learning techniques. Such processes include vehicle and traffic navigation guidance, intelligent transportation systems, adaptive control of grid-attached equipment, large scientific instruments.\nThis creates a vulnerability for a cyber attacker to sabotage processes through tainted training data or specially crafted inputs. Consequences might be tainted manufactured output, traffic collisions, power outages, or damage to scientific instruments or experiments. This project is developing secure machine learning methods that will enable safer operation of automated, adaptive, learning-driven “cyber-physical system” processes.\nThis project is supported by Berkeley Lab Contractor Supported Research funding.\nPrincipal Investigators: Sean Peisert (PI; LBNL) Daniel Arnold (Co-PI; LBNL)\nProject Alumni: Bashir Mohammed → DeepMind Yize Chen (Postdoc) → Assistant Professor, University of Hong Kong\nPublications resulting from this project: Yize Chen, Yuanyuan Shi, Daniel Arnold, and Sean Peisert, ”SAVER: Safe Learning-Based Controller for Real-Time Voltage Regulation,” Proceedings of the 2022 IEEE Power Engineering Society (PES) General Meeting, Denver, CO. July 17-21 2022.\nYize Chen, Yuanyuan Shi, Daniel Arnold, and Sean Peisert, “SAVER: Safe Learning-Based Controller for Real-Time Voltage Regulation,” arXiv preprint arXiv:2111.15152, 30 Nov 2021.\nYize Chen, Daniel Arnold, Yuanyuan Shi, and Sean Peisert, “Understanding the Safety Requirements for Learning-based Power Systems Operations,” arXiv preprint arXiv:2110.04983, 11 Oct 2021\nMore information is available on other Berkeley Lab research projects focusing on cybersecurity in general, as well as specifically on cybersecurity for energy delivery systems.\n","date":1664582400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1664582400,"objectID":"57b9c392c2fabd343663fc58d963d1f0","permalink":"https://secpriv.lbl.gov/project/ldrd-secure-ml-control/","publishdate":"2022-10-01T00:00:00Z","relpermalink":"/project/ldrd-secure-ml-control/","section":"project","summary":"This project is developing secure machine learning methods that will enable safer operation of automated, adaptive, learning-driven cyber-physical system processes.  It is supported by LBNL LDRD funds and is led by [Sean Peisert](https://www.cs.ucdavis.edu/~peisert/).","tags":["machine learning","power grid"],"title":"Securing Automated, Adaptive Learning-Driven Cyber-Physical System Processes","type":"project"},{"authors":null,"categories":null,"content":"Project Summary Data is frequently not shared by organizations because that data is considered by the organization to be in some way sensitive. For example, there may be laws or regulations prohibiting sharing due to personal privacy or national security issues, or the organization owning the data may also consider that data to be a proprietary trade secret. In any case, that data cannot or will not be released in raw form, and so alternative approaches are needed if that data is to be shared at all.\nToday, data is often not shared at all, or if it is shared, it is done so in ways that require people processing or analyzing that data to access the data in highly secured, non-networked environments set up to prevent any data from being exfiltrated either physically from a building or certainly from a network. This is the reason why much research is hindered. Sometimes data is shared through processes of “anonymization” in which data is typically either masked or made more general. Unfortunately, these techniques have repeatedly been shown to fail, typically by merging external information containing identifiable information with quasi-identifiers contained in the dataset in order to identify “anonymized” records in the dataset.\nThis project aims to develop techniques for enabling data analysis for the purposes of detecting and/or investigating cyberattacks against energy delivery systems while also preserving aspects of key confidentiality elements within the underlying raw data being analyzed. Specifically, this project proposes to examine the application of privacy-preserving techniques to OT and grid-security-relevant IT data provided by the California Energy Commission (CEC), Kevala, and Portland General Electric, in order to protect privacy as much as possible, thereby minimizing the amount of data for which “traditional” (and vulnerable) anonymization techniques need to be applied. The result will be a solution for anonymization of data collected from OT and IT networks pertaining to energy grid cyberattack detection that has been tested for its ability to retain privacy properties and still enable attack detection.\nThis project is supported by the U.S. Department of Energy’s Cybersecurity for Energy Delivery Systems (CEDS) program.\nDOE Press Release: “Department of Energy Announces Awardees of $30 Million Research Call to Enhance Cybersecurity for Energy Delivery Systems,” August 27, 2019.\nPrincipal Investigator: Sean Peisert (PI; LBNL)\nCo-Leads: Anna Scaglione (Lead at Cornell Tech) Aram Shumavon (Lead at Kevala)\nPostdocs: Tong Wu (Cornell Tech)\nGraduate Students: Andrew Campbell Nikhil Ravi (Cornell Tech) Leah Woldemariam\nPartners: Cornell Tech\nKevala, Inc.\nCalifornia Energy Commission Portland General Electric\nSunPower\nPast Researchers: Rojin Zandi (Cornell Tech) Sachin Kadam (ASU) Daniel Arnold (LBNL) Reinhard Gentz (LBNL) Raksha Ramakrishnan (KTH) Ciaran Roberts (LBNL)\nPast Partners: Arizona State University\nPress regarding this project: Scientific Data Division Summer Students Tackle Data Privacy - Sept. 15, 2022\nPresentations relating to this project: Sean Peisert, “Cybersecurity and Privacy research for Science and Energy,” Networking and Information Technology Research and Development (NITRD) Cyber Security and Information Assurance (CSIA) Interagency Working Group (IWG) Meeting, March 24, 2022.\nPublications resulting from this project: Tong Wu, Anna Scaglione, Adrian Petru Surani, Daniel Arnold, and Sean Peisert, “Network-Constrained Reinforcement Learning for Optimal EV Charging Control,” Proceedings of the IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), October 31–November 3, 2023.\nRobert Currie, Sean Peisert, Anna Scaglione, Aram Shumavon, and Nikhil Ravi, “Data Privacy for the Grid: Toward a Data Privacy Standard for Inverter-Based and Distributed Energy Resources,” IEEE Power \u0026amp; Energy Magazine, 21(5), pp. 58-57, Sept.-Oct 2023.\nSachin Kadam, Anna Scaglione, Nikhil Ravi, Sean Peisert, Brent Lunghino, and Aram Shumavon, “Optimum Noise Mechanism for Differentially Private Queries in Discrete Finite Sets,” Proceedings of the 2023 IEEE International Conference on Smart Applications, Communications and Networking (SmartNets), Istanbul, Turkey, July 25–27, 2023.\nRaksha Ramakrishna, Anna Scaglione, Tong Wu, Nikhil Ravi, and Sean Peisert, “Differential Privacy for Class-based Data: A Practical Gaussian Mechanism,” to appear in IEEE Transactions on Information Forensics and Security, 2023.\nNikhil Ravi, Anna Scaglione, Julieta Giraldez, Parth Pradhan, Chuck Moran, and Sean Peisert, “Solar Photovoltaic Systems Metadata Inference and Differentially Private Publication,” arXiv preprint arXiv:2304.03749 7 Apr 2023.\nNikhil Ravi, Anna Scaglione, Sachin Kadam, Reinhard Gentz, Sean Peisert, Brent Lunghino, Emmanuel Levijarvi, and Aram Shumavon, “Differentially Private K-means Clustering Applied to Meter Data Analysis and Synthesis,” …","date":1661558400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1661558400,"objectID":"7bb66a5c81dc06d19d8443aa6c358664","permalink":"https://secpriv.lbl.gov/project/ceds-privacy/","publishdate":"2022-08-27T00:00:00Z","relpermalink":"/project/ceds-privacy/","section":"project","summary":"This project aims to develop techniques for enabling data analysis for the purposes of detecting and/or investigating cyberattacks against energy delivery systems while also preserving aspects of key confidentiality elements within the underlying raw data being analyzed. The result will be a complete solution for anonymization of data collected from OT and IT networks pertaining to energy grid cyberattack detection that has been tested for its ability to retain privacy properties and still enable attack detection.  It is funded by DOE CESER's CEDS program and is led by [Sean Peisert](https://www.cs.ucdavis.edu/~peisert/).","tags":["differential privacy","power grid","machine learning","cyber-physical systems","data privacy"],"title":"Provable Anonymization of Grid Data for Cyberattack Detection","type":"project"},{"authors":null,"categories":null,"content":"Background Energy Storage Systems (ESS), such as electric batteries, are increasingly becomming critical components of the electric power system. As these devices begin to provide grid services, it is critical to ensure that they are resistant to cyber attacks and cannot be exploited as a mechanism to disrupt the operation of the power grid. Tools from nonlinear and optimal control theory can be utilized to develop control policies to re-dispatch setpoints in both the ESS and the surrounding electric grid infrastructure to mitigate the effect of cyber attacks.\nObjectives Characterize the interaction ESS control systems and Distributed Energy Resource (DER) smart inverter functions on electric grid voltage stability.\nDevelop a supervisory control system (reinforcement learning-based) that will identify and autonomously update control parameters of the power electronic control systems within ESS devices.\nValidate control approaches via Hardware-In-the-Loop (HIL) testing at the LBNL FLEXGRID facility.\nIntegrate the defensive reinforcement learning-based agent into the NRECA Open Modeling Framework (OMF) simulation tool.\nProject Description The Supervisory Parameter Adjustment for Distribution Energy Storage (SPADES) project will develop the methodology and tools allowing Energy Storage Systems (ESS) to automatically reconfigure themselves to counteract cyberattacks against both the ESS control system directly and indirectly through the electric distribution grid. This research will begin with an effort to analyze the stability of both ESS control systems and the interaction of the ESS control system and the electric grid, to determine what parameters an attacker would change if a given device (or multiple devices) were to be compromised. Then, the research team will develop a supervisory control framework that utilizes adaptive control and reinforcement learning techniques to adjust ESS control system parameters and ESS active and reactive power injections to actively defend against a variety of cyberattacks. The supervisory control framework will be validated in Hardware-in-the-Loop (HIL) experiments where an independent red team will attempt to alter control parameters of an ESS to prevent the device from providing grid services. The reinforcement learning defensive algorithms will be integrated into the National Rural Electric Cooperative Association (NRECA) Open Modeling Framework (OMF), thereby allowing defensive strategies to be tailored on a utility specific basis. The major outcomes of this project will be the tools to isolate the component of the ESS control system that has been compromised during a cyberattack as well as policies for changing the control parameters of ESS to mitigate a wide variety of cyberattacks on both the ESS device itself and the electric distribution grid.\nThis project is supported by the U.S. Department of Energy’s Cybersecurity for Energy Delivery Systems (CEDS) program.\nPrincipal Investigator: Daniel Arnold (PI; LBNL)\nSenior Personnel: Sean Peisert (LBNL) Ciaran Roberts (LBNL) Sy-Toan Ngo (LBNL) David Pinney (NRECA) Anna Scaglione (Cornell Tech) Bruno Leao (Siemens)\nPartners: Cornell Tech\nNational Rural Electric Cooperative Association (NRECA)\nSiemens\nProject Workshops and Reports Year 1, held on 12/02/2021 Year 2, held on 12/08/2022 and 12/15/2022 Years 3 and 4\nPress regarding this project: TBD\nPublications resulting from this project: Wu, T., Scaglione, A., and Arnold, D. “Complex-Value Spatio-temporal Graph Convolutional Neural Networks and its Applications to Electric Power Systems AI,” IEEE Transactions on Smart Grid, early access, https://doi.org/10.1109/TSG.2023.3332591\nWu, T., Scaglione, A., and Arnold, D., “Constrained Reinforcement Learning for Predictive Control in Real-Time Stochastic Dynamic Optimal Power Flow,” in IEEE Transactions on Power Systems, https://doi.org/10.1109/TPWRS.2023.3326121\nLeao, B. P., Vempati, J., Bhela, S., Ahlgrim, T., and Arnold, D., “Augmented Digital Twin for Identification of Most Critical Cyberattacks in Industrial Systems”, https://doi.org/10.48550/arXiv.2306.04821\nRoberts, C., Arnold, D., and Callaway, D., “An Online Adaptive Damping Controller for Converter-Interfaced Generation.”, IEEE Transactions on Power Systems, vol. 39, no. 2, pp. 3962-3971, March 2024, http://doi.org/10.1109/TPWRS.2023.3286186\nWu, T., Scaglione, A., and Arnold, D. “Spatio-Temporal Graph Convolutional Neural Networks for Physics-Aware Grid Learning Algorithms,” IEEE Transactions on Smart Grid, vol. 14, no. 5, pp. 4086-4099, Sept. 2023 https://doi.org/10.1109/TSG.2023.3239740\nLosada Carreno, I., Saha, S. S., Scaglione, A., Arnold, D., Ngo, S., and Roberts, C., “Log(v) 3LPF: A Linear Power Flow Formulation for Unbalanced Three-Phase Distribution Systems,” IEEE Transactions on Power Systems, vol. 38, no. 1, pp. 100-113, Jan. 2023, doi: https://doi.org/10.1109/TPWRS.2022.3166725\nArnold, D., Saha, S. S., Ngo, S., Roberts, C., Scaglione, A., Johnson, N., Peisert, …","date":1661212800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1661212800,"objectID":"0b24aad682cf9927a57aba7c64d690c5","permalink":"https://secpriv.lbl.gov/project/ceds-spades/","publishdate":"2022-08-23T00:00:00Z","relpermalink":"/project/ceds-spades/","section":"project","summary":"This project is developing the methodology and tools allowing Electric Storage Systems (ESS) to automatically reconfigure themselves to counteract cyberattacks, both directly against the ESS control systems and indirectly through the electric grid. It is funded by DOE CESER's CEDS program and is led by [Daniel Arnold](https://eta.lbl.gov/people/daniel-arnold).","tags":["power grid","machine learning","AI"],"title":"Supervisory Parameter Adjustment for Distribution Energy Storage (SPADES)","type":"project"},{"authors":null,"categories":null,"content":"Overview LBNL held the second workshop for the SPADES project in Dec. 2022 where the project partners presented deep dives into work conducted in the second year of the project. Presentations from project partners are included below:\nParticipants Daniel Arnold (Principal Investigator), LBNL dbarnold@lbl.gov\nCiaran Roberts, LBNL cmroberts@lbl.gov\nSy-Toan Ngo, LBNL sytoanngo@lbl.gov\nDavid Pinney, NRECA, david.pinney@nreca.coop\nLisa Slaughter, NRECA, lisa.m.slaughter@gmail.com\nBruno Leao, Siemens CT, bruno.leao@siemens.com\nSindhu Suresh, Siemens CT, sindhu-suresh@siemens.com\nSiddharth Bhela, Siemens CT, siddharth.bhela@siemens.com\nDan Grinkevich, Siemens CT, daniel.grinkevich@siemens.com\nAnna Scaglione, ASU, as337@cornell.edu\nIgnacio Losada Carreno, Cornell Tech, il244@cornell.edu\nTong Wu, Cornell Tech, tw385@cornell.edu\nPresentations LBNL (Sy-Toan Ngo)\nNRECA (David Pinney)\nSiemens CT (Bruno Leao)\nCornell Tech (Ignacio Losada Carreno, Tong Wu)\n","date":1638921600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1638921600,"objectID":"436fac17c72619c840d2ad27eac9b18c","permalink":"https://secpriv.lbl.gov/talk/ceds_spades_y2_workshop/","publishdate":"2021-12-08T00:00:00Z","relpermalink":"/talk/ceds_spades_y2_workshop/","section":"talk","summary":"LBNL held the second workshop for the SPADES project in Dec. 2021 where the project partners presented deep dives into work conducted in the second year of the project.","tags":["power grid","machine learning"],"title":"Supervisory Parameter Adjustment for Distribution Energy Storage (SPADES) - Year 2 Workshop","type":"talk"},{"authors":null,"categories":null,"content":"Project Summary LBNL will lead a new workgroup for Artificial Intelligence (AI) for IBR/DER cybersecurity that will seek a draft standard report on AI performance requirements for high solar / IBR / DER penetration scenarios. This working group will specifically focus on advancing an understanding of performance requirements for IBR/DER for high solar generation scenarios. As IBR / DER grow from tens of thousands to millions of devices, increased automation is likely required but needs to be considered within conflicting recommendations for manual control. Automation has brought significant advantages in the power grid for ensuring stability, increasing efficiency, and even providing cybersecurity benefits. At the same time, automation significantly increases cybersecurity risks because automated systems can be remotely attackable, and have similar vulnerabilities to other types of computing systems.\nLBNL will also lead a new workgroup for privacy-preserving \u0026amp; cybersecure data and model sharing standards for solar / IBR / DER industry stakeholders. Use cases will focus on data and models for high solar / IBR / DER penetration scenarios. This will include threat-sharing program reformations, leveraging prior experience with CESER’s CEDS program.\nThis project was supported by the U.S. Department of Energy’s Solar Energy Technologies Office (SETO).\nPrincipal Investigators: Sean Peisert (PI; LBNL) Daniel Arnold (Co-PI; LBNL)\nPartners: Bob Currie (VP Product and Strategy, Kevala, Inc.) Aram Shumavon (Founder and CEO, Kevala, Inc.)\nPublications resulting from this project: Robert Currie, Sean Peisert, Anna Scaglione, Aram Shumavon, and Nikhil Ravi, “Data Privacy for the Grid: Toward a Data Privacy Standard for Inverter-Based and Distributed Energy Resources,” IEEE Power \u0026amp; Energy Magazine, 21(5), pp. 58-57, Sept.-Oct 2023.\nMore information is available on other Berkeley Lab research projects focusing on cybersecurity in general, as well as specifically on cybersecurity for energy delivery systems.\n","date":1633046400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1633046400,"objectID":"992d255862ddeddc5ecc092fd794af86","permalink":"https://secpriv.lbl.gov/project/seto-standards/","publishdate":"2021-10-01T00:00:00Z","relpermalink":"/project/seto-standards/","section":"project","summary":"This project aims to develop an understanding of security and performance requirements for the use of AI high solar / IBR / DER penetration scenarios, and also to develop an understanding of understanding power grid data confidentiality and privacy requirements.  It is funded by DOE's SETO office and is co-led by [Sean Peisert](https://https://www.cs.ucdavis.edu/~peisert/) and [Daniel Arnold](https://eta.lbl.gov/people/daniel-arnold).","tags":["differential privacy","power grid","machine learning","cyber-physical systems","data privacy","AI"],"title":"Securing Solar for the Grid (S2G)","type":"project"},{"authors":null,"categories":null,"content":"Project Summary In this project, LBNL will help inform DHS S\u0026amp;T regarding the state of the art in software assurance tools and capabilities.\nThis project is supported by the the U.S. Department of Homeland Security’s Science and Technology Directorate.\nPrincipal Investigator: Sean Peisert (PI; LBNL)\nPublications resulting from this project: Sean Peisert, “The Current State of Software Assurance Tools and Techniques,” LBNL Technical Report, October 24, 2022.\nMore information is available on other Berkeley Lab research cybersecurity projects.\n","date":1632182400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1632182400,"objectID":"eefc9ee9f7ea896e2a8c468eaf5aee2a","permalink":"https://secpriv.lbl.gov/project/dhs-software-assurance/","publishdate":"2021-09-21T00:00:00Z","relpermalink":"/project/dhs-software-assurance/","section":"project","summary":"In this project, LBNL will help inform DHS S\u0026T regarding the state of the art in software assurance tools and capabilities.  It is funded by DHS S\u0026T and is led by [Sean Peisert](https://https://www.cs.ucdavis.edu/~peisert/).","tags":["formal methods","software assurance"],"title":"AOSCSWAP: Study of Academic, Open Source, and COTS Software Assurance Products","type":"project"},{"authors":null,"categories":null,"content":"Overview LBNL held an end of project workshop for the CIGAR project on Mar. 17, 2021 where project participants, stakeholders, and advisors were convened to discuss outcomes of the CIGAR project. Due to the COVID-19 pandemic, the workshop was held virtually.\nWorkshop Agenda Workshop Agenda\nPresentations Introductory Remarks and Project Overview, Sean Peisert and Daniel Arnold (LBNL)\nReinforcement Learning and PyCIGAR Architecture, Sy-Toan Ngo and Daniel Arnold (LBNL)\nSimulation Experiments/Results, Ciaran Roberts (LBNL)\nIntegration of PyCIGAR into NRECA OMF and Demo, David Pinney (NRECA)\nASU Linearized Power Flow Model and SoDa, Ignacio Losada Carreno (ASU)\nSiemens Internal Tech Transfer Tool, Graph Convolutional Neural Networks and RL Extensions, Anton Kocheturov (Siemens)\nKey Findings and Future Research Directions, Daniel Arnold (LBNL)\n","date":1615939200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1615939200,"objectID":"8e780acc5e874b1741d90134247c2e0d","permalink":"https://secpriv.lbl.gov/talk/ceds_cigar_y3_workshop/","publishdate":"2021-03-17T00:00:00Z","relpermalink":"/talk/ceds_cigar_y3_workshop/","section":"talk","summary":"LBNL held an end of project workshop for the CIGAR project on Mar. 17, 2021 where project participants, stakeholders, and advisors were convened to discuss outcomes of the CIGAR project.","tags":["power grid","machine learning"],"title":"Cybersecurity via Inverter Grid Automatic Reconfiguration (CIGAR) - Year 3 (End of Project) Workshop","type":"talk"},{"authors":null,"categories":null,"content":"Background The proliferation of Distributed Energy Resources (DER) in electric distribution systems is challenging many existing conventional paradigms of how the grid is planned and operated. This includes strategies to ensure the safety of the electric grid from cyber attacks. Aggregations of DER constitute a mechanism through which hostile entities can remotely influence multiple systems and states in the grid in such a way that distribution system operators have virtually no warning of an attack and no clear strategy to mitigate the effects of intrusion. Furthermore, as infrastructure already exists to reconfigure DER smart inverter settings, attackers need not infiltrate individual systems to gain control over large quantities of DER. Instead, attackers need only gain access to one of a handful of systems designed to wirelessly update DER.\nObjectives Development of a state observer that detects the presence and severity of cyber attacks. Creation of a set of adaptive controllers that adjust settings of DER inverter controllers and distribution grid voltage regulation and protection systems to mitigate the cyber-physical attack in real time. Development of reinforcement learning-based agents that will explore defenses against different attack scenarios Project Description The LBNL-led “Cybersecurity via Inverter-Grid Automatic Reconfiguration (CIGAR)” project seeks to develop supervisory control algorithms to counteract cyber-physical attacks that have compromised multiple independent systems in the electric grid. This research will begin with an effort to analyze the stability of different types of feedback control systems (e.g, distributed energy resources, and voltage regulation and protection systems) in the electric grid to determine what parameters an attacker would change if DER and utility voltage regulation and protection systems were compromised. Then, the research team will develop adaptive control algorithms that adjust critical parameters in non-compromised systems to actively fight the cyber-physical attack. Finally, this project will utilize reinforcement learning techniques to simultaneously develop defense strategies in higher dimensions tailored to specific sections of the electric grid. Analysis of derived attack and defensive strategies will also highlight specific system vulnerabilities as well as determine recommended upgrades to enhance system cyber security.\nThis project was supported by the U.S. Department of Energy’s Cybersecurity for Energy Delivery Systems (CEDS) program.\nDOE/LBNL Press Release: “Berkeley Lab Aims to Strengthen the Cybersecurity of the Grid,” September 26, 2017.\nPrincipal Investigators: Sean Peisert (PI; LBNL)\nDaniel Arnold (Co-PI; LBNL)\nSenior Personnel: Reinhard Gentz (LBNL) Ciaran Roberts (LBNL) Sy-Toan Ngo (LBNL)\nDavid Pinney (Lead at NRECA) Anna Scaglione (Lead at ASU) Dmitriy Fradkin (Lead at Siemens) Sindhu Suresh (Siemens) Anton Kocheturov (Siemens)\nStudents: Ewa Garbiec (UC Dublin) Ignacio Losada Carreño (ASU) Alexandre Milesi (Univ. Tech. Compiègne) Shammya Saha (ASU)\nPartners: Arizona State University\nNational Rural Electric Cooperative Association (NRECA) Siemens\nProject Workshops: End of Project Workshop\nPress regarding this project: Solar power opens up new targets for cyber attackers (Archer News) — May 30, 2019\nCyberattacks threaten smart inverters, but scientists have solutions (Solar Power World) — April 30, 2019\nCyber Defense Tool Is an Early Warning System for Grid Attacks (IEEE Spectrum Energywise Blog) — March 27, 2018\nBerkeley Lab Aims to Strengthen the Cybersecurity of the Grid — September 27, 2017\nPresentations relating to this project: Sean Peisert, “Cybersecurity and Privacy research for Science and Energy,” Networking and Information Technology Research and Development (NITRD) Cyber Security and Information Assurance (CSIA) Interagency Working Group (IWG) Meeting, March 24, 2022.\nPublications resulting from this project: Sean Peisert, Daniel Arnold, Ciaran Roberts, Sy-Toan Ngo, Michael Sankur, Anna Scaglione, Ignacio Losada Carreno, Shammya Saha Anton Kocheturov, Dmitriy Fradkin, David Pinney, Ryan Mahoney, Lisa Slaughter, “Cybersecurity via Inverter Grid Automatic Reconfiguration (CIGAR) Year 3 Report,” March 31 2021.\nCiaran Roberts, Sy-Toan Ngo, Alexandre Milesi, Anna Scaglione, Sean Peisert, Daniel Arnold, “Deep Reinforcement Learning for Mitigating Cyber-Physical DER Voltage Unbalance Attacks,” Proceedings of the 2021 American Control Conference (ACC), May 26–28, 2021. [arXiv]\nShammya Shananda Saha, Daniel Arnold, Anna Scaglione, Eran Schweitzer, Ciaran Roberts, Sean Peisert, and Nathan G. Johnson. “Lyapunov Stability of Smart Inverters Using Linearized DistFlow Approximation,” IET Renewable Power Generation, accepted 28 September 2020. [arXiv]\nIgnacio Losada Carreño, Raksha Ramakrishna, Anna Scaglione, Daniel Arnold, Ciaran Roberts, Sy-Toan Ngo, Sean Peisert, and David Pinney, “SoDa: An Irradiance-Based Synthetic Solar Data …","date":1614556800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1614556800,"objectID":"9e09141f487908638a575e06fa07c280","permalink":"https://secpriv.lbl.gov/project/ceds-cigar/","publishdate":"2021-03-01T00:00:00Z","relpermalink":"/project/ceds-cigar/","section":"project","summary":"This project performed research to enable distribution grids to adapt to resist a cyber-attack by (1) developing adaptive control algorithms for DER, voltage regulation, and protection systems; (2) analyze new attack scenarios and develop associated defensive strategies. It was funded by DOE's CEDS program and was co-led by [Sean Peisert](https://www.cs.ucdavis.edu/~peisert/) and [Daniel Arnold](https://eta.lbl.gov/people/daniel-arnold).","tags":["power grid","machine learning","AI"],"title":"Cybersecurity via Inverter-Grid Automatic Reconfiguration (CIGAR)","type":"project"},{"authors":null,"categories":null,"content":"Overview LBNL held the first workshop for the SPADES project on Dec. 2, 2020 where the project participants convened to discuss progress made over the past year as well as plan for work to be conducted during Year 2. Due to the COVID-19 pandemic, the workshop was held virtually.\nParticipants Daniel Arnold (Principal Investigator), LBNL dbarnold@lbl.gov\nSean Peisert, LBNL sppeisert@lbl.gov\nCiaran Roberts, LBNL cmroberts@lbl.gov\nSy-Toan Ngo, LBNL sytoanngo@lbl.gov\nMichael Sankur, LBNL msankur@lbl.gov\nDavid Pinney, NRECA, david.pinney@nreca.coop\nLisa Slaughter, NRECA, lisa.m.slaughter@gmail.com\nBruno Leao, Siemens CT, bruno.leao@siemens.com\nSindhu Suresh, Siemens CT, sindhu-suresh@siemens.com\nSiddharth Bhela, Siemens CT, siddharth.bhela@siemens.com\nDan Grinkevich, Siemens CT, daniel.grinkevich@siemens.com\nAnna Scaglione, ASU, ascaglio@asu.edu\nIgnacio Losada Carreno, ASU, ilosadac@asu.edu\nWorkshop Agenda Workshop Agenda\nPresentations Introductory Remarks, Daniel Arnold (LBNL)\nStorage and Control Module Architecture in PyCIGAR, Michael Sankur and Sy-Toan Ngo (LBNL)\nGrid Forming/following \u0026amp; Active Load Stability Simulation, Ciaran Roberts (LBNL)\nOpen Modeling Framework (OMF) Updates. Use Case Analysis, Network Reduction, David Pinney \u0026amp; Lisa Slaughter (NRECA)\nRed Team Planning/Methodology, Bruno Leao (Siemens)\nLog(V) 3LPF: A linearized solution to train reinforcement learning algorithms for unbalanced distribution systems, Ignacio Losada Carreno (ASU)\nConcluding Remarks, Daniel Arnold (LBNL)\nWorkshop Q\u0026amp;A\n","date":1606867200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1606867200,"objectID":"1850a233e698cfcbfb064d0c60fa1e74","permalink":"https://secpriv.lbl.gov/talk/ceds_spades_y1_workshop/","publishdate":"2020-12-02T00:00:00Z","relpermalink":"/talk/ceds_spades_y1_workshop/","section":"talk","summary":"LBNL held the first workshop for the SPADES project on Dec. 2, 2020 where the project participants convened to discuss progress made over the past year as well as plan for work to be conducted during Year 2.  Due to the COVID-19 pandemic, the workshop was held virtually.","tags":["power grid","machine learning"],"title":"Supervisory Parameter Adjustment for Distribution Energy Storage (SPADES) - Year 1 Workshop","type":"talk"},{"authors":null,"categories":null,"content":"Background This project aims to explore applications of a Byzantine Fault Tolerant (BFT) architecture in combination with Machine Learning (ML/AI) methods to ensure that the bulk power system, including protective relays in the transmission grid, and associated substation and control center systems, can perform intrusion tolerant operations.\nThis project is funded by several offices within the U.S. Department of Energy under the auspices of the DOE Grid Modernization Initiative. The project is led by PNNL.\nPrincipal Investigator at LBNL: Sean Peisert (PI at LBNL)\nPublications resulting from this project: James R. Clavin, Yue Huang, Xin Wang, Pradeep M. Prakash, Sisi Duan, Jianwu Wang, and Sean Peisert, “A Framework for Evaluating BFT,” Proceedings of the IEEE International Conference on Parallel and Distributed Systems (ICPADS), December 14–16, 2021.\nThis project is supported by the U.S. Department of Energy’s Grid Modernization Initiative.\nMore information is available on other Berkeley Lab research projects focusing on cybersecurity in general, as well as specifically on cybersecurity for energy delivery systems.\n","date":1584662400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1584662400,"objectID":"9f1b06e911349b9e2fe5eded508cb13a","permalink":"https://secpriv.lbl.gov/project/gmlc-byzantine/","publishdate":"2020-03-20T00:00:00Z","relpermalink":"/project/gmlc-byzantine/","section":"project","summary":"This project aims to explore applications of a Byzantine Fault Tolerant (BFT) architecture in combination with ML/AI methods to ensure that the bulk power system, including protective relays in the transmission grid, and associated substation and control center systems, can perform intrusion tolerant operations. It is funded by the [DOE Grid Modernization Initiative](https://www.energy.gov/2019-grid-modernization-lab-call-awards).  The LBNL portion of this effort is led by [Sean Peisert](https://www.cs.ucdavis.edu/~peisert/).","tags":["power grid","fault tolerance","secure systems"],"title":"Byzantine Security — Multi-layered Intrusion Tolerant Byzantine Architecture for Bulk Power System Protective Relays","type":"project"},{"authors":[],"categories":[],"content":"Create slides in Markdown with Wowchemy Wowchemy | Documentation\nFeatures Efficiently write slides in Markdown 3-in-1: Create, Present, and Publish your slides Supports speaker notes Mobile friendly slides Controls Next: Right Arrow or Space Previous: Left Arrow Start: Home Finish: End Overview: Esc Speaker notes: S Fullscreen: F Zoom: Alt + Click PDF Export: E Code Highlighting Inline code: variable\nCode block:\nporridge = \u0026#34;blueberry\u0026#34; if porridge == \u0026#34;blueberry\u0026#34;: print(\u0026#34;Eating...\u0026#34;) Math In-line math: $x + y = z$\nBlock math:\n$$ f\\left( x \\right) = ;\\frac{{2\\left( {x + 4} \\right)\\left( {x - 4} \\right)}}{{\\left( {x + 4} \\right)\\left( {x + 1} \\right)}} $$\nFragments Make content appear incrementally\n{{% fragment %}} One {{% /fragment %}} {{% fragment %}} **Two** {{% /fragment %}} {{% fragment %}} Three {{% /fragment %}} Press Space to play!\nOne Two Three A fragment can accept two optional parameters:\nclass: use a custom style (requires definition in custom CSS) weight: sets the order in which a fragment appears Speaker Notes Add speaker notes to your presentation\n{{% speaker_note %}} - Only the speaker can read these notes - Press `S` key to view {{% /speaker_note %}} Press the S key to view the speaker notes!\nOnly the speaker can read these notes Press S key to view Themes black: Black background, white text, blue links (default) white: White background, black text, blue links league: Gray background, white text, blue links beige: Beige background, dark text, brown links sky: Blue background, thin dark text, blue links night: Black background, thick white text, orange links serif: Cappuccino background, gray text, brown links simple: White background, black text, blue links solarized: Cream-colored background, dark green text, blue links Custom Slide Customize the slide style and background\n{{\u0026lt; slide background-image=\u0026#34;/media/boards.jpg\u0026#34; \u0026gt;}} {{\u0026lt; slide background-color=\u0026#34;#0000FF\u0026#34; \u0026gt;}} {{\u0026lt; slide class=\u0026#34;my-style\u0026#34; \u0026gt;}} Custom CSS Example Let’s make headers navy colored.\nCreate assets/css/reveal_custom.css with:\n.reveal section h1, .reveal section h2, .reveal section h3 { color: navy; } Questions? Ask\nDocumentation\n","date":1549324800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1549324800,"objectID":"0e6de1a61aa83269ff13324f3167c1a9","permalink":"https://secpriv.lbl.gov/slides/example/","publishdate":"2019-02-05T00:00:00Z","relpermalink":"/slides/example/","section":"slides","summary":"An introduction to using Wowchemy's Slides feature.","tags":[],"title":"Slides","type":"slides"},{"authors":null,"categories":null,"content":"","date":1546300800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1546300800,"objectID":"c3854cefe5d82ee4c024bd68272381a7","permalink":"https://secpriv.lbl.gov/research/ceds/","publishdate":"2019-01-01T00:00:00Z","relpermalink":"/research/ceds/","section":"research","summary":"","tags":null,"title":"Cybersecurity for Energy Delivery Systems","type":"widget_page"},{"authors":null,"categories":null,"content":"","date":1546300800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1546300800,"objectID":"347ee0e2e15139134af745f0a4c3d501","permalink":"https://secpriv.lbl.gov/research/research-cyberinfrastructure/","publishdate":"2019-01-01T00:00:00Z","relpermalink":"/research/research-cyberinfrastructure/","section":"research","summary":"","tags":null,"title":"Cybersecurity for Research Cyberinfrastructure and High-Performance Computing","type":"widget_page"},{"authors":null,"categories":null,"content":"","date":1546300800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1546300800,"objectID":"6087c0ef875554f4409ac52928d79279","permalink":"https://secpriv.lbl.gov/projects/","publishdate":"2019-01-01T00:00:00Z","relpermalink":"/projects/","section":"","summary":"","tags":null,"title":"Research","type":"widget_page"},{"authors":null,"categories":null,"content":"Project Summary In this project, LBNL Computing Sciences Research supported the automation of synthetic biology research pipelines supporting the design-build-test-learn (DBTL) cycle, including ingest and analysis of liquid chromatography mass spectrometry and feedstocks-to-fuels pipelines.\nThe work was led at JBEI by Héctor García Martín and supported by LBNL Computing Sciences Research by Reinhard Gentz and Sean Peisert.\nThis project was supported by the DOE Joint BioEnergy Institute (JBEI) and the DOE Agile BioFoundry (ABF).\nPublications resulting from this project: Luca Pion-Tonachini, Kristofer Bouchard, Hector Garcia Martin, Sean Peisert, W. Bradley Holtz, Anil Aswani, Dipankar Dwivedi, Haruko Wainwright, Ghanshyam Pilania, Benjamin Nachman, Babetta L. Marrone, Nicola Falco, Prabhat, Daniel Arnold, Alejandro Wolf-Yadlin, Sarah Powers, Sharlee Climer, Quinn Jackson, Ty Carlson, Michael Sohn, Petrus Zwart, Neeraj Kumar, Amy Justice, Claire Tomlin, Daniel Jacobson, Gos Micklem, Georgios V. Gkoutos, Peter J. Bickel, Jean-Baptiste Cazier, Juliane Müller, Bobbie-Jo Webb-Robertson, Rick Stevens, Mark Anderson, Ken Kreutz-Delgado, Michael W. Mahoney, James B. Brown, “Learning from Learning Machines: a New Generation of AI Technology to Meet the Needs of Science,” arXiv preprint arXiv:2111.13786, 27 Nov 2021.\nChris Lawson, Jose Manuel Martí, Tijana Radivojevic, Sai Vamshi R. Jonnalagadda, Reinhard Gentz, Nathan J. Hillson, Sean Peisert, Joonhoon Kim, Blake A. Simons, Christopher J. Petzold, Steven W. Singer, Aindrila Mukhopadhyay, Deepti Tanjore, Josh Dunn, and Héctor García Martín, “Machine Learning for Metabolic Engineering: A Review,” Metabolic Engineering, available online 19 November 2020. [DOI]\nReinhard Gentz, Héctor García Martin, Edward Baidoo, and Sean Peisert, “Workflow Automation in Liquid Chromatography Mass Spectrometry,” Proceedings of the 15th IEEE International Conference on e-Science (eScience), San Diego, CA, September 2019. [DOI]\n","date":1522540800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1522540800,"objectID":"57b87928613685d50f9be679068288c4","permalink":"https://secpriv.lbl.gov/project/jbei-automation/","publishdate":"2018-04-01T00:00:00Z","relpermalink":"/project/jbei-automation/","section":"project","summary":"In this project, LBNL Computing Sciences Research supported the automation of synthetic biology research pipelines supporting the design-build-test-learn (DBTL) cycle, including ingest and analysis of liquid chromatography mass spectrometry and feedstocks-to-fuels pipelines.","tags":["synthetic biology","machine learning"],"title":"Synthetic Biology Automation","type":"project"},{"authors":null,"categories":null,"content":"A Science DMZ is a portion of the network, built at or near the local network perimeter of an individual research institution, that is designed such that the equipment, configuration, and security poli- cies are optimized for high-performance workflows and large datasets.\nDeveloped by ESnet engineers, the Science DMZ model addresses common network performance problems encountered at research institutions by creating an environment that is tailored to the needs of high performance science applications, including high-volume bulk data transfer, remote experiment control, and data visualization.\nThe Science DMZ architecture also maintains the security of the data through a number of distinct techniques, but does not employ commercial firewalls due to their negative impact on performance. As a result, the Science DMZ model is not currently employed in environments subject to the HIPAA Security Rule and HITECH requirements, due to the presumed technical controls based on de facto use of stateful and deep packet–inspecting commercial firewalls.\nWe have taken a central of tenet of the Science DMZ, and reengineered it for “restricted data” as a Medical Science DMZ. We have defined a Medical Science DMZ as a method or approach that allows data flows at scale while simultaneously addressing the HIPAA Security Rule and related regulations governing biomedical data and appropriately managing risk. We emphasize use cases that involve scientists transferring and processing medical research data that have very different requirements than those of medical centers communicating with suppliers, service providers, and employees. Our network design pattern addresses Big Data and can be implemented using a combination of physical, administrative, and technical safeguards.\nCite the Medical Science DMZ Two versions of our Medical Science DMZ paper have been published in Journal of the American Medical Informatics Association (JAMIA) — a “brief communication” in JAMIA 23(6), November 2016, and a “full” version in JAMIA 25(3), March 2018. Citation information for the “full” version of our Medical Science DMZ paper – the canonical citation – is as follows:\nSean Peisert, Eli Dart, William K. Barnett, James Cuff, Robert L. Grossman, Edward Balas, Ari Berman, Anurag Shankar, and Brian Tierney, “The Medical Science DMZ: A Network Design Pattern for Data-Intensive Medical Science,” Journal of the American Medical Informatics Association (JAMIA), 26(3):267–274, March 1, 2018. DOI:10.1093/jamia/ocx104\n@article{MedicalScienceDMZ-2018-JAMIA-Full, Author = {Sean Peisert and Eli Dart and Barnett, William K. and James Cuff and Grossman, Robert L. and Edward Balas and Ari Berman and Anurag Shankar and Brian Tierney}, Journal = {Journal of the American Medical Informatics Association (JAMIA)}, Month = {March 1}, Number = {3}, Pages = {267--274}, Title = {{The Medical Science DMZ: A Network Design Pattern for Data-Intensive Medical Science}}, Volume = {26}, Year = {2018}} ","date":1519862400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1519862400,"objectID":"035da675de71852b82ce889bbe5399cf","permalink":"https://secpriv.lbl.gov/project/medical-science-dmz/","publishdate":"2018-03-01T00:00:00Z","relpermalink":"/project/medical-science-dmz/","section":"project","summary":"We have defined a Medical Science DMZ as a method that allows data flows at scale while simultaneously addressing the HIPAA Security Rule and related regulations governing biomedical data and appropriately managing risk.","tags":["network","medical","research cyberinfrastructure"],"title":"Medical Science DMZ","type":"project"},{"authors":null,"categories":null,"content":"Background This project will bring together a multi-disciplinary UC-Lab team of cybersecurity and electricity infrastructure experts to investigate, through both cyber and physical modeling and physics-aware cybersecurity analysis, the impact and significance of cyberattacks on electricity distribution infrastructure. We will develop new strategies for mitigating vulnerabilities, detecting intrusion, and protecting against detrimental system wide impact. This project will build a new knowledge base to address both theoretical and practical challenges in electricity distribution cybersecurity.\nPrincipal Investigators: Hamed Mohsenian-Rad (PI; UC Riverside)\nMahnoosh Alizadeh (Co-PI; PI at UC Santa Barbara)\nRajit Gadh (Co-PI; PI at UC Los Angeles)\nSean Peisert (Co-PI; PI at LBNL)\nKeyue Smedley (Co-PI; PI at UC Irvine)\nEmma Stewart (Co-PI; PI at LLNL)\nRead more at the UC-National Lab Center for Power Distribution Cyber Security web site.\nPress regarding this project: Lab Researchers Awarded Funds for Climate Science, Cybersecurity Research — March 15, 2018\nPublications resulting from this project: none yet\nThis project is funded by the UC-Lab Fees Research Program.\nMore information is available on other Berkeley Lab research projects focusing on cybersecurity in general, as well as specifically on cybersecurity for energy delivery systems.\n","date":1519862400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1519862400,"objectID":"0d8ba1dcb9fea3b02e8930a4ade65f8a","permalink":"https://secpriv.lbl.gov/project/uc-lfrp-cybersecurity-grid/","publishdate":"2018-03-01T00:00:00Z","relpermalink":"/project/uc-lfrp-cybersecurity-grid/","section":"project","summary":"This project will bring together a multi-disciplinary UC-Lab team of cybersecurity and electricity infrastructure experts to investigate, through both cyber and physical modeling and physics-aware cybersecurity analysis, the impact and significance of cyberattacks on electricity distribution infrastructure.  It is funded by the [UC-Lab Fees Research Program](https://www.ucop.edu/research-initiatives/programs/lab-fees/).  The overall project is led by [Hamed Mohsenian-Rad](http://intra.ece.ucr.edu/~hamed/); the LBNL portion is led by [Sean Peisert](https://www.cs.ucdavis.edu/~peisert/).","tags":["power grid"],"title":"UC-Lab Center for Electricity Distribution Cybersecurity","type":"project"},{"authors":null,"categories":null,"content":"Background The goal of this project is to create advanced, distributed data analytics capability to provide visibility and controllability to distribution grid operators.\nPrincipal Investigators: Emma Stewart (Lead; LLNL) Sean Peisert (PI at LBNL)\nSenior Personnel at LBNL: Daniel Arnold (LBNL) Reinhard Gentz (LBNL)\nPublications resulting from this project: none yet\nThis project is supported by the U.S. Department of Energy’s Grid Modernization Initiative.\nMore information is available on other Berkeley Lab research projects focusing on cybersecurity in general, as well as specifically on cybersecurity for energy delivery systems.\n","date":1512086400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1512086400,"objectID":"7eeb201790f652e0b533fb90c2109530","permalink":"https://secpriv.lbl.gov/project/gmlc-ml/","publishdate":"2017-12-01T00:00:00Z","relpermalink":"/project/gmlc-ml/","section":"project","summary":"The goal of this project is to create advanced, distributed data analytics capability  to provide visibility and controllability to distribution grid operators.   It is funded by the [DOE Grid Modernization Initiative](http://energy.gov/doe-grid-modernization-laboratory-consortium-gmlc-awards).  The LBNL portion of this effort is led by [Sean Peisert](https://www.cs.ucdavis.edu/~peisert/).","tags":["power grid","machine learning"],"title":"Integrated Multi Scale Machine Learning for the Power Grid","type":"project"},{"authors":null,"categories":null,"content":"Principal Investigator: Sean Peisert (PI)\nFaculty Scientists Chen-Nee Chuah (LBNL Faculty Scientist / UC Davis)\nDipak Ghosal (LBNL Faculty Scientist / UC Davis)\nGraduate Students Ross Gegan (LBNL GSR / UC Davis) Chang Liu (LBNL GSR / UC Davis)\nA large scale distributed denial of service (DDoS) attack has the potential to not only impact the target site, but impact performance along the entire network path. Today, DDoS mitigation across DOE Sites is largely handled at the site borders using a combination of heuristic and filtering techniques, manual changes, and commercial services that can “absorb” attacks against specific sites. With the scale of DDoS increasing dramatically with little indication of slowing down, the task of DDoS detection and mitigation across ESnet’s extensive wide area network (WAN) becomes a higher priority, with increased complexity of detection and execution.\nAs ESnet pushes the boundary of modern network technology, we must also develop the security tools and strategies that are most effective for monitoring the WAN to prevent DDoS attacks for a next-generation network architecture. Specifically, this includes research issues of disambiguating very large science flows from malicious attacks, and developing mechanisms and tools for DDoS detection that can integrate this knowledge.\nWhile commercial DDoS tools and services exist to mitigate DDoS attacks to certain extents, and within typical enterprise and service provider networks, the effectiveness of those tools is extremely limited. ESnet transits all types of traffic, from ordinary websites and email to massive scientific data sets, thus DDoS protection targeting multiple facets of the network stack is required. Since commercial tools are typically and almost exclusively focused on commercial enterprise traffic, research on both detecting and mitigating DDoS is still deemed as essential as demonstrated by the interest of numerous other organizations funding research efforts in this space, including NSF, DARPA’s “Extreme DDoS Defense (XD3),” and others. However, most research [BBHkc09,MR04] and commercial service products target protection for individual sites, not entire WANs, let alone WANs carrying large volumes of scientific traffic. Since it is ESnet’s responsibility to protect availability for the entire ESnet WAN, this raises both the importance and value of blocking attacks earlier, before they reach individual DOE Sites.\nA focus in the DOE space is ESnet’s ability to inform DDoS identification techniques with additional information sources to reduce the likelihood of false positives. WAN level detection algorithms can be bolstered by information provided by DDoS detection information provided by the Sites to confirm positive DDoS detection. On the opposite front, pre-defined network circuit reservations (as are available through OSCARS) can help identify a large flow as being scientific data rather than a malicious attack. The DOE is in a unique position to perform the research necessary to take a very different approach from both commercial services as well as the research approaches pursued by funding agencies other than DOE.\nThis project is supported by the US Department of Energy’s iJC3 Cyber research program\nDDoS Detection Source Code at GitHub\nPublications resulting from this project: Chang Liu, “Network Monitoring and Security Enhancement in Software-Defined Networking,” Ph.D Dissertation, University of California, Davis, Department of Electrical and Computer Engineering, May 2019. (Advisor: Prof. Chen-Nee Chuah.)\nSoftware resulting from this project: LBNL DDoS Detection on Science Networks\nMore information is available on other Berkeley Lab research projects focusing on cybersecurity in general, as well as specifically on cybersecurity for research cyberinfrastructure and high-performance computing.\n","date":1488326400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1488326400,"objectID":"3ea9697d7992f853374954a715a7a49f","permalink":"https://secpriv.lbl.gov/project/ijc3-cyber-rd/","publishdate":"2017-03-01T00:00:00Z","relpermalink":"/project/ijc3-cyber-rd/","section":"project","summary":"This project develops techniques for detecting DDoS attacks and disambiguating them from large-scale science flows.  It is funded by the DOE iJC3 Cyber research program and is led by [Sean Peisert](https://www.cs.ucdavis.edu/~peisert/).","tags":["network","machine learning","research cyberinfrastructure"],"title":"Detecting Distributed Denial of Service Attacks on Wide-Area Networks","type":"project"},{"authors":null,"categories":null,"content":"Principal Investigator: Sean Peisert (PI)\nSenior Personnel: Venkatesh Akella (UC Davis / LBNL Faculty Scientist)\nJason Lowe-Power (UC Davis / LBNL Faculty Scientist)\nLBNL-Affiliated Graduate Students: Ayaz Akram (UC Davis / LBNL)\nProject Alumni: Bogdan Copos (LBNL/UC Davis; Ph.D. 2017) → SRI International → Google\nProf. Hein Meling (LBNL/University of Stavanger)\nAmir Teshome Wonjiga (LBNL/INRIA Rennes; Ph.D. 2019)\nReinhard Gentz (LBNL)\nAnna Giannakou (LBNL)\nScientific data today is at risk due to how it is collected, stored, and analyzed in highly disparate computing systems. How can we make claims about the integrity of data as it traverses open, international networks and via instruments and systems with widely varying reliability and provenance? Numerous causes for integrity loss are possible, including bugs in existing computational pipelines, network events, user error, unintentional system effects or even intentional attack by outsiders (e.g., scientific competitors), insiders (e.g., disgruntled employees), or in the hardware/software supply chain, without any trace of the modification. Given these gaps and shortcomings in existing HPC solutions, how can we make claims about the integrity of the scientific data as it traverses those systems and networks?\nWe believe that in order to solve the problems described above that future HPC hardware and software solutions should be co-designed together with security and scientific computing integrity concepts designed and built into as much of the stack from the outset as possible. Given the risk of data loss due to software and hardware, this should take into account hardware elements, operating systems, compilers, application software, and the networking stack, all the way down to the way in which software developers write software and users interact with systems in a way that can affect scientific computing integrity. However, prior to laying out the research roadmap to design and construct such an architecture, we believe that several important aspects first need to be understood more clearly.\nThis project takes a broad look at several aspects of security and scientific integrity issues in HPC systems. Using several case studies as exemplars, and working closely with both domain scientists as well as facility staff, we propose to test and validate several initial concepts in existing scientific computing workflows at NERSC DOE HPC facility, and analyze those models better characterize integrity-related computational behavior.\nEarly work on this project focused on a range of activities, including identifying misuse of computing systens, leveraging blockchains for scientific computing. More recent work has focused on developing trustworthy scientific computing architectures.\nFor more on the current work, see Data Enclaves for Scientific Computing.\nThis project is supported by the US Department of Energy’s Office of Science’s Advanced Scientific Computing Research (ASCR) program.\nPress regarding this project: Berkeley Lab Cybersecurity Specialist Highlights Data Sharing Benefits, Challenges at NAS Meeting — Dec. 4, 2018\nCRD’s Peisert to Discuss Data Sharing at National Academies’ COSEMPUP Meeting — Nov. 5, 2018\nLab Experts Help Coordinate ISC18, World’s First, Largest Computing Conference - June 21, 2018\nInto the Medical Science DMZ (Science Node) March 23, 2018\nBerkeley Lab Researchers Contribute to Making Blockchains Even More Robust — January 30, 2018\nESnet’s Science DMZ Design Could Help Transfer, Protect Medical Research Data (Science Node) — October 16, 2017\nBerkeley Lab’s cybersecurity expert Sean Peisert discusses challenges \u0026amp; opportunities of securing HPC — Aug. 24, 2017\nHPC security article in Communications of the ACM\nVideo accompanying HPC security article on Vimeo\nPublications resulting from this project: Ayaz Akram, Venkatesh Akella, Sean Peisert, and Jason Lowe-Power, “Enabling Design Space Exploration for RISC-V Secure Compute Environments,” Proceedings of the Fifth Workshop on Computer Architecture Research with RISC-V (CARRV), (co-located with ISCA 2021) June 17, 2021\nSean Peisert, “Trustworthy Scientific Computing,” Communications of the ACM (CACM), 64(5), pp. 18–21, May 2021.\nAyaz Akram, Anna Giannakou, Venkatesh Akella, Jason Lowe-Power, and Sean Peisert, “Performance Analysis of Scientific Computing Workloads on General Purpose TEEs,” Proceedings of the 35th IEEE International Parallel \u0026amp; Distributed Processing Sysmposium (IPDPS), May 17–21, 2021.\nAyaz Akram, “Trusted Execution for High-Performance Computing,” Proceedings of the 15th EuroSys Doctoral Workshop (EuroDW), 2021. video\nAyaz Akram, “Architectures for Secure High-Performance Computing,” Proceedings of the Young Architect Workshop (YArch) held in conjunction with the International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), April 2021. video\nAyaz Akram, Anna Giannakou, Venkatesh Akella, Jason Lowe-Power, and Sean Peisert, …","date":1473897600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1473897600,"objectID":"4f12508634d6b755a4a3a74ba7c26020","permalink":"https://secpriv.lbl.gov/project/ascr-hpc-cybersecurity-codesign/","publishdate":"2016-09-15T00:00:00Z","relpermalink":"/project/ascr-hpc-cybersecurity-codesign/","section":"project","summary":"This project takes a broad look at several aspects of security and scientific integrity issues in HPC systems. It is funded by DOE ASCR and is led by [Sean Peisert](https://www.cs.ucdavis.edu/~peisert/).","tags":["HPC","network","machine learning","research cyberinfrastructure","secure systems","confidential computing"],"title":"Toward a Hardware/Software Co-Design Framework for Ensuring the Integrity of Exascale Scientific Data","type":"project"},{"authors":null,"categories":null,"content":"Background The goal of this project is to develop technologies and methodologies to protect the nation’s power grid from advanced cyber and all-hazard threats. This will be done through the collection of disparate data and the use of advanced analytics to detect threats and response to them.\nPrincipal Investigators: Jamie Van Randwyk (Lead; LLNL) Sean Peisert (Co-PI; Lead at LBNL)\nSenior Personnel at LBNL Daniel Arnold (LBNL) Joshua Boverhof (LBNL) Reinhard Gentz (LBNL)\nAdditional National Laboratory Partners: PNNL SNL\nPartners: Electric Power Board (EPB) National Rural Electric Cooperative Association (NRECA)\nPress regarding this project: Cyberattacks threaten smart inverters, but scientists have solutions (Solar Power World) — April 30, 2019\nBerkeley Lab Aims to Strengthen the Cybersecurity of the Grid — September 27, 2017\nPublications resulting from this project: none yet\nThis project is supported by the U.S. Department of Energy’s Cybersecurity for Energy Delivery Systems (CEDS) program in support of the Grid Modernization Initiative.\nMore information is available on other Berkeley Lab research projects focusing on cybersecurity in general, as well as specifically on cybersecurity for energy delivery systems.\n","date":1464739200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1464739200,"objectID":"e98504c15ece70b4a21a1e5e0b9977cf","permalink":"https://secpriv.lbl.gov/project/ceds-threat-detection/","publishdate":"2016-06-01T00:00:00Z","relpermalink":"/project/ceds-threat-detection/","section":"project","summary":"The goal of this project is to develop technologies and methodologies to protect the nation's power grid from advanced cyber and all-hazard threats. This will be done through the collection of disparate data and the use of advanced analytics to detect threats and response to them.  It is funded by DOE OE's CEDS program via the [Grid Modernization Initiative](http://energy.gov/doe-grid-modernization-laboratory-consortium-gmlc-awards) and is co-led by [Sean Peisert](https://www.cs.ucdavis.edu/~peisert/).","tags":["power grid","machine learning"],"title":"Power Grid Threat Detection and Response with Data Analytics","type":"project"},{"authors":null,"categories":null,"content":"Principal Investigator: Sean Peisert (PI)\nScientists/Postdocs Dipankar Dwivedi\nReinhard Gentz\nMelissa Stockman\nGraduate Students Bogdan Copos (LBNL/UC Davis; Ph.D. 2017) → SRI International → Google\nThis project involves using power data for monitoring use of computing systems, including supercomputers and large computing centers. By using power data, as opposed to data provided by the computing environment itself, the technology collects the data non-invasively. More information is available at the LBNL Innovation and Partnerships Office.\nPublications resulting from this project: Bogdan Copos and Sean Peisert, “Catch Me If You Can: Using Power Analysis to Identify HPC Activity,” arXiv preprint arXiv:2005.03135, 2020.\nMelissa Stockman, Dipankar Dwivedi, Reinhard Gentz, Sean Peisert, “Detecting Programmable Logic Controller Code Using Machine Learning” International Journal of Critical Infrastructure Protection, accepted July 3, 2019. [DOI]\nBogdan Copos, Sean Peisert (advisor), Modeling Systems Using Side Channel Information. PhD dissertation, University of California, Davis, 2017.\nMore information is available on other LBNL research projects focusing on cybersecurity in general, as well as specifically on HPC Security projects.\nSoftware resulting from this project: Identifying Computational Operations Based on Power Measurements\nMore information is available on other Berkeley Lab research projects focusing on cybersecurity in general, as well as specifically on cybersecurity for research cyberinfrastructure and high-performance computing.\n","date":1452643200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1452643200,"objectID":"214a310088184c15c5bf546c78973a67","permalink":"https://secpriv.lbl.gov/project/inferring-computing/","publishdate":"2016-01-13T00:00:00Z","relpermalink":"/project/inferring-computing/","section":"project","summary":"This project uses power data to monitor the use of computing systems, including supercomputers and large computing centers.   It is led by [Sean Peisert](https://www.cs.ucdavis.edu/~peisert/).","tags":["HPC","cyber-physical systems","machine learning","research cyberinfrastructure"],"title":"Inferring Computing Activity Using Physical Sensors","type":"project"},{"authors":null,"categories":null,"content":"Current key management architectures are not designed for machine-to-machine communication, are designed around an “always online” mentality, and are often burdensome to manage (key distribution, revocation lists, governance, etc.). This project is designing and developing a key management system to meet the unique requirements of electrical distribution systems (EDSs). Namely it is disruption tolerant, scales well, is centrally managed, has policy enforcement and auditing, automates key management services for devices, etc…\nThis project is supported by the U.S. Department of Energy’s Cybersecurity for Energy Delivery Systems (CEDS) program.\nPrincipal Investigators: Thomas Edgar (PI; PNNL)\nSean Peisert (Co-PI; Lead at LBNL)\nSenior Personnel at LBNL Joshua Boverhof (LBNL) Reinhard Gentz (LBNL)\nProject Alumni: Chuck McParland (LBNL → RTISYS / LBNL Affiliate)\nPartners: Corelight (née Broala)\nPublications resulting from this project: Thomas W. Edgar, Aditya Ashok, Garret E. Seppala, K.M. Arthur-Durrett, M. Engels, Reinhard Gentz, and Sean Peisert, “An Automated Disruption-Tolerant Key Management Framework for Critical Systems,” Journal of Information Warfare, accepted 8 October, 2019.\nSoftware resulting from this project: LBNL Disruption Tolerant Key Management Monitoring for Stream-Processing Architecture for Real-time Cyber-physical Security (DTKM-SPARCS)\nMore information is available on other Berkeley Lab research projects focusing on cybersecurity in general, as well as specifically on cybersecurity for energy delivery systems.\n","date":1443657600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1443657600,"objectID":"daebde1efda9ba363845978d7f00f4ed","permalink":"https://secpriv.lbl.gov/project/ceds-dtkm/","publishdate":"2015-10-01T00:00:00Z","relpermalink":"/project/ceds-dtkm/","section":"project","summary":"This project is designing and developing a key management system to meet the unique requirements of electrical power distribution systems.  It is funded by DOE OE's CEDS program and is led by [Sean Peisert](https://www.cs.ucdavis.edu/~peisert/).","tags":["power grid","cyber-physical systems","network","secure systems","cryptography"],"title":"An Automated, Disruption Tolerant Key Management System for the Power Grid","type":"project"},{"authors":null,"categories":null,"content":"This project is developing a system-based workflow to securely acquire wireless data from mechanical ventilators in critical care environments, and leverage scalable web-based analytic platforms to advance data analytics and visualization of issues surrounding patients with respiratory failure.\nResearchers involved at UC Davis:\nProf. Jason Adams, M.D., M.S. (UC Davis) Prof. Nick Anderson, Ph.D. (PI; UC Davis) Prof. Jean-Pierre Delplanque, Ph.D. (UC Davis) Dr. Brooks Kuhn, M.D. (UC Davis) Prof. Sean Peisert, Ph.D. (UC Davis and LBNL) Sponsor: CITRIS and UC Davis Health System\nMore information is available at the UC Davis ventilator project web site.\nPublications resulting from this project: Jason Adams, Monica Lieng, Brooks Kuhn, Edward Guo, Edik Simonian, Sean Peisert, JP Delplanque, and Nick Anderson, “Automated Mechanical Ventilator Waveform Analysis of Patient-Ventilator Asynchrony,” CHEST Journal, 148(4), October 2015. [bib | DOI | CDL]\nMore information is available on other Berkeley Lab research projects focusing on cybersecurity in general, as well as specifically on cybersecurity for research cyberinfrastructure and high-performance computing.\n","date":1443657600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1443657600,"objectID":"fc2680b3d2b67dc58d6c175baf384a64","permalink":"https://secpriv.lbl.gov/project/ventilator-data/","publishdate":"2015-10-01T00:00:00Z","relpermalink":"/project/ventilator-data/","section":"project","summary":"This project is developing a system-based workflow to securely acquire wireless data from mechanical ventilators in critical care environments, and leverage scalable web-based analytic platforms to advance data analytics and visualization of issues surrounding patients with respiratory failure.","tags":["machine learning","medical"],"title":"Bedside to the Cloud and Back","type":"project"},{"authors":null,"categories":null,"content":"LBNL’s component of this project focused on mapping and analyzing the qualities of resilient networks by investigating components of redundancy, diversity, quality of service, etc… The project’s goal is to be able to quantify and compare the resilience of networks in a scientifically meaningful way.\nThis project was led at LBNL by Sean Peisert.\nMore information is available at the UC Davis resilience project web site.\nMore information is available on other Berkeley Lab research projects focusing on cybersecurity in general, as well as specifically on cybersecurity for research cyberinfrastructure and high-performance computing.\n","date":1443052800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1443052800,"objectID":"99e28f751fd33dfb3cf6851eea1e1cd1","permalink":"https://secpriv.lbl.gov/project/resilience/","publishdate":"2015-09-24T00:00:00Z","relpermalink":"/project/resilience/","section":"project","summary":"This project focused on mapping and analyzing the qualities of resilient networks by investigating components of redundancy, diversity, quality of service, etc... The project's goal is to be able to quantify and compare the resilience of networks in a scientifically meaningful way.  This project was led at LBNL by [Sean Peisert](https://www.cs.ucdavis.edu/~peisert/).","tags":["network","fault tolerance","research cyberinfrastructure","secure systems"],"title":"Host and Network Resilience","type":"project"},{"authors":null,"categories":null,"content":"Two principal components for providing protection in large-scale distributed systems are Byzantine fault tolerance (BFT) and intrusion detection systems (IDS). BFT is used to implement strictly consistent replication of state in the face of arbitrary failures, including those introduced by malware and Internet pathogens. Intrusion detection relates to a broad set of services that detect events that could indicate the presence of an ongoing attack. But BFT traditionally suffers from high latency and replication requirements. But as these two components approach system security differently, we believe that intrusion detection has the potential to has the potential to improve BFT. The integration of these two efforts, at both the fundamental and system levels, is the theme of this research effort.\nMore information is available at the UC Davis BFT+IDS project web site.\nMore information is available on other Berkeley Lab research projects focusing on cybersecurity in general, as well as specifically on cybersecurity for research cyberinfrastructure and high-performance computing.\n","date":1443052800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1443052800,"objectID":"c460e5bf174e0da92d6a43ef9b76f5cd","permalink":"https://secpriv.lbl.gov/project/nsf-bft-ids/","publishdate":"2015-09-24T00:00:00Z","relpermalink":"/project/nsf-bft-ids/","section":"project","summary":"This project was funded by NSF's SaTC program, and was co-led by [Sean Peisert](https://www.cs.ucdavis.edu/~peisert/). The theme of this effort was to integrate Byzantine fault tolerance (BFT) into intrusion detection systems (IDS), at both the fundamental and system levels, thereby improving both BFT and IDS. potential to improve BFT.","tags":["fault tolerance"],"title":"Symbiosis in Byzantine Fault Tolerance and Intrusion Detection","type":"project"},{"authors":null,"categories":null,"content":"Principal Investigators Jen Schopf (Lead PI at IU)\nSean Peisert (Former Co-PI; Former Lead at LBNL and UC Davis → Security Advisor)\nAndrew Lake (Current Lead at LBNL) Jason Leigh (Co-PI; Lead at UH)\nLBNL Project Alumni: Jon Dugan\nDipankar Dwivedi (Postdoc)\nAnna Giannakou (Postdoc) Monte Goode\nBrian Tierney (ESnet Scientist 1988-2017) → retired\nChris Tracy\nNetSage is an open privacy-aware network measurement, analysis, and visualization service designed to address the needs of today’s international networks. Modern science is increasingly data-driven and collaborative in nature, producing petabytes of data that can be shared by tens to thousands of scientists all over the world. The NSF-supported International Research Network Connection (IRNC) links have been essential to performing these science experiments.\nProviding near real-time monitoring and visualization of international data transfers will help ensure that scientific workflows are operating at maximum efficiency. NetSage services provide an unprecedented combination of passive measurements, including SNMP data, flow data, and Zeek/Bro-based traffic analysis, as well as active measurements, mainly perfSONAR, and longitudinal network performance data visualization. User privacy is a significant concern in this project given the data flowing through the exchange points. NetSage addresses these concerns through the use of a privacy advisory board that will ensure the data gathering activities are conducted to meet all community standards. The proposed work is a partnership between Indiana University, University of California at Davis, Lawrence Berkeley National Laboratory, and University of Hawaii at Manoa. This uniquely strong team combines backgrounds in production international network support, networking measurement and prediction tools, network-intensive applications and data visualization.\nThis project is supported by the National Science Foundation’s International Research Network Connections (IRNC) program.\nRead more and demo the interface at the project web site.\nNetflow Analysis and Prediction Source Code at GitHub\nPublications resulting from this project: Anna Giannakou, Dipankar Dwivedi, and Sean Peisert, “A Machine Learning Approach for Packet Loss Prediction in Science Flows,” Future Generation Computer Systems - Special Issue on Innovating the Network for Data Intensive Science - INDIS 2018, accepted 25 July, 2019.\nAlberto Gonzalez, Jason Leigh, Sean Peisert, Brian Tierney, Andrew Lee, Jennifer M. Schopf, “Monitoring Big Data Transfers Over International Research Network Connections”, Proceedings of IEEE BigData Congress 2017, Honolulu, Hawaii, June 2017,\nAlberto Gonzalez, Jason Leigh, Sean Peisert, Brian Tierney, Andrew Lee, Jennifer M. Schopf, “NetSage: Open Privacy-Aware Network Measurement, Analysis, And Visualization Service”, Proceedings of TNC16 Networking Conference, Prague, Czech Republic, June 2016,\nSoftware resulting from this project: Research Network Transfer Performance Predictor (netperf-predict)\nMore information is available on other Berkeley Lab research projects focusing on cybersecurity in general, as well as specifically on cybersecurity for research cyberinfrastructure and high-performance computing.\n","date":1430438400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1430438400,"objectID":"ac59ea19de7a2bb56c7d23f43094829d","permalink":"https://secpriv.lbl.gov/project/nsf-netsage/","publishdate":"2015-05-01T00:00:00Z","relpermalink":"/project/nsf-netsage/","section":"project","summary":"NetSage is a network measurement, analysis and visualization service funded by the National Science Foundation and is designed to address the needs of today's international networks.  This project is co-led by [Sean Peisert](https://www.cs.ucdavis.edu/~peisert/) at LBNL.","tags":["network","machine learning","research cyberinfrastructure"],"title":"NetSage - an open privacy-aware network measurement, analysis, and visualization service","type":"project"},{"authors":null,"categories":null,"content":"The power distribution grid, like many cyber physical systems, was developed with careful consideration for safe operation. However, a number of features of the power system make it particularly vulnerable to cyber attacks via IP networks. “IT security” approaches to dealing with malware and other cyber attacks via a network include traditional intrusion detection systems, firewalls, encryption, etc… These techniques can help, but as we’ve observed in a previous project, traditional IT security techniques tend to leave a gap in safety and protection when applied to cyber-physical devices because they do not consider physical information known about the cyber-physical device they are protecting. Not only does this leave a gap in protection, but it ignores valuable information that could be used to better protect the cyber-physical device.\nThe goal of this is to design and implement a measurement network, which can detect and report the resultant impact of cyber security attacks on the distribution system network. The cyber-attacks against the distribution grid that we primarily focus on are ones that (1) modify the distribution grid operation and causing it to behave in individually or collectively disruptive or damaging ways; (2) mask communication from substation components in the distribution grid, through cyber denial-of-service attack, and prevent awareness of the actual operational function; and (3) mask communication to substation components in the distribution grid, through cyber denial of service attack, causing misbehaving components to fail to receive instructions to restore safe operation. The detection and reporting will be within short time frame, at present not communicable or measured on the distribution system, allowing operators to perform remedial action.\nTo do this, this project uses micro phasor measurement units to capture information about the physical state of the power distribution grid and combines this with SCADA command monitoring in real time. The project will build models of safe and unsafe states of the distribution grid so that certain classes cyber attacks can potentially be detected by their physical effects on the power distribution grid alone. The result will be a system that provides an independent, integrated picture of the distribution grid’s physical state, which will be difficult for a cyber-attacker to subvert using data-spoofing techniques.\nSee the detection algorithms in action via our graphical front-end at the LBNL Power Data Portal.\nSource code for the LBNL Stream-Processing Architecture for Real-time Cyber-physical Security (SPARCS) is available at GitHub.\nThis project is supported by the U.S. Department of Energy’s Cybersecurity for Energy Delivery Systems (CEDS) program.\nPrincipal Investigators: Sean Peisert (PI; LBNL)\nCiaran Roberts (Co-PI; LBNL)\nAnna Scaglione (Co-PI; ASU)\nSenior Personnel Reinhard Gentz (LBNL)\nStudents Mahdi Jamei (ASU)\nIndustry Partners: EnerNex (Erich Gunther (previously), Aaron Snyder, Bob Zavadil, Dave Mueller, Jens Schoene)\nEPRI (Galen Rasche, Jens Boemer)\nPower Standards Laboratory (Alex McEachern)\nCorelight (née Broala)\nOSIsoft (John Matranga)\nRiverside Public Utilities\nSouthern Company\nProject Alumni: Chuck McParland (Former Co-PI; LBNL → RTISYS / LBNL Affiliate)\nEmma Stewart (Former Co-PI; LBNL → LLNL)\nPress regarding this project: Electric grid protection through low-cost sensors, machine learning (Route 50; formerly Government Computing News) — September 21, 2018\nCyber Defense Tool Is an Early Warning System for Grid Attacks (IEEE Spectrum Energywise Blog) — March 27, 2018\nCombination of Old and New Yields Novel Power Grid Cybersecurity Tool — March 7 2018\nPublications resulting from this project: Mahdi Jamei, Raksha Ramakrishna, Teklemariam Tesfay, Reinhard Gentz, Ciaran Roberts, Anna Scaglione, and Sean Peisert, “Phasor Measurement Units Optimal Placement and Performance Limits for Fault Localization,” IEEE Journal on Selected Areas in Communications (J-SAC), Special Issue on Communications and Data Analytics in Smart Grid, accepted 2 October, 2019. [DOI]\nCiaran Roberts, Anna Scaglione, Mahdi Jamei, Reinhard Gentz, Sean Peisert, Emma M. Stewart, Chuck McParland, Alex McEachern, and Daniel Arnold, “Learning Behavior of Distribution System Discrete Control Devices for Cyber-Physical Security,” IEEE Transactions on Smart Grid, accepted 31 July, 2019. [DOI]\nReinhard Gentz, Sean Peisert, Joshua Boverhof, Daniel Gunter, “SPARCS: Stream-Processing Architecture applied in Real-time Cyber-physical Security” Proceedings of the 15th IEEE International Conference on e-Science (eScience), San Diego, CA, September 2019.\nMahdi Jamei, Security Analysis of Interdependent Critical Infrastructures: Power, Cyber and Gas, PhD dissertation, Arizona State University, December 2018.\nMahdi Jamei, Anna Scaglione, and Sean Peisert, “Low-Resolution Fault Localization Using Phasor Measurement Units with Community Detection,” Proceedings of the 2018 IEEE …","date":1410739200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1410739200,"objectID":"c885156efc9e450c5cebfb183ec497e9","permalink":"https://secpriv.lbl.gov/project/ceds-upmu/","publishdate":"2014-09-15T00:00:00Z","relpermalink":"/project/ceds-upmu/","section":"project","summary":"This project used micro-PMU measurements and SCADA commands to develop a system to detect cyberattacks against the power distribution grid. It was funded by DOE OE's CEDS program and was led by [Sean Peisert](https://www.cs.ucdavis.edu/~peisert/).","tags":["power grid","machine learning","cyber-physical systems","network"],"title":"Cyber Security of Power Distribution Systems by Detecting Differences Between Real-time Micro-Synchrophasor Measurements and Cyber-Reported SCADA","type":"project"},{"authors":null,"categories":null,"content":"Can synchronized distribution level phasor measurements enhance planning for power flow and system control, security and resiliency in the modernized grid?\nBy installing a number of µPMUs in various locations in the electric distribution system and evaluating the data from them, this data supports a variety of projects to determine whether refined measurement of voltage phase angles can enable advanced diagnostic, monitoring and control methodologies in distribution systems, and to begin developing algorithms for diagnostic applications based on µPMU data.\nApplications being studied include:\nState estimation and enhanced visibility for distribution system operators Characterization of loads and distributed generation Diagnosis of potentially problematic conditions such as oscillations or FIDVR Microgrid synchronization Cybersecurity of power distribution grid equipment The data available is a set of power measurements and annotations, and an interface for exploring and downloading that data. The power measurements are collected by micro-phasor measurement units (μPMUs) and PQube3 power quality meters manufactured by Power Standards Laboratory in Alameda, CA and located at Lawrence Berkeley National Laboratory, as well as other sites.\nThe manufacturer Power Standards Laboratory (PSL) supplied and tested the measurement technology, which is based on PSL’s already commercialized PQube power quality recorder.\nFor the data collected by the CEDS µPMU project, please reference this publication for citations, which also contains more information on the dataset:\nSean Peisert, Reinhard Gentz, Joshua Boverhof, Chuck McParland, Sophie Engle, Abdelrahman Elbashandy, and Dan Gunter, “LBNL Open Power Data,” LBNL Technical Report, May 2017. DOI:10.21990/C21599\nThis work was sponsored in part by the U.S. Department of Energy’s Cybersecurity for Energy Delivery Systems program.\nAdditional data Several legacy datasets generated by an AEPA-E project are also hosted and linked to via this web site and are available for direct download:\nDownload Open µPMU dataset raw data files over HTTP For that data, please reference this publication for citations:\nStewart E.M., et al “Open μPMU: A real world reference distribution micro-phasor measurement unit data set for research and application development,” LBNL Technical Report 1006408, October 2016.\nThis research was sponsored in part by the U.S. Department of Energy ARPA-E program (DE-AR0000340). The California Institute of Energy and Environment (CIEE) led this project along with Lawrence Berkeley National Laboratory (LBNL).\nQuestions or need help? If you have questions or need help with accessing the data, please submit an issue.\nDisclaimers The information, data, or work presented herein was funded in part by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.\n","date":1410739200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1410739200,"objectID":"ede6006cca15bae7eec0471789ba62fa","permalink":"https://secpriv.lbl.gov/project/powerdata/","publishdate":"2014-09-15T00:00:00Z","relpermalink":"/project/powerdata/","section":"project","summary":"This distribution level phasor measurement data can be used to understand ways to enables advanced diagnostic, monitoring and control methodologies in distribution systems.","tags":["power grid"],"title":"LBNL Power Data","type":"project"},{"authors":null,"categories":null,"content":"Using seed funding from the NNSA CIO, this consortium of eight DOE laboratories worked to form an enduring, national computer security research laboratory to address cybersecurity threats. Research efforts that the laboratory would address ranged from very short-range, tactical issues that leverage current capabilities, to very long-range research with results and solutions that may not be deployable for over 20 years. LBNL’s effort was led by Deb Agarwal and Sean Peisert.\nMore information is available on other Berkeley Lab research projects focusing on cybersecurity in general, as well as specifically on cybersecurity for research cyberinfrastructure and high-performance computing.\n","date":1380499200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1380499200,"objectID":"b7a951b396d537035b40830d6274e94e","permalink":"https://secpriv.lbl.gov/project/nnsa-csl/","publishdate":"2013-09-30T00:00:00Z","relpermalink":"/project/nnsa-csl/","section":"project","summary":"Using seed funding from the NNSA CIO, this consortium of eight DOE laboratories worked to form an enduring, national computer security research laboratory to address cybersecurity threats. LBNL's effort was led by Deb Agarwal and [Sean Peisert](https://www.cs.ucdavis.edu/~peisert/).","tags":["research cyberinfrastructure"],"title":"NNSA Cyber Sciences Lab (CSL)","type":"project"},{"authors":null,"categories":null,"content":"This project was funded by NSF’s CISE Directorate, and was led by Sean Peisert. The project sought to define and prototype a security layer using a method of intrusion detection based on mobile agents and swarm intelligence. The project’s goal was to provide a lightweight, decentralized, intrusion detection method that is adaptable to changing threats while communicating suspicious activity across hierarchical layers to humans who can respond when needed. The goal was to augment, not replace, more traditional security mechanisms.\nMore information is available at the UC Davis GENI Hive Mind project web site.\nMore information is available on other Berkeley Lab research projects focusing on cybersecurity in general, as well as specifically on cybersecurity for research cyberinfrastructure and high-performance computing.\n","date":1378252800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1378252800,"objectID":"f9be34b6031382e8735a8c7b368d27b5","permalink":"https://secpriv.lbl.gov/project/nsf-geni-hivemind/","publishdate":"2013-09-04T00:00:00Z","relpermalink":"/project/nsf-geni-hivemind/","section":"project","summary":"This project sought to define and prototype a security layer using a method of intrusion detection based on mobile agents and swarm intelligence. The project was funded by NSF's CISE Directorate, and was led by [Sean Peisert](https://www.cs.ucdavis.edu/~peisert/).","tags":["network","machine learning"],"title":"The Hive Mind: Applying a Distributed Security Sensor Network to GENI.","type":"project"},{"authors":["Cybersecurity Research for Science and Energy at the Berkeley Lab","Robert Ford"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. Create your slides in Markdown - click the Slides button to check out the example. Supplementary notes can be added here, including code, math, and images.\n","date":1372636800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1372636800,"objectID":"ff6a19061a984819d30c916886db56ef","permalink":"https://secpriv.lbl.gov/publication/example/","publishdate":"2017-01-01T00:00:00Z","relpermalink":"/publication/example/","section":"publication","summary":"Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis posuere tellus ac convallis placerat. Proin tincidunt magna sed ex sollicitudin condimentum.","tags":[],"title":"An example conference paper","type":"publication"},{"authors":null,"categories":null,"content":" This project is called, “Application of Cyber Security Techniques in the Protection of Efficient Cyber-Physical Energy Generation Systems.” In this project, we designed and developed a security monitoring and analysis framework for control systems and smart grid technologies. This system is designed to enhance resiliency of the system by integrating traditional computer security and safety engineering techniques. The goal is to integrate the monitoring and analysis of IP network traffic, as well as serial communications and physical constraints within a single intrusion detection system (IDS) framework and provide capabilities for determining the physical safety of system operations by simultaneously examining behavior at multiple hierarchical layers and contexts.\nThis project was supported by the U.S. Department of Energy’s Cybersecurity for Energy Delivery Systems (CEDS) program.\nPrincipal Investigators: Sean Peisert (PI; LBNL)\nChuck McParland (PI; LBNL)\nAnna Scaglione (Lead at UC Davis)\nPostdocs Masood Parvania (Postdoc; UC Davis → faculty at U of Utah)\nZhifang Wang (UC Davis → faculty at VCU)\nStudents Mahnoosh Alizadeh (UC Davis → Stanford)\nJonathan Ganz (UC Davis)\nReinhard Gentz (UC Davis)\nMahdi Jamei (UC Davis)\nGeorgia (Gina) Koutsandria (UC Davis → Univ. of Rome, “La Sapienza”)\nXiao (Simon) Li (UC Davis → UC Berkeley)\nVishak Muthukumar (UC Davis)\nSteven Templeton (UC Davis)\nTeng Wang (UC Davis)\nPartners: Corelight (née Broala)\nOSIsoft (John Matranga)\nMore information on the Energy Sector Control Systems Working Group (ESCSWG) page\nLBNL Physics-Based IDS Source Code at GitHub\nPublications resulting from this project: Georgia Koutsandria, Reinhard Gentz, Mahdi Jamei, Anna Scaglione, Sean Peisert, and Chuck McParland, “A Real-Time Testbed Environment for Cyber-Physical Security on the Power Grid,” Proceedings of the First ACM Workshop on Cyber-Physical Systems Security \u0026amp; Privacy (CPS-SPC), Denver, CO, October 16, 2015. [BibTeX] [DOI]\nChuck McParland, Sean Peisert, and Anna Scaglione, “Monitoring Security of Networked Control Systems: It’s the Physics,” IEEE Security and Privacy,12(6), November/December 2014. [BibTeX] [DOI]\nGeorgia Koutsandria, Vishak Muthukumar, Masood Parvania, Sean Peisert, Chuck McParland, and Anna Scaglione, “A Hybrid Network IDS for Protective Digital Relays in the Power Transmission Grid,” Proceedings of the 5th IEEE International Conference on Smart Grid Communications (SmartGridComm), Venice, Italy, November 3–6, 2014. [BibTeX] [DOI]\nGeorgia Koutsandria, Cyber Physical Security for Power Grid Protection, M.S. Thesis, Dept. of Electrical and Computer Engineering, University of California, Davis, Sept. 2014. [BibTeX]\nMasood Parvania, Georgia Koutsandria, Vishak Muthukumar, Sean Peisert, Chuck McParland, and Anna Scaglione, “Hybrid Control Network Intrusion Detection Systems for Automated Power Distribution Systems,” Proceedings of the 1st International Workshop on Trustworthiness of Smart Grids (ToSG), Atlanta, GA, June 23, 2014. [BibTeX] [DOI]\nXiao Li, Zhifang Wang, Vishak Muthukumar, Anna Scaglione, Sean Peisert, and Chuck McParland, “Networked Loads in the Distribution Grid,” Proceedings of the 2012 Asia-Pacific Signal \u0026amp; Information Processing Association (APSIPA) Annual Summit and Conference, Hollywood, CA, December 3–6, 2012. [BibTeX] [DOI]\nMore information is available on other Berkeley Lab research projects focusing on cybersecurity in general, as well as specifically on cybersecurity for energy delivery systems.\n","date":1325376000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1325376000,"objectID":"9ac9ed5f12345d13a795d9e74283591e","permalink":"https://secpriv.lbl.gov/project/ceds-cps-security/","publishdate":"2012-01-01T00:00:00Z","relpermalink":"/project/ceds-cps-security/","section":"project","summary":"The goal of this project was to design and implement a measurement network, which can detect and report the resultant impact of cyber security attacks on the distribution system network. It was funded by DOE OE's CEDS program and was co-led by [Chuck McParland](https://crd.lbl.gov/divisions/scidata/idf/affiliates/charles-mcparland/) and [Sean Peisert](https://www.cs.ucdavis.edu/~peisert/).","tags":["power grid","cyber-physical systems","network"],"title":"Application of Cyber Security Techniques in the Protection of Efficient Cyber-Physical Energy Generation Systems","type":"project"},{"authors":null,"categories":null,"content":"In this project, CRD researchers developed mathematical and statistical techniques to analyze the secure access and use of high-performance computer systems. The project was funded by the U.S. Department of Energy’s Applied Mathematics Section. The overall goals of the project were to develop mathematical and statistical methods to detect intrusions of high-performance computing systems. Our mathematical analysis is predicated on the fact that large HPC systems represent unique environments, quite unlike unspecialized systems or general Internet traffic. User behavior on HPC systems tends to be much more constrained (often driven by research deadlines and limited computational resources), and is generally limited to certain paradigms of computation (the set of codes performing the bulk of execution provide a rich source of information). In addition, the collaboration networks of users on an HPC system exhibits special characteristics than can be exploited to detect misuse or fraud. In this research work, we employed real system data, which we have obtained in collaboration with staff in the NERSC Division of LBNL.\nLBNL was this lead institution for this activity, which also involved the University of California at Davis (UC Davis) and the International Computer Science Institute (ICSI) at UC Berkeley through LBNL subcontracts. Over the course of the project, LBNL senior investigators included Deb Agarwal, David H. Bailey (PI), Scott Campbell, Juan Meza, Sean Peisert, Taghrid Samak, and Alexander Slepoy. The UC Davis senior investigators were Sean Peisert (joint appointment) and Matt Bishop. ICSI senior investigators included Vern Paxson and Robin Sommer. The research team was supported in their software development and data collection techniques by the staff at the National Energy Research Scientific Computing Center (NERSC). A variety of students and postdocs at the Berkeley Lab, UC Berkeley, and UC Davis, were also involved, as were external collaborators (not funded by this grant), at Mt. Sinai School of Medicine and the University of San Francisco.\nKey work included:\nThe adaptation and development of a “rule-ensemble” technique from the field of mathematical statistics to accurately and economically finds class labels, and to determine which parameter constraints are most useful for predicting these labels. The development and application of a technique of fingerprinting computation on HPC machines based on network theory and machine learning. An examination of Domain Name Server (DNS) traffic using entropy analysis. Development of data sanitization techniques, so that real cybersecurity data can be shared with a wider community of researchers without compromising user privacy. Development of a script to generate a set of synthetic jobs, which then can be used to test job fingerprint algorithms. A selected list of publications resulting from this project follows.\n2013 Orianna DeMasi, Taghrid Samak, David H. Bailey, “Identifying HPC Codes via performance logs and machine learning”, Proceedings of the First Workshop on Changing Landscapes in HPC Security, June 17, 2013,\nSean Whalen, Sean Peisert, Matt Bishop, “Multiclass Classification of Distributed Memory Parallel Computations”, Pattern Recognition Letters (PRL), February 2013, 34(3):322-329, doi: 10.1016/j.patrec.2012.10.007\nTaghrid Samak, Christine Morin, David H. Bailey, “Energy consumption models and predictions for large-scale systems”, Proceedings of the Ninth Workshop on High-Performance, Power-Aware Computing, January 22, 2013, to appea,\n2012 Sophie Engle, Sean Whalen, “Visualizing Distributed Memory Computations with Hive Plots”, Proceedings of the 9th ACM International Symposium on Visualization for Cyber Security (VizSec), Seattle, WA, ACM, October 15, 2012, 56-63, doi: 10.1145/2379690.2379698\nDavid H. Bailey, Orianna DeMasi, Juan Meza, “Feature selection and multi-class classification using a rule ensemble method”, May 25, 2012,\nSean Whalen, Sophie Engle, Sean Peisert, Matt Bishop, “Network-Theoretic Classification of Parallel Computation Patterns,” International Journal of High Performance Computing Applications (IJHPCA), 26(2):159-169, May 2012. doi: 10.1177/1094342012436618\n2011 Matt Bishop, Justin Cummins, Sean Peisert, Bhume Bhumitarana, Anhad Singh, Deborah Agarwal, Deborah Frincke, Michael Hogarth, “Relationships and Data Sanitization: A Study in Scarlet”, Proceedings of the 2010 New Security Paradigms Workshop (NSPW), Concord, MA, ACM, September 2011, 151-164, doi: 10.1145/1900546.1900567\nOrianna DeMasi, Juan Meza and David H. Bailey, “Dimension reduction using rule ensemble machine learning methods: A numerical study of three ensemble methods”, August 30, 2011,\nSean Whalen, Sean Peisert, Matt Bishop, “Network-Theoretic Classification of Parallel Computation Patterns”, Proceedings of the First International Workshop on Characterizing Applications for Heterogeneous Exascale Systems (CACHES), Tucson, AZ, IEEE Computer Society, June 4, …","date":1285891200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1285891200,"objectID":"f6587688ff5fd379ed3ed2814dd81071","permalink":"https://secpriv.lbl.gov/project/ascr-ids-hpc/","publishdate":"2010-10-01T00:00:00Z","relpermalink":"/project/ascr-ids-hpc/","section":"project","summary":"This project developed mathematical and statistical techniques to analyze the secure access and use of high-performance computer systems. It was funded by DOE ASCR and was originally led by David H. Bailey.","tags":["HPC","machine learning","network","research cyberinfrastructure","secure systems"],"title":"A Mathematical and Data-Driven Approach to Intrusion Detection for High-Performance Computing","type":"project"},{"authors":null,"categories":null,"content":"This seed project looked at defining means for understanding what data can be sanitized, and how. Traditional techniques often either make data unusable for research or operational purposes or fail to completely sanitize the data. Thus, our data sanitization work built on past techniques by also using an “open world” assumption. We also asked, what are the relationships between data fields that would need to be made in order to reveal certain information, what associations need to be protected in order to conceal certain information, and, finally, given policy constraints by the different stakeholders, can a dataset be sanitized in a way that satisfies the policies of all of those people, or would certain compromises to one or more policies need to be made? At LBNL, this project was led by Sean Peisert and was funded by the Institute for Information Infrastructure Protection (I3P).\nMore information is available at the UC Davis data sanitization project web site.\nMore information is available on other Berkeley Lab research projects focusing on cybersecurity in general, as well as specifically on cybersecurity for research cyberinfrastructure and high-performance computing.\n","date":1285027200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1285027200,"objectID":"dcd35d6d50b888ebca9cc63c71fd60e9","permalink":"https://secpriv.lbl.gov/project/i3p-sanitization/","publishdate":"2010-09-21T00:00:00Z","relpermalink":"/project/i3p-sanitization/","section":"project","summary":"This  project looked at defining means for understanding what data can be sanitized, and how.  At LBNL, this project was led by [Sean Peisert](https://www.cs.ucdavis.edu/~peisert/) and was funded by the Institute for Information Infrastructure Protection (I3P).","tags":["data privacy","research cyberinfrastructure"],"title":"I3P Data Sanitization","type":"project"},{"authors":null,"categories":null,"content":"We have had several different thrusts to our work with elections and electronic voting.\nOne key thrust was to explore process composition tools as applied to elections, concentrating particularly on mail-in and Internet voting. This included exploration of how to compose systems from pre-analyzed process components, how to analyze the vulnerability of these systems to attacks, and how to guarantee that important security properties are ensured for the resulting composed system. The underlying processes represent aspects of national and local elections, their composition produces an election process, and analysis of the composition gives insight into potential errors or attacks on the election. Providing an approach for formally reasoning about human participation extends current security work. The project also breaks new ground by exploring process-based approaches for modeling and defending against attacks.\nAnother thrust that we have examined looked at auditing. Election auditing verifies that the systems and procedures work as intended, and that the votes have been counted correctly. If a problem arises, forensic techniques enable auditors to determine what happened and how to compensate if possible. Complicating this is that the audit trails enabling analysis of failures may contain information that either exposes the identity of the voter (enabling voter coercion, for example); or that communicates a message to a third party (enabling vote selling). The goal of this project was to determine the information needed to assess whether the election process in general, and e-voting machines in particular, operate with the desired degree of assurance, especially with respect to anonymity and privacy.\nThe leads at UC Davis for this work were:\nMatt Bishop (PI; UC Davis) Sean Peisert (CoPI; UC Davis and LBNL)\nThis work was performed in close cooperation with the Marin County Registrar of Voters’ office and the Yolo County Clerk-Recorder’s office.\nWe also collaborated closely with Lee Osterweil, Lori Clarke, George Avrunin, and their graduate students and postdocs in the LASER Lab at UMass Amherst.\nMore information is available at the UC Davis elections and electronic voting project web site and UMass Amherst elections project web site.\nSelected publications resulting from this project Matt Bishop, Philip Stark, Josh Benaloh, Joseph Kiniry, Ron Rivest, Sean Peisert, Joseph Hall, and Vanessa Teague, “Open-Source Software Won’t Ensure Election Security,” Lawfare, August 24, 2017. [bib]\nLeon J. Osterweil, Matt Bishop, Heather M. Conboy, Huong Phan, Borislava I. Simidchieva, George S. Avrunin, Lori A. Clarke, and Sean Peisert, “A Comprehensive Framework for Using Iterative Analysis to Improve Human-Intensive Process Security: An Election Example,” ACM Transactions on Privacy and Security (TOPS), 20(2), March 2017. [DOI] [OA] [CDL]\nMatt Bishop, Heather Conboy, Huong Phan, Borislava I. Simidchieva, George Avrunin, Lori Clarke, Lee Osterweil, and Sean Peisert,\u0026#34; Insider Detection by Process Analysis,\u0026#34; Proceedings of the 2014 Workshop on Research for Insider Threat (WRIT), IEEE Computer Society Security and Privacy Workshops, San Jose, CA, May 18, 2014.\nMatt Bishop and Sean Peisert, “Security and Elections,” IEEE Security and Privacy,10(5), pp. 64–67, Sept.-Oct. 2012. [BibTeX] [DOI]\nHuong Phan, George Avrunin, Matt Bishop, Lori Clarke, and Leon J. Osterweil, “A Systematic Process-Model-Based Approach for Synthesizing Attacks and Evaluating Them,” Proceedings of the 2012 Electronic Voting Technology Workshop/Workshop on Trustworthy Elections (EVT/WOTE), Washinton, D.C., August 2012.\nBorislava I. Simidchieva, Sophie J. Engle, Michael Clifford, Alicia Clay Jones, Sean Peisert, Matt Bishop, Lori A. Clarke, and Leon J. Osterweil, “Modeling Faults to Improve Election Process Robustness,” Proceedings of the 2010 Electronic Voting Technology Workshop/Workshop on Trustworthy Elections (EVT/WOTE), Washinton, D.C., August 11–13, 2010. [BibTeX] [Authoritative]\nMatt Bishop, Sean Peisert, Candice Hoke, Mark Graff, and David Jefferson, “E-Voting and Forensics: Prying Open the Black Box,” Proceedings of the 2009 Electronic Voting Technology Workshop/Workshop on Trustworthy Elections (EVT/WOTE), Montreal, Canada, August 10–11, 2009. [BibTeX] [Authoritative]\nSean Peisert, Matt Bishop, and Alec Yasinsac, “Vote Selling, Voter Anonymity, and Forensic Logging of Electronic Voting Machines,” Proceedings of the 42nd Hawaii International Conference on System Sciences (HICSS), Decision Technologies and Service Sciences Track, Digital Forensics Pedagogy and Foundational Research Activity Minitrack, Waikoloa, HI, January 5–8, 2009. (Nominated for Best Paper Award) [BibTeX] [DOI]\nMatt Bishop, Mark Graff, Candice Hoke, David Jefferson, and Sean Peisert, “Resolving the Unexpected in Elections: Election Officials’ Options,” October 8, 2008. [BibTeX] [CDL]\nDistributed by the Center For Election Excellence and the American Bar Association. Sponsors: …","date":1222819200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1222819200,"objectID":"01a86d7ebf694b8a31611fdb58483fd7","permalink":"https://secpriv.lbl.gov/project/election-process-modeling-analysis/","publishdate":"2008-10-01T00:00:00Z","relpermalink":"/project/election-process-modeling-analysis/","section":"project","summary":"This  project looked at defining means for understanding what data can be sanitized, and how.  At LBNL, this project was led by [Sean Peisert](https://www.cs.ucdavis.edu/~peisert/) and was funded by the Institute for Information Infrastructure Protection (I3P).","tags":["formal methods"],"title":"Election Process Modeling and Analysis","type":"project"},{"authors":null,"categories":null,"content":"This project is looking at defining, analyzing, and seeking methods of ameliorating the insider threat. Whereas security has traditionally been defined with respect to a perimeter, using static and binary access control decisions, we assert that such a perimeter no longer exists and that traditional access control techniques inhibit authorized users from performing their job. We define the “insider threat” as a combination of (a) access to a particular resource, (b) knowledge of a particular resource, and/or (c) trust of an individual by a particular organization. Moreover, the insider threat is clearly also not binary, but a spectrum of “insiderness” based on the aforementioned qualities. In the past, we have sought to develop access control solutions that integrate this understanding in combination while also being informed by social science of how users may react most optimally to system access control and countermeasures. More recently, we have used a process modeling and analysis approach in the context of elections to evaluate insider threats.\nMore information is available at the UC Davis Insider Threat project web site.\nMore information is available on other Berkeley Lab research projects focusing on cybersecurity in general, as well as specifically on cybersecurity for research cyberinfrastructure and high-performance computing.\n","date":1220227200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1220227200,"objectID":"e7061aa17c7bb2e9282df9510e2b017f","permalink":"https://secpriv.lbl.gov/project/insider-threat/","publishdate":"2008-09-01T00:00:00Z","relpermalink":"/project/insider-threat/","section":"project","summary":" This project looked at defining, analyzing, and seeking methods of ameliorating the insider threat.","tags":["forensics","insider threat"],"title":"Insider Threat","type":"project"},{"authors":null,"categories":null,"content":"This project is looking at establishing a rigorous, scientific model of forensic logging and analysis that is both efficient and effective at establishing the data that is necessary to record in order to understand past events. Additional applications include e-voting and forensic evidence in the courtroom. While forensics traditionally looks at available data and attempts to draw conclusions from it, we, in contrast, seek to understand the questions that we want to answer, and then derive what data is necessary to support answers to those questions.\nMore information is available at the UC Davis Computer Forensics project web site.\n","date":1188604800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1188604800,"objectID":"bee1cc289b844da61ea0bb8d714bea62","permalink":"https://secpriv.lbl.gov/project/i3p-forensics/","publishdate":"2007-09-01T00:00:00Z","relpermalink":"/project/i3p-forensics/","section":"project","summary":"This project is looking at establishing a rigorous, scientific model of forensic logging and analysis that is both efficient and effective at establishing the data that is necessary to record in order to understand past events.  This work was led by [Sean Peisert](https://www.cs.ucdavis.edu/~peisert/).","tags":["forensics"],"title":"Computer Forensics","type":"project"},{"authors":null,"categories":null,"content":"Project Summary The diverse set of organizations and software components involved in a typical collaboratory make providing a seamless security solution difficult. In addition, the users need support for a broad range of frequency and locations for access to the collaboratory. A collaboratory security solution needs to be robust enough to ensure that valid participants are not denied access because of its failure. There are many tools that can be applied to the task of securing collaborative environments and these include public key infrastructure, secure sockets layer, Kerberos, virtual and real private networks, grid security infrastructure, and username/password. A combination of these mechanisms can provide effective secure collaboration capabilities.\nPrincipal Investigators and Senior Personnel: Deb Agarwal Karlo Berket Keith R. Jackson Charles McParland Marcia Perry Mary R. Thompson\nPublications resulting from this project: Karlo Berket and Deborah Agarwal. Enabling Secure Ad-Hoc Collaboration. In Proceedings of the Workshop on Advanced Collaborative Environments (WACE), Seattle, WA, 2003.\nDeborah Agarwal, Markus Lorch, Mary Thompson, and Marcia Perry. A New Security Model for Collaborative Environments. In Proceedings of the Workshop on Advanced Collaborative Environments (WACE), Seattle, WA, June 2003.\nD. Agarwal and K. Berket. Supporting dynamic ad hoc collaboration capabilities. Arxiv preprint cs/0307037, 2003.\nDeborah Agarwal, Charles McParland, and Marcia Perry. Supporting collaborative computing and interaction. Technical report LBNL-50418, Lawrence Berkeley National Laboratory, 2002.\nDeborah Agarwal, Keith Jackson, and Mary Thompson. Securing collaborative environments. Technical report LBNL-50427, Lawrence Berkeley National Laboratory, 2002.\nMore information is available on other Berkeley Lab research projects focusing on cybersecurity in general, as well as specifically on cybersecurity for research cyberinfrastructure and high-performance computing.\n","date":1064880000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1064880000,"objectID":"785c76357c3fc3d18dc3fe880746a3ed","permalink":"https://secpriv.lbl.gov/project/secure-collaboration/","publishdate":"2003-09-30T00:00:00Z","relpermalink":"/project/secure-collaboration/","section":"project","summary":"This project developed advanced approaches to to secure scientific computing collaborations.","tags":["research cyberinfrastructure","secure systems"],"title":"Secure Collaboration","type":"project"},{"authors":null,"categories":null,"content":"Project Summary LBNL’s work in reliable multicast built on graduate work in developing reliable multicast by Agarwal (Totem), Berket (InterGroup), Keith Jackson, and Artur Muratas. Related to this work included implementation of a secure group protocol, and improving methods for securing group keys.\nOne of LBNL’s efforts in reliable multicast involved working groups created to set up the Comprehensive Nuclear-Test-Ban Treaty verification network, an international network connecting 321 monitoring stations around the world. The goal was to monitor in real-time whether there were activities that could be the result of nuclear weapon testing. LBNL’s role in the project examined the feasibility of using multicast in the Comprehensive Nuclear Test-Ban Treaty Organization (CTBTO) network, monitoring for signs of explosions.\nSubseqeuntly, LBNL also used reliable multicast to provide remote experiment access to Advanced Light Source (ALS) Beamline 7.\nLater, Chevassut led a team including Agarwal, Essiari, Farret, and Thompson on secure group multicast communication encryption.\nThe work at LBNL was funded primarily by DOE ASCR.\nPrincipal Investigators and Senior Personnel: Deb Agarwal Karlo Berket Olivier Chevassut Abdelilah Essiari Guillaume Farret Keith R. Jackson William Johnston Artur Muratas Mary R. Thompson\nCollaborators: Giuseppe Ateniese Emmanuel Bresson Pierre-Alain Fouque Pierrick Gaudry Damian Hasse Yongdae Kim Samuel Meder David Pointcheval Frank Siebenlist Gene Tsudik\nSelected publications resulting from this project: Michel Abdalla, Emmanuel Bresson, Olivier Chevassut, Bodo Möller, David Pointcheval, “Strong password-based authentication in TLS using the three-party group Diffie-Hellman protocol,” Int. J. Secur. Networks, 2(3/4):284-296, 2007.\nEmmanuel Bresson, Olivier Chevassut, and David Pointcheval, “A security solution for IEEE 802.11’s ad hoc mode: password-authentication and group Diffie-Hellman key exchange,” Int. J. Wirel. Mob. Comput., 2(1), 4-13, 2007.\nEmmanuel Bresson, Olivier Chevassut, David Pointcheval, “Provably secure authenticated group Diffie-Hellman key exchange,” ACM Trans. Inf. Syst. Secur., 10(3), 2007.\nMichel Abdalla, Emmanuel Bresson, Olivier Chevassut, Bodo Möller, David Pointcheval, “Provably secure password-based authentication in TLS,” Proceedings of AsiaCCS, 2006.\nOlivier Chevassut, Pierre-Alain Fouque, Pierrick Gaudry, and David Pointcheval. “The twist-augmented technique for key exchange” Proceedings of the International Workshop on Public Key Cryptography, 2006.\nMichel Abdalla, Emmanuel Bresson, Olivier Chevassut, David Pointcheval, “Password-Based Group Key Exchange in a Constant Number of Rounds,” Public Key Cryptography, 2006.\nMichel Abdalla, Olivier Chevassut, Pierre-Alain Fouque, David Pointcheval, A Simple Threshold Authenticated Key Exchange from Short Secrets, Proceedings of ASIACRYPT, 2005.\nMichel Abdalla, Olivier Chevassut, David Pointcheval, One-Time Verifier-Based Encrypted Key Exchange. Public Key Cryptography, 2005.\nOlivier Chevassut, Pierre-Alain Fouque, Pierrick Gaudry, and David Pointcheval, “Key derivation and randomness extraction” Cryptology ePrint Archive 2005.\nEmmanuel Bresson, Olivier Chevassut, Abdelilah Essiari, David Pointcheval, Mutual authentication and group key agreement for low-power mobile devices, Comput. Commun. 27(17):1730-1737, 2004.\nEmmanuel Bresson, Olivier Chevassut, David Pointcheval, New Security Results on Encrypted Key Exchange, Public Key Cryptography, 2004.\nLiang Fang, Samuel Meder, Olivier Chevassut, and Frank Siebenlist, “Secure password-based authenticated key exchange for web services, Proceedings of the 2004 Workshop on Secure Web Service, 2004.\nEmmanuel Bresson, Olivier Chevassut, David Pointcheval, Security proofs for an efficient password-based key exchange, Proceedings of CCS, 2003.\nEmmanuel Bresson, Olivier Chevassut, Abdelilah Essiari, David Pointcheval, Mutual Authentication and Group Key Agreement for low-Power Mobile Devices, Proceedings of MWCN, 2003\nKarlo Berket, Deborah A Agarwal, and Olivier Chevassut. A practical approach to the InterGroup protocols. Future Generation Computer Systems, 18(5):709–719, 2002.\nEmmmanuel Bresson, Olivier Chevassut, and David Pointcheval, “Group Diffie-Hellman Key Exchange Secure Against Dictionary Attacks,” Proceedings of ASIACRYPT, Queenstown, New Zealand, 2002.\nEmmanuel Bresson, Olivier Chevassut, and David Pointcheval. “Dynamic group Diffie-Hellman key exchange under standard assumptions,” Proceedings of Eurocrypt, Amsterdam, Netherlands, 2002\nOlivier Chevassut, “Authenticated group Diffie-Hellman key exchange: theory and practice,” 2002.\nEmmanuel Bresson, Olivier Chevassut, and David Pointcheval. “The group Diffie-Hellman problems,” Selected Areas in Cryptography: 9th Annual International Workshop, 2002.\nEmmanuel Bresson, Olivier Chevassut, David Pointcheval, Security Proofs for an Efficient Password-Based Key Exchange. IACR Cryptol. ePrint Arch. 2002.\nDeborah A. Agarwal. …","date":1033344000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1033344000,"objectID":"b37ac25e81d7fab2e917f7eb4aa00fb3","permalink":"https://secpriv.lbl.gov/project/ascr-multicast/","publishdate":"2002-09-30T00:00:00Z","relpermalink":"/project/ascr-multicast/","section":"project","summary":"This project examined the use of reliable multicast communication protocols, including for the Comprehensive test Ban Treaty.","tags":["nuclear treaty assurance","network","secure systems","cryptography"],"title":"Reliable Multicast for Continuous Data Transmission for Nuclear Treaty Verification","type":"project"},{"authors":null,"categories":null,"content":"Project Summary Vern Paxson developed the initial version of the Bro Network Security Monitor initial version in 1995 while at Lawrence Berkeley National Laboratory. The original software was called “Bro” as an “Orwellian reminder that monitoring comes hand in hand with the potential for privacy violations.” Bro changed its name to Zeek and has also been commercialized in a spinoff called Corelight.\nPaxson first deployed Zeek while at the Berkeley Labin 1996, and the USENIX Security Symposium published Paxson’s original paper on Zeek in 1998, and awarded it the Best Paper Award that year. The paper was awarded a “Test of Time Award” in 2022 for its lasting impact on the research community.\nThe Berkeley Lab’s work with Zeek/Bro has continued over the years including 100G capable network monitoring using Bro in 2015; applications of Zeek/Bro to the Science DMZ and Medical Science DMZ network design patterns; the commercial spinoff of Zeek/Bro into Corelight(previously Broala) by Paxson, Robin Sommer, and LBNL Scientific Division Director / ESnet Director, Greg Bell; and Berkeley Lab and ESnet personnels’ continued roles on the Zeek Leadership Team.\nThe canonical reference for Zeek/Bro is Paxson’s 1999 ``Bro: A System for Detecting Network Intruders in Real-Time.\u0026#39;\u0026#39;\nMore Information: Vern Paxson Zeek (open source) Corelight (commercial spinoff) additional history\nMore information is available on other Berkeley Lab research projects focusing on cybersecurity in general, as well as specifically on cybersecurity for research cyberinfrastructure and high-performance computing.\n","date":938649600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":938649600,"objectID":"b94cdcb4f6815815ca96cab12fa3bef0","permalink":"https://secpriv.lbl.gov/project/network-security-monitor/","publishdate":"1999-09-30T00:00:00Z","relpermalink":"/project/network-security-monitor/","section":"project","summary":"Vern Paxson developed the Bro/Zeek Network Security Monitor while at Lawrence Berkeley National Laboratory.","tags":["research cyberinfrastructure","secure systems","network"],"title":"Bro/Zeek Network Security Monitor","type":"project"},{"authors":null,"categories":null,"content":"","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"f26b5133c34eec1aa0a09390a36c2ade","permalink":"https://secpriv.lbl.gov/admin/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/admin/","section":"","summary":"","tags":null,"title":"","type":"page"},{"authors":null,"categories":null,"content":"google-site-verification: googlea3505071e5b054e3.html","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"4fc054b4a3fcaa34f6bfab96066274c4","permalink":"https://secpriv.lbl.gov/googlea3505071e5b054e3/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/googlea3505071e5b054e3/","section":"","summary":"google-site-verification: googlea3505071e5b054e3.html","tags":null,"title":"","type":"page"}]