Disclaimer. The source code in this repository has been made publicly available for transparency and as contribution for the scientific community. The source code reflects in most parts the state in which the results for the referenced publications were obtained. The source code has mostly been left as is.
This repository contains the source code for the publication:
P. Krämer et al., “ProFi: Scalable and efficient website fingerprinting,” IEEE TNSM, pp. 1–14, Under Submission.
The code consists of three separate projects:
- tls-gatherer
- tls-fingerprint
- prototype
The tls-gatherer and the tls-fingerprint container contain docker files that
encapsulate the development environment.
The tls-gatherer project implements the measurement pipeline that we used to collect our data. The project relies on a Kubernetes cluster and a backend-database, i.e., does not run standalone. A dump of the database is included in the provided data.
The TLS Fingerprint repository contains the code to train and evaluate fingeprints.
A good starting-point is the scripts folder. This folder contains Python scripts
that train and evaluate models, convert data, etc.
Note that the subfolders baum-welch, argparse, and implementation/ctypes
contain C code that must be build manually.
To evaluate funcionality, build a docker image and mount the tls-fingerprint folder
to /opt/project in the container. The data must be mounted separately (see below).
The prototype implements the entwork functions. The start.py scripts provides
an interface to run the experiments.
Note that the prototype depends on OpenNetVM and MoonGen. This repository contains only the implemented NFs.