Encoded-IF

Anomaly Detection Enhancement Method Using Interpretable Unsupervised Machine Learning in Industrial Information Systems at Multivariate Time Series Environment

https://doi.org/10.5302/J.ICROS.2024.23.0200

Journal of Institute of Control, Robotics and Systems (2024) 30(3)
ISSN:1976-5622
eISSN:2233-4335

Usage

SWaT data processing

1. Run unzip ./SWaT/data/SWaT.zip to unzip the datasets
   or
2. Run cd ./SWaT/utils
   Run python gdrivedl.py https://drive.google.com/open?id=1rVJ5ry5GG-ZZi5yI4x9lICB8VhErXwCw ./SWaT
   Run python gdrivedl.py https://drive.google.com/open?id=1iDYc0OEmidN712fquOBRFjln90SbpaE7 ./SWaT
   Run mkdir -p ./../data
   Run mv ./SWaT ./../data/SWaT

Traing & Evaluation

SMD datasets

SMD
machine-1-1, machine-1-2, machine-1-3, machine-1-4, machine-1-5, machine-1-6, machine-1-7, machine-1-8,
machine-2-1, machine-2-2, machine-2-3, machine-2-4, machine-2-5, machine-2-6, machine-2-7, machine-2-8, machine-2-9,
machine-3-1, machine-3-2, machine-3-3, machine-3-4, machine-3-5, machine-3-6, machine-3-7, machine-3-8, machine-3-9,
machine-3-10, machine-3-11

to run of SMAP, MSL and SMD datasets

1. Run main.ipynb by jupyter
   or
2. Run main.py by python

# available models : IF, USAD, Encoded-IF
python main.py --dataset SMAP 
python main.py --dataset MSL 
python main.py --dataset SMD

# available sub-SMD datasets
# python main.py --dataset machine-{a}-{b} --model Encoded-IF --max_epoch 0
# a = {1, 2, 3}
# b = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}

to run of SWaT datasets

1. Run /SWaT/IsolationForest.ipynb by jupyter
2. Run /SWaT/AutoEncoder.ipynb by jupyter
3. Run /SWaT/USAD.ipynb by jupyter
4. Run /SWaT/Encoded-IF.ipynb by jupyter

Data description

Dataset	Train	Test	Dimensions
SWaT	496,800	449,919	51
SMAP	135,183	427,617	25
MSL	58,317	73,729	55
SMD	708,405	708,420	28*28