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NSIBF

Time Series Anomaly Detection for Cyber-physical Systems via Neural System Identification and Bayesian Filtering

Cheng Feng and Pengwei Tian. 2021. Time Series Anomaly Detection for Cyber-physical Systems via Neural System Identification and Bayesian Filtering. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD ’21), August 14–18, 2021, Virtual Event, Singapore. ACM, New York, NY, USA, 10 pages. https://doi.org/10.1145/3447548.3467137

Getting Started

Install dependencies (with python 3.6)

pip install -r requirements.txt

Run the qualitative experiment

cd experiments
python qualitative_experiment.py

Run the PUMP experiment

cd experiments
python PUMP_experiement.py

Run the WADI experiment

cd experiments
python WADI_experiement.py

Run the SWAT experiment

cd experiments
python SWAT_experiement.py

nsibf's Projects

deep-kernel-transfer icon deep-kernel-transfer

Official pytorch implementation of the paper "Deep Kernel Transfer in Gaussian Processes for Few-shot Learning"

pars-exad icon pars-exad

PARs: Predicate-based Association rules for model-agnostic anomaly explanation

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