Machine Learning

Anomaly Detection for a Water Treatment System Using Unsupervised Machine Learning

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  • arXiv
    5 pts

    Anomaly Detection for a Water Treatment System Using Unsupervised Machine Learning

    In this paper, we propose and evaluate the application of unsupervised machine learning to anomaly detection for a Cyber-Physical System (CPS). We compare two methods: Deep Neural Networks (DNN) adapted to time series data generated by a CPS, and one-class Support Vector Machines (SVM). These methods are evaluated against data from the Secure Water Treatment (SWaT) testbed, a scaled-down but fully operational raw water purification plant. For both methods, we first train detectors using a log generated by SWaT operating under normal conditions. Then, we evaluate the performance of both methods using a log generated by SWaT operating under 36 different attack scenarios. We find that our DNN generates fewer false positives than our one-class SVM while our SVM detects slightly more anomalies. Overall, our DNN has a slightly better F measure than our SVM. We discuss the characteristics of the DNN and one-class SVM used in this experiment, and compare the advantages and disadvantages of the two methods.

    Anomaly Detection for a Water Treatment System Using Unsupervised Machine Learning
    by Jun Inoue, Yoriyuki Yamagata, Yuqi Chen, Christopher M. Poskitt, Jun Sun
    https://arxiv.org/pdf/1709.05342v2.pdf

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