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 method...

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Bibliographic Details
Main Authors: INOUE, Jun, YAMAGATA, Yoriyuki, CHEN, Yuqi, POSKITT, Christopher M., SUN, Jun
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2017
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Online Access:https://ink.library.smu.edu.sg/sis_research/4704
https://ink.library.smu.edu.sg/context/sis_research/article/5707/viewcontent/Anomaly_detection_water_treatment_ICDMW17_av.pdf
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Institution: Singapore Management University
Language: English