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Open AccessArticle

Unsupervised Anomaly Detection for Network Data Streams in Industrial Control Systems

1
Key Laboratory of Trustworthy Distributed blueComputing and Service, Beijing University of Posts and Telecommunications, Beijing 100876, China
2
China Electric Power Research Institute, Haidian District, Beijing 100192, China
*
Authors to whom correspondence should be addressed.
Information 2020, 11(2), 105; https://doi.org/10.3390/info11020105 (registering DOI)
Received: 19 January 2019 / Revised: 12 February 2020 / Accepted: 13 February 2020 / Published: 15 February 2020
(This article belongs to the Special Issue Machine Learning for Cyber-Security)
The development and integration of information technology and industrial control networks have expanded the magnitude of new data; detecting anomalies or discovering other valid information from them is of vital importance to the stable operation of industrial control systems. This paper proposes an incremental unsupervised anomaly detection method that can quickly analyze and process large-scale real-time data. Our evaluation on the Secure Water Treatment dataset shows that the method is converging to its offline counterpart for infinitely growing data streams.
Keywords: anomaly detection; industrial control systems; data streams; incremental learning anomaly detection; industrial control systems; data streams; incremental learning
MDPI and ACS Style

Liu, L.; Hu, M.; Kang, C.; Li, X. Unsupervised Anomaly Detection for Network Data Streams in Industrial Control Systems. Information 2020, 11, 105.

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