Unsupervised Anomaly Detection for Network Data Streams in Industrial Control Systems

被引：7

作者：

Liu, Limengwei ^{[1
]}

Hu, Modi ^{[1
]}

Kang, Chaoqun ^{[2
]}

Li, Xiaoyong ^{[1
]}

机构：

[1] Beijing Univ Posts & Telecommun, Key Lab Trustworthy Distributed Comp & Serv, Beijing 100876, Peoples R China

[2] China Elect Power Res Inst, Beijing 100192, Peoples R China

来源：

INFORMATION | 2020年 / 11卷 / 02期

关键词：

anomaly detection; industrial control systems; data streams; incremental learning;

D O I：

10.3390/info11020105

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页数：14

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