Evaluation of Machine Learning Algorithms for Anomaly Detection in Industrial Networks

被引：17
|
作者：
Bernieri, Giuseppe [1 ]
Conti, Mauro [1 ]
Turrin, Federico [1 ]
机构：
[1] Univ Padua, Dept Math, Padua, Italy
来源：
2019 IEEE INTERNATIONAL SYMPOSIUM ON MEASUREMENTS & NETWORKING (M&N 2019) | 2019年
关键词：
Machine Learning; Anomaly Detection; Industrial Control System; Cyber-Physical System; Security;
D O I：
10.1109/iwmn.2019.8805036
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
The cyber-physical security of Industrial Control Systems (ICSs) represents an actual and worthwhile research topic. In this paper, we compare and evaluate different Machine Learning (ML) algorithms for anomaly detection in industrial control networks. We analyze supervised and unsupervised ML-based anomaly detection approaches using datasets extracted from the Secure Water Treatment (SWaT), a testbed developed to emulate a scaled-down real industrial plant. Our experiments show strengths and limitations of the two ML-based anomaly detection approaches for industrial networks.
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收藏
页数：6
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