Explainable AI for Process-Aware Attack Detection in Industrial Control Systems

被引：0

作者：

Kenmogne, Lea Astrid ^{[1
]}

Mocanu, Stephane ^{[1
]}

机构：

[1] Univ Grenoble Alpes, INRIA, CNRS, Grenoble INP,LIG, F-38000 Grenoble, France

来源：

2024 IEEE 10TH INTERNATIONAL CONFERENCE ON NETWORK SOFTWARIZATION, NETSOFT 2024 | 2024年

关键词：

Explainable Artificial Intelligence; Anomaly based detection; Industrial Control Systems; INTRUSION DETECTION;

D O I：

10.1109/NetSoft60951.2024.10588940

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Industrial Control System cybersecurity has become an important study area after the occurrence of several mediatic events in the 2010's (Stuxnet, BlackEnergy, Industroyer). Two common characteristics of these attacks are the fact that they were not violating the communication protocols being "stealth" for classical pattern-based detection methods and that they explicitly target the physical process. In this paper we study the performance and explainability of an artificial intelligence based detection system for the detection of such sophisticated attacks.

引用

页码：363 / 368

页数：6

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