Statistical Approach to Detection of Attacks for Stochastic Cyber-Physical Systems

被引：2

作者：

Marelli, Damian ^{[1
,2
]}

Sui, Tianju ^{[3
]}

Fu, Minyue ^{[1
,4
]}

机构：

[1] Guandong Univ Technol, Sch Automat, Guangzhou, Guangdong, Peoples R China

[2] Natl Sci & Tech Res Council, French Argentinean Int Ctr Informat & Syst Sci, Buenos Aires, DF, Argentina

[3] Dalian Univ Technol, Dept Control Sci & Engn, Dalian, Peoples R China

[4] Univ Newcastle, Sch Elect Engn & Comp Sci, Callaghan, NSW, Australia

来源：

IFAC PAPERSONLINE | 2018年 / 51卷 / 25期

关键词：

System security; linear systems; cyber-physical systems; integrity attacks; attack detection; statistical hypothesis testing;

D O I：

10.1016/j.ifacol.2018.11.101

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

We study the problem of detecting an attack on a stochastic cyber-physical system. We start by proposing a detection criterion based on checking the statistics of the Kalman prediction error. To show the importance of the proposed criterion, we study certain attacking schemes which can be effectively detected by this criterion, but not by checking simpler conditions based on commonly used statistics. We then use the proposed criterion to derive a detection algorithm. This algorithm is based on a statistical confidence test, giving a confidence level for detecting an attack. We present simulation results to illustrate the performance of this algorithm. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

引用

页码：178 / 183

页数：6

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