Attack Detection Method based on Bayesian Hypothesis Testing Principle in CPS

被引:5
作者
Han, Ke [1 ]
Duan, Youyan [1 ]
Jin, Rui [1 ]
Ma, Zhicheng [1 ]
Wang, Huihui [2 ]
Wu, Wendou [3 ,4 ]
Wang, Baijuan [3 ,4 ]
Cai, Xiaobo [3 ,4 ]
机构
[1] Kunming Met Coll, Sch Elect & Mech Engn, Kunming 650033, Yunnan, Peoples R China
[2] Jacksonville Univ, Dept Engn, Jacksonville, FL 32211 USA
[3] Yunnan Agr Univ, Coll Big Data, Kunming 650201, Yunnan, Peoples R China
[4] Yunnan Organ Tea Ind Intelligent Engn Res Ctr, Kunming 650201, Yunnan, Peoples R China
来源
2020 INTERNATIONAL CONFERENCE ON IDENTIFICATION, INFORMATION AND KNOWLEDGE IN THE INTERNET OF THINGS (IIKI2020) | 2021年 / 187卷
关键词
prior probability distribution; information physics system; information security; attack detection; Bayesian hypothesis test; CYBER-PHYSICAL SYSTEMS; SCHEME;
D O I
10.1016/j.procs.2021.04.086
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Cyber physical system (CPS) introduces the concepts and methods of communication networks into traditional industrial processes, and realizes the control of physical processes by information flow, which is a key issue in national defense, military and industrial production. Many application problems provide solutions. However, this also makes the original pure industrial system environment face more security risks. Considering the security of the CPS control layer, based on the principle of Bayesian hypothesis testing, a detection method for the tampering of the measurement data of the control layer is proposed. This method uses the prior knowledge of the parameters to make the model still usable under the condition of a small sample amount of data. At the same time, the judgment conclusions made can accurately give the probability value of the attack behavior, which can more intuitively explain the possibility of the current statement. Compared with the previous traditional hypothesis testing method, this method is in line with the background of the CPS attack, so it has practical advantages. (C) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/lincenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the International Conference on Identification, Information and Knowledge in the internet of Things, 2020.
引用
收藏
页码:474 / 480
页数:7
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