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  5. A survey on security control and attack detection for industrial cyber-physical systems

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Article
English
2017

A survey on security control and attack detection for industrial cyber-physical systems

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English
2017
Neurocomputing
Vol 275
DOI: 10.1016/j.neucom.2017.10.009

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Qinglong Qinglong Han
Qinglong Qinglong Han

Swinburne University Of Technology

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Derui Ding
Qinglong Qinglong Han
Yang Xiang
+2 more

Abstract

Cyber-physical systems (CPSs), which are an integration of computation, networking, and physical processes, play an increasingly important role in critical infrastructure, government and everyday life. Due to physical constraints, embedded computers and networks may give rise to some additional security vulnerabilities, which results in losses of enormous economy benefits or disorder of social life. As a result, it is of significant to properly investigate the security issue of CPSs to ensure that such systems are operating in a safe manner. This paper, from a control theory perspective, presents an overview of recent advances on security control and attack detection of industrial CPSs. First, the typical system modeling on CPSs is summarized to cater for the requirement of the performance analysis. Then three typical types of cyber-attacks, i.e. denial-of-service attacks, replay attacks, and deception attacks, are disclosed from an engineering perspective. Moreover, robustness, security and resilience as well as stability are discussed to govern the capability of weakening various attacks. The development on attack detection for industrial CPSs is reviewed according to the categories on detection approaches. Furthermore, the security control and state estimation are discussed in detail. Finally, some challenge issues are raised for the future research.

How to cite this publication

Derui Ding, Qinglong Qinglong Han, Yang Xiang, Xiaohua Ge, Xian‐Ming Zhang (2017). A survey on security control and attack detection for industrial cyber-physical systems. Neurocomputing, 275, pp. 1674-1683, DOI: 10.1016/j.neucom.2017.10.009.

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Publication Details

Type

Article

Year

2017

Authors

5

Datasets

0

Total Files

0

Language

English

Journal

Neurocomputing

DOI

10.1016/j.neucom.2017.10.009

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