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A survey on security control and attack detection for industrial cyber-physical systems
Open accessAbstract
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.
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Cites
Vulnerability Assessment of AC State Estimation With Respect to False Data Injection Cyber-Attacks
Hug, G., & Giampapa, J. A. (2012) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2012.2195338 · Cited by 595
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Deep reinforcement learning for cyber security
Nguyen, T. T., & Reddi, V. J. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3121870 · Cited by 620
Cybersecurity of industrial cyber-physical systems: A review
Kayan, H., Nunes, M., Rana, O., Burnap, P., & Perera, C. (2022) · ACM Computing Surveys · doi:10.1145/3510410 · Cited by 261
Reinforcement learning for iot security: A comprehensive survey
Uprety, A., & Rawat, D. B. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3040957 · Cited by 214
State of the art of cyber-physical systems security: An automatic control perspective
Zacchia Lun, Y., D’Innocenzo, A., Smarra, F., Malavolta, I., & Di Benedetto, M. D. (2019) · Journal of Systems and Software · doi:10.1016/j.jss.2018.12.006 · Cited by 211
Djenna, A., Harous, S., & Saidouni, D. E. (2021) · Applied Sciences · doi:10.3390/app11104580 · Cited by 199
Metadata
- Authors
- Ding, D., Han, Q.-L., Xiang, Y., Ge, X., & Zhang, X.-M.
- Venue
- Neurocomputing
- Publisher
- Elsevier BV
- Published
- 2018-01
- Volume
- 275
- Pages
- 1674-1683
- ISSN
- 0925-2312 (ISSN-L)
- OpenAlex
- W2762155482
- Cited by
- 944
- References
- 1
- FWCI
- 53.03
- Type
- journal article
- Language
- en
- Open access
- green
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- Ding, D., Han, Q.-L., Xiang, Y., Ge, X., & Zhang, X.-M. (2018). A survey on security control and attack detection for industrial cyber-physical systems. Neurocomputing. https://doi.org/10.1016/j.neucom.2017.10.009
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