A Survey on Machine-Learning Based Security Design for Cyber-Physical Systems
A cyber-physical system (CPS) is the integration of a physical system into the real world and control applications in a computing system, interacting through a communications network. Network technology connecting physical systems and computing systems enables the simultaneous control of many physic...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/12/5458 |
| _version_ | 1797530389047148544 |
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| author | Sangjun Kim Kyung-Joon Park |
| author_facet | Sangjun Kim Kyung-Joon Park |
| author_sort | Sangjun Kim |
| collection | DOAJ |
| description | A cyber-physical system (CPS) is the integration of a physical system into the real world and control applications in a computing system, interacting through a communications network. Network technology connecting physical systems and computing systems enables the simultaneous control of many physical systems and provides intelligent applications for them. However, enhancing connectivity leads to extended attack vectors in which attackers can trespass on the network and launch cyber-physical attacks, remotely disrupting the CPS. Therefore, extensive studies into cyber-physical security are being conducted in various domains, such as physical, network, and computing systems. Moreover, large-scale and complex CPSs make it difficult to analyze and detect cyber-physical attacks, and thus, machine learning (ML) techniques have recently been adopted for cyber-physical security. In this survey, we provide an extensive review of the threats and ML-based security designs for CPSs. First, we present a CPS structure that classifies the functions of the CPS into three layers: the physical system, the network, and software applications. Then, we discuss the taxonomy of cyber-physical attacks on each layer, and in particular, we analyze attacks based on the dynamics of the physical system. We review existing studies on detecting cyber-physical attacks with various ML techniques from the perspectives of the physical system, the network, and the computing system. Furthermore, we discuss future research directions for ML-based cyber-physical security research in the context of real-time constraints, resiliency, and dataset generation to learn about the possible attacks. |
| first_indexed | 2024-03-10T10:28:14Z |
| format | Article |
| id | doaj.art-57f9214b211941bb82ac59641ccc8d11 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T10:28:14Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-57f9214b211941bb82ac59641ccc8d112023-11-21T23:50:50ZengMDPI AGApplied Sciences2076-34172021-06-011112545810.3390/app11125458A Survey on Machine-Learning Based Security Design for Cyber-Physical SystemsSangjun Kim0Kyung-Joon Park1Department of Information and Communication Engineering, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu 42988, KoreaDepartment of Information and Communication Engineering, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu 42988, KoreaA cyber-physical system (CPS) is the integration of a physical system into the real world and control applications in a computing system, interacting through a communications network. Network technology connecting physical systems and computing systems enables the simultaneous control of many physical systems and provides intelligent applications for them. However, enhancing connectivity leads to extended attack vectors in which attackers can trespass on the network and launch cyber-physical attacks, remotely disrupting the CPS. Therefore, extensive studies into cyber-physical security are being conducted in various domains, such as physical, network, and computing systems. Moreover, large-scale and complex CPSs make it difficult to analyze and detect cyber-physical attacks, and thus, machine learning (ML) techniques have recently been adopted for cyber-physical security. In this survey, we provide an extensive review of the threats and ML-based security designs for CPSs. First, we present a CPS structure that classifies the functions of the CPS into three layers: the physical system, the network, and software applications. Then, we discuss the taxonomy of cyber-physical attacks on each layer, and in particular, we analyze attacks based on the dynamics of the physical system. We review existing studies on detecting cyber-physical attacks with various ML techniques from the perspectives of the physical system, the network, and the computing system. Furthermore, we discuss future research directions for ML-based cyber-physical security research in the context of real-time constraints, resiliency, and dataset generation to learn about the possible attacks.https://www.mdpi.com/2076-3417/11/12/5458cyber-physical systemshierarchical CPS structureCPS securitycyber-physical attacksmachine learning-based detectionlearning-enabled CPS |
| spellingShingle | Sangjun Kim Kyung-Joon Park A Survey on Machine-Learning Based Security Design for Cyber-Physical Systems Applied Sciences cyber-physical systems hierarchical CPS structure CPS security cyber-physical attacks machine learning-based detection learning-enabled CPS |
| title | A Survey on Machine-Learning Based Security Design for Cyber-Physical Systems |
| title_full | A Survey on Machine-Learning Based Security Design for Cyber-Physical Systems |
| title_fullStr | A Survey on Machine-Learning Based Security Design for Cyber-Physical Systems |
| title_full_unstemmed | A Survey on Machine-Learning Based Security Design for Cyber-Physical Systems |
| title_short | A Survey on Machine-Learning Based Security Design for Cyber-Physical Systems |
| title_sort | survey on machine learning based security design for cyber physical systems |
| topic | cyber-physical systems hierarchical CPS structure CPS security cyber-physical attacks machine learning-based detection learning-enabled CPS |
| url | https://www.mdpi.com/2076-3417/11/12/5458 |
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