A review on security analysis of cyber physical systems using Machine learning

被引：29

作者：

Ahmed Jamal A. ^{[1
]}

Mustafa Majid A.-A. ^{[1
]}

Konev A. ^{[1
]}

Kosachenko T. ^{[1
]}

Shelupanov A. ^{[1
]}

机构：

[1] Department of Complex Information Security of Computer Systems, Faculty of Security, Tomsk State University of Control Systems and Radioelectronics, Tomsk

来源：

Materials Today: Proceedings | 2023年 / 80卷

关键词：

Cyber security; Cyber threat intelligence; Intrusion detection; Machine learning;

D O I：

10.1016/j.matpr.2021.06.320

中图分类号：

学科分类号：

摘要：

The concept of Cyber Physical System (CPS) is widely used in different industries across the globe. In fact, it is the holistic approach towards dealing with cyber space and physical environments that do have inter-dependencies. In the existing systems, there was a separate approach for security of the two worlds (cyber and physical). However, it does not provide necessary security when security is employed independently. The research in this paper identifies the need for integrated security for CPS. Besides it throws light into different security challenges associated with CPS and the countermeasures that existed based on machine learning and deep learning techniques that come under Artificial Intelligence (AI) and data science. From the review of literature, it is understood that data science perspective is suitable for protecting CPS with required adaptive strategy. This paper provides several useful insights related to security analysis of CPS using machine learning. It paves way for further investigation and realize a comprehensive security framework to protect CPS from internal and external cyber-attacks. © 2021

引用

页码：2302 / 2306

页数：4

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