A Taxonomy of AI Techniques for Security and Privacy in Cyber–Physical Systems





Internet of Things (IoT), autonomous systems, control systems, real-time systems, Industry 4.0, adversarial machine learning, risk mitigation


This research paper addresses the concerns related to security and privacy in cyber–physical systems (CPS) and explores the role of artificial intelligence (AI) in addressing these concerns. This paper presents a comprehensive classification of various security and privacy threats in CPS, providing an organized overview of potential risks, economic loss, and enabling effective risk assessment. This paper highlights how AI can help address the security and privacy concerns in CPS by presenting a detailed flow chart that illustrates the stepby-step process of using AI and machine learning (ML) techniques to detect security and privacy issues. This integrated approach serves as a guide for designing ML-based secure CPS, enabling proactive defense mechanisms and improving incident response and recovery. Furthermore, the research explores the various AI techniques that can be employed to address security and privacy concerns in CPS. A taxonomy of ML techniques specifically relevant to security and privacy issues is provided, offering insights into the potential applications of these techniques. In conclusion, this research emphasizes the significance of addressing security and privacy concerns in CPS and highlights the role of AI in tackling these challenges.


Received: 23 August 2023 | Revised: 18 December 2023 | Accepted: 11 January 2024


Conflicts of Interest
The author declares that he has no conflicts of interest to this work.


Data Availability Statement
Data sharing is not applicable to this article as no new data were created or analyzed in this study.


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How to Cite

Bandi, A. (2024). A Taxonomy of AI Techniques for Security and Privacy in Cyber–Physical Systems. Journal of Computational and Cognitive Engineering, 3(2), 98–111. https://doi.org/10.47852/bonviewJCCE42021539