Version 1
: Received: 8 July 2023 / Approved: 8 July 2023 / Online: 10 July 2023 (10:12:36 CEST)
How to cite:
Bandi, A. A Taxonomy of AI Techniques for Security and Privacy in Cyber-Physical Systems. Preprints2023, 2023070564. https://doi.org/10.20944/preprints202307.0564.v1
Bandi, A. A Taxonomy of AI Techniques for Security and Privacy in Cyber-Physical Systems. Preprints 2023, 2023070564. https://doi.org/10.20944/preprints202307.0564.v1
Bandi, A. A Taxonomy of AI Techniques for Security and Privacy in Cyber-Physical Systems. Preprints2023, 2023070564. https://doi.org/10.20944/preprints202307.0564.v1
APA Style
Bandi, A. (2023). A Taxonomy of AI Techniques for Security and Privacy in Cyber-Physical Systems. Preprints. https://doi.org/10.20944/preprints202307.0564.v1
Chicago/Turabian Style
Bandi, A. 2023 "A Taxonomy of AI Techniques for Security and Privacy in Cyber-Physical Systems" Preprints. https://doi.org/10.20944/preprints202307.0564.v1
Abstract
Background:As cyber-physical systems (CPS) continue to grow, it is progressively crucial to address the challenges of data protection using artificial intelligence (AI). Objective:The goal of this research is to provide an up-to-date overview of the security and privacy issues of CPS that incorporate AI techniques. Method:To achieve this, the author conducted a systematic literature review, focusing on 35 relevant articles. Results:The data collected from these studies was then categorized into three main areas: 1) different security and privacy issues, 2) application areas and vulnerabilities, and 3) the AI techniques used to address security and privacy concerns. The literature review highlights that intrusion detection and cyberattacks are the most commonly studied areas in CPS, while Machine Learnning (ML)-based attacks and vessel trajectory are less explored. The review identifies various CPS applications such as water treatment, energy, healthcare, and transportation that address security and privacy concerns. However, a relatively small proportion of studies focused on the manufacturing domain. The review also notes that while supervised machine learning algorithms under the classification category are commonly used to address data protection issues, there are comparatively fewer studies that have implemented automation processes using robots and deep learning. Limitations:The articles related to blockchain-based research were not included in this review to focus solely on AI techniques. Conclusion:The results of this analysis indicate that there is a significant need for innovative AI/ML techniques to protect intelligent systems and networks from ML-based security threats.
Keywords
Internet of Things (IoT); Beyond 5G; 6G communications; Autonomous systems; Control systems; Real-time systems; Industry 4.0; Aversrial Machine Learning
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
