Detection of Cyber Threats to Cyber-Physical Systems Using Artificial Intelligence

Document Type : Research Article

Author

استادیار دانشکده علوم و فنون فارابی

10.22034/amn.2024.376

Abstract

Cyber-Physical Systems (CPS), by integrating physical and cyber components, have revolutionized various industries. However, with their increasing integration into complex networks, these systems face a growing array of sophisticated cyber threats. This article investigates cyber threats to CPS and the role of artificial intelligence (AI) in their detection and mitigation. The study first analyzes various cyber threats targeting CPS, including malware (e.g., ransomware and trojans), distributed denial-of-service (DDoS) attacks, zero-day attacks, and insider threats. Subsequently, AI-based solutions to counter these threats are proposed. The research also addresses practical challenges in implementing these solutions, such as the need for reliable training data, real-time processing limitations, and resource optimization. The study population consists of six domain experts, with data analyzed through interviews and MaxQDA software. 
The results indicate that optimizing and evaluating equipment, combined with AI, enables effective detection of cyber threats to CPS. By presenting practical examples and reviewing evaluation metrics (e.g., detection rate and false positive rate), the article demonstrates that integrating various AI methods can significantly enhance the security of cyber-physical systems. 

Keywords

References

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Volume 23, Issue 87
November 2024
Pages 111-148

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