AI-Driven Vulnerability Assessment and Mitigation Strategies for CyberPhysical Systems
Keywords:
Cyber-Physical Systems (CPS), Artificial Intelligence (AI), Vulnerability Analysis, Mitigation Techniques, Machine Learning, Anomaly Detection.Abstract
Cyber-Physical Systems (CPS) integrate computational elements with physical processes, creating complex, interconnected networks that control critical infrastructures such as healthcare, energy, transportation, and manufacturing. As these systems become more pervasive, they face an increasing number of security threats that can jeopardize both their functionality and safety. Traditional security measures are often inadequate to address the unique vulnerabilities of CPS, especially in the face of evolving cyber threats. This paper explores AI-driven approaches for vulnerability assessment and mitigation in CPS. We present a comprehensive framework that leverages machine learning (ML), deep learning (DL), and anomaly detection techniques to identify, analyze, and predict potential vulnerabilities in real-time. The framework employs AI algorithms to continuously monitor system behaviors, detect unusual patterns, and proactively predict potential attack vectors. Additionally, we propose mitigation strategies powered by AI that enable automated responses, reducing the time to recover from security breaches. By enhancing the security of CPS with intelligent, adaptive systems, this work aims to contribute to the resilience and safety of critical infrastructures in an increasingly connected world. The results demonstrate the potential of AI to not only detect vulnerabilities but also to orchestrate adaptive, self-healing mechanisms that significantly improve the robustness of CPS against cyber threats.