Enhancing the Security of Cyber-Physical Systems through Blockchain and Machine Learning-Based Intrusion Detection Systems

Abstract

The rapid advancement in the integration of Cyber-Physical Systems (CPS) into critical infrastructure such as smart grids, industrial automation, and healthcare systems introduces unprecedented vulnerabilities to security threats and cyberattacks. Traditional security mechanisms are often insufficient to address the unique challenges posed by CPS, including real-time monitoring, dynamic adaptation, and scalability. This paper presents a hybrid approach to enhancing the security of CPS by integrating blockchain technology and machine learning-based Intrusion Detection Systems (IDS). Blockchain ensures data integrity, decentralization, and transparency, while machine learning algorithms enhance anomaly detection capabilities, enabling real-time identification of malicious activities. This study investigates the combination of these two technologies for creating a robust, scalable, and adaptive security architecture for CPS. Through simulations and performance analysis, the proposed system is evaluated in terms of accuracy, response time, and system resilience. Results indicate that the integrated blockchain and machine learning-based IDS outperform traditional security models in terms of detecting and mitigating attacks, demonstrating significant promise for CPS security.