Enhancing Access Control, Authorization, and Accountability in Cyber-Physical Systems Using Machine Learning

Cyber-Physical Systems (CPS) integrate networking, computing, and physical processes, forming the backbone of critical industries such as healthcare, energy, and transportation. The increasing complexity and interconnection of CPS have led to significant compliance and security challenges. This research introduces a novel framework that leverages Machine Learning (ML) techniques to enhance access control, authorization, and accountability within CPS environments. By combining these techniques with traditional access control methods, the framework addresses the unique demands of CPS, including scalability and adaptability to dynamic conditions. A key innovation lies in applying ensemble methods like Random Forest, AdaBoost, and Gradient Boosting, which outperform individual models by mitigating overfitting and improving generalizability. The framework also incorporates sophisticated feature engineering and regularization strategies tailored to CPS, ensuring robust and efficient security solutions. Through rigorous data preprocessing, relationship analysis, and model validation, this study demonstrates how machine learning can significantly advance the security posture of CPS, offering a scalable and effective approach tailored to the specific needs of these critical systems.