Comparative Analysis of Machine Learning and Deep Learning Algorithms for Early Detection of Hardware Vulnerabilities in Hardware Security Systems

Abstract

Early detection of hardware vulnerabilities is critical to ensuring the security and reliability of electronic systems. This study investigates and compares various machine learning and deep learning algorithms for their effectiveness in identifying hardware vulnerabilities during the early stages of the development lifecycle. The analysis focuses on algorithms commonly used in hardware security applications, including anomaly detection, classification, and pattern recognition, to detect issues such as hardware Trojans, side-channel attacks, and other security threats. Key performance metrics such as accuracy, detection speed, computational efficiency, and robustness against adversarial scenarios are evaluated. Furthermore, the study highlights the trade-offs between machine learning and deep learning approaches, considering their scalability and deployment feasibility in resource-constrained environments. The findings aim to provide insights into selecting the most suitable algorithm for early vulnerability detection, contributing to the advancement of secure hardware design and the mitigation of potential risks in critical systems.