Securing Hardware with AI: Intrusion Detection, Threat Mitigation, and Trust Assurance - Shankar, Vasuki; Muralidhar, Nanditha (International Journal of Computer Sciences and Engineering, 2025)
As cyber threats targeting modern hardware become increasingly sophisticated, traditional security mechanisms such as encryption and isolation are no longer sufficient. This paper explores the adaptation of Artificial Intelligence in the realm of hardware security to enhance anomaly detection, intrusion detection, and real-time threat mitigation. Through case studies of Intel SGX, AMD SEV, and Microsoft Pluton, the study demonstrates how AI-driven mechanisms can enhance protection against side-channel attacks, firmware compromises, and hardware Trojans. While AI significantly improves threat resilience, challenges like computational overhead, adversarial attacks, and a dearth of explainability hinder its widespread adoption. We conclude the paper by identifying the need for lightweight AI models and AI-Quantum integration as future directions for building robust, next-generation hardware security frameworks.