Abstract

Context-aware computing applications depend on embedded hardware systems, utilizing sensors embedded in the hardware to gather real-time data and interact with specialized OS software (firmware) for autonomous processing and analysis of intelligent data. Securing embedded IC hardware systems and ensuring their trustworthiness requires an intelligent approach to effectively detect spontaneous hardware Trojans (HTs) insertions and modification attacks aiming to compromise the system's integrity, potentially leaking sensitive information or causing destruction. Implementing robust and advanced intrusion detection systems against supply chain hardware Trojan to countermeasure and continuous monitoring the behavior of these malicious hardware is essential to enable trust in context-aware computing applications. AI-enabled hardware side-channel analysis, involving power and timing assessments, assists in detection of anomalies that may signify potential Trojans. This paper propose intelligent AI approach utilizing unsupervised machine learning in conjunction with hardware side-channel analysis to eliminate the need for golden data samples and efficiently detect hardware Trojan detection. Employing unsupervised clustering, the methodology not only showcased a superior false positive rate but also demonstrated a comparable accuracy level when compared to supervised counterparts such as the K-Nearest Neighbors (KNN) classifier, Support Vector Machine (SVM), and Gaussian classifier-methods reliant on the availability of golden data for training. Notably, the proposed model exhibited an impressive accuracy rate of 93%, particularly excelling in pinpointing diminutive Trojans triggered by concise events, surpassing the capabilities of preceding techniques. In conclusion, this research advances a groundbreaking paradigm in hardware Trojan detection, accentuating its potential in bolstering the integrity of semiconductor IC supply chains.