A Golden-Free Unsupervised ML-Assisted Security Approach for Detection of IC Hardware Trojans

Hardware Trojans are deliberate malicious hardware modifications inserted in semiconductor Integrated Circuits (ICs) for the purpose of stealing or leaking sensitive information, as well as disrupting critical systems upon activation. Emerging hardware security research highlights the criticality of employing AI for effective detection within the semiconductor IC supply chain. The efficient detection of these malicious Trojan circuits is of utmost significance, as it holds paramount importance in cultivating trust within the semiconductor IC supply chain. However, prevailing detection methodologies, predominantly reliant on Side-Channel Analysis (SCA), often necessitate the utilization of golden chips for validation. This article heralds a new era in hardware Trojan detection, harnessing the prowess of unsupervised machine learning in conjunction with SCA to eliminate the need for golden data. 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. 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 paradigm in hardware Trojan detection, emphasizing its potential in enhancing the integrity of semiconductor IC supply chains.