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

Abstract

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, underscoring the importance of robust detection mechanisms. Emerging hardware security research highlight 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, often necessitate the utilization of golden chips for validation. This paper heralds a new era in Hardware Trojan detection, harnessing the prowess of unsupervised machine learning in conjunction with side-channel analysis to eliminate the need for golden data. Through FPGA-based experimentation involving Trojans of varying dimensions, the efficacy of this innovative approach was evaluated. Employing unsupervised clustering, the methodology effectively uncovered anomalies. The application of unsupervised learning techniques 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.