Hardware Trojan Detection employing Machine Learning, Physical Unclonable Functions and Side Channel Analysis

The globalization in the semiconductor domain, primarily to reduce the cost of fabrication, has engendered the integrated circuits (ICs) to be compromised. These semiconductor chips are vulnerable to various attacks such as - hardware Trojan (HT) intrusion. These stealthy HTs can compromise the integrity and functionality of the devices by leaking critical information, compromising the performance, or causing a complete device shutdown. Detecting HTs before deploying ICs in critical infrastructure, is essential to safeguard against these threats. Significant research is done in this area; however, a fool proof technique of HT detection is still required. With this background, this paper presents a technique for the efficient detection of hardware Trojans within ICs, leveraging both power side channel analysis and machine learning (ML) algorithms. The framework proposed in this paper measures the variations in the power consumption of a ring oscillator physical unclonable function (ROPUF) and trains the collected dataset to execute the ML algorithms. The ROPUF is programmed on an Artix-7 FPGA mounted on a Nexys 4 Digilent board, implemented with a Trojan. Our approach uses k-nearest neighbors (knn), logistic regression (LR), and decision tree (DT) as the three ML algorithms to detect hardware Trojan, assessed on four parameters - accuracy, precision, recall, and F1 score. We also present confusion matrix of each executed algorithm. The results demonstrate that LR gives the best precision of 100%, while best accuracy and recall is presented by DT at 98%.