HTnet: Transfer Learning for Golden Chip-Free Hardware Trojan Detection
Sina Faezi, Rozhin Yasaei, Mohammad Abdullah Al Faruque
Abstract
Design and fabrication outsourcing has made integrated circuits (IC) vulnerable to malicious modifications by third parties known as hardware Trojans (HT). Over the last decade, the use of side-channel measurements for detecting the malicious manipulation of the ICs has been extensively studied. However, the suggested approaches often suffer from three major limitations: 1) reliance on a trusted identical chip (i.e. golden chip), 2) untraceable footprints of subtle hardware Trojans which remain inactive during the testing phase, and 3) the need to identify the best discriminative features that can be used for separating side-channel signals coming from HT-free and HT-infected circuits. To overcome these shortcomings, we propose a novel neural network design (i.e. HTNet) and a feature extractor training methodology that can be used for HT detection in run time. We create a library of known hardware Trojans and collect electromagnetic and power side-channel signals for each case and train HTnet to learn the best discriminative features based on this library. Then, in the test time we fine tune HTnet to learn the behavior of the particular chip under test. We use HTnet followed by an anomaly detection mechanism in run-time to monitor the chip behavior and report malicious activities in the side-channel signals. We evaluate our methodology using TrustHub [15] benchmarks and show that HTnet can extract a robust set of features that can be used for HT-detection purpose.