ML-Based Hardware Trojan Detection in AI Accelerators via Power Side-Channel Analysis
AbdelSalam Baltagi, Yehya A. Nasser, Amer Baghdadi · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · 2025
To accelerate development and system integration, many companies opt to outsource the design of complex AI accelerators to third-party IP vendors rather than developing them inhouse. This practice raises security concerns, particularly the risk of hardware Trojan (HT) attacks [1]. Traditional testing methods are impractical for detecting HTs in modern AI/ML accelerators due to their hardware complexity and inability to provide insights into the inserted Trojans. In this work, we propose a methodology to detect the presence of HTs in different AI/ML accelerators and identify key Trojan characteristics using power side-channel analysis (PSCA). We present a testbed for accurate power consumption measurement, enabling the collection of real ML inference power traces. We inserted multiple HTs in several AI/ML accelerators and prepared various dataset configurations to analyze multiple HT scenarios. Then, we proposed a novel method for preprocessing PSCA data that consists of segmenting the power traces and extracting statistical features from time and frequency domains. Our proposed technique, equipped with an ML-based HT detection and identification method, achieves up to 99% accuracy.