Robust hardware Trojan detection leveraging dual-domain features and stacked ensemble learning

Abstract

Cyber-physical systems rely on integrated circuits (ICs), making them vulnerable to hardware Trojans that can remain dormant until triggered, causing functional disruption or information leakage. Detecting these stealthy attacks is challenging because they introduce only subtle changes in circuit behavior. We present a golden-chip-free hardware Trojan detection framework that combines time-domain and frequency-domain features extracted from side-channel power traces. The framework evaluates six artificial intelligence models, including random forest, gradient boosting, naive Bayes, deep neural network, long short-term memory, and graph neural network, and integrates them using a stacked ensemble classifier. Evaluation on the AES-Trojan benchmark demonstrates that the proposed ensemble consistently outperforms the individual baseline models, achieving a macro-averaged ROC-AUC of 0.987. The results show that combining dual-domain feature extraction with stacked ensemble learning enables accurate and robust detection of hardware Trojans directly from side-channel emissions without requiring a trusted reference IC.