A dual-stage deep learning approach for robust detection and identification of hardware trojans using monte-carlo dropout

Abstract Hardware Trojans (HTs) pose a significant threat to the integrity and security of integrated circuits, particularly in critical systems where stealthy hardware modifications can lead to catastrophic consequences. Detecting such Trojans through side-channel analysis (SCA) remains a major challenge due to subtle signal variations and environmental noise. This paper presents a dual-path deep learning framework for the detection and identification of HTs via side-channel analysis. The proposed approach transforms time-series side-channel data- including power consumption, electromagnetic emissions, and timing information-into two distinct image-based representations using Markov Transition Fields (MTF) and a reshaping technique. These transformed representations feed into a two-stage architecture: an Attack Detector, which determines the presence of an HT, and an Attack Identifier, which classifies the specific type of detected HT. To enhance reliability, Monte Carlo Dropout (MCD) is integrated for uncertainty estimation, enabling the framework to flag low-confidence detections and support more robust decision-making. The proposed method is evaluated on publicly available AES hardware Trojan datasets from TrustHub and IEEE Dataport, demonstrating superior accuracy over existing approaches, particularly for complex HT variants with subtle attack signatures. Furthermore, the robustness of the proposed method has been assessed by introducing noise into the validation dataset to simulate real-world operational perturbations such as process variation, aging, and voltage level variations. The results demonstrate the effectiveness and applicability of the dual-path deep learning framework for hardware Trojan detection.