Deep Learning-Based Hardware Trojan Detection via Dual-Path CNN and Markov Transition Field Representations

This paper introduces a dual-path deep learning framework specially designed for the efficient detection and classification of Hardware Trojans (HTs) through Side-Channel Analysis (SCA). Using Markov Transition Field (MTF) encoding and a reshaping strategy, the proposed method first converts side-channel time-series signals, including power traces, electromagnetic leaks, and timing data, into two different image-like formats. Detecting subtle Trojan activity requires these representations to capture intricate signal dynamics. Subsequently, each image is processed by a separate convolutional neural network (CNN) branch within the dual-path architecture, with each path optimized for extracting complementary features. To enhance classification performance, the outputs of both CNNs are fused via a dense neural network layer. The dual-path mechanism contributes to improved detection accuracy and robust feature extraction, while the overall architecture supports the classification of specific Trojan types. Evaluation using the publicly available AES Hardware Trojan dataset sourced from TrustHub and IEEE DataPort demonstrates that the proposed model benefits from CNN-based feature learning and outperforms several existing approaches.