Enhancing Hardware Trojan Detection: A Dual-Path CNN Approach to Side-Channel Analysis

Study presents a dual-path deep learning architecture designed to effectively detect and identify HT using Side-Channel Analysis (SCA). Initially, the method converts side-channel time series data-including power consumption, electromagnetic emissions, and timing information-into two separate image-like formats by applying Markov Transition Field (MTF) and reshaping techniques. This transformation effectively preserves the complex features of the data, which are crucial for detecting subtle Trojan manipulations. Subsequently, the resulting images are passed through two distinct convolutional neural network (CNN) pathways within our dual-path architecture, each optimized to enhance feature extraction. The features obtained from each pathway are then merged and input into a dense neural network layer, combining various signal characteristics to achieve robust and accurate classification. This dual-path strategy build our proposed method, enabling the simultaneous classification of both legitimate and Trojan-infected data, as well as the identification of the specific type of Trojan attack. The model has undergone extensive testing using the publicly accessible Advanced Encryption Standard (AES) dataset for HTs sourced from TrustHub and IEEE Dataport. Our method not only improves classification accuracy by using the combined strengths of CNNs but also shows marked improvements over current techniques.