Detection Method of Hardware Trojan Based on Attention Mechanism and Residual-Dense-Block under the Markov Transition Field

Since 2007, methods that utilize side-channel data to detect hardware Trojan (HT) problems have been widely studied. Machine learning methods are widely used for hardware Trojan detection, but with the development of integrated circuits (ICs), better results are usually obtained using deep learning methods. In this paper, we propose an architecture inspired by Residual-Block and Dense-Block and combine it with SE Attention Mechanism, which we named the Res-Dense-SE-Net network. By combining residual connectivity, dense connectivity, and attention mechanism, the Res-Dense-SE-Net network can enjoy the advantages of these three network architectures at the same time, which can improve the expressiveness and performance of the model. The Res-Dense-SE-Net network can capture the key features in the image better, and it can solve the problems of gradient vanishing and feature transfer efficiently, which can in turn improve the classification accuracy and the generalization ability of the model. Based on the publicly available AES series of hardware Trojans from TrustHub and the publicly available hardware Trojan-side channel data by Faezi et al., we evaluate the effectiveness of the method proposed in this paper. The experimental results show that when a single Trojan exists, the method proposed in this paper has a high accuracy rate; and when multiple types of hardware Trojans exist at the same time and need to be categorized, the categories of hardware Trojans can also be effectively identified, and the categorization accuracy is high compared with the existing deep learning methods.