A framework for hardware trojan detection based on contrastive learning

With the rapid development of the semiconductor industry, Hardware Trojans (HT) as a kind of malicious function that can be implanted at will in all processes of integrated circuit design, manufacturing, and deployment have become a great threat in the field of hardware security. Side-channel analysis is widely used in the detection of HT due to its high efficiency, non-contact nature, and accuracy. In this paper, we propose a framework for HT detection based on contrastive learning using power consumption information in unsupervised or weakly supervised scenarios. First, the framework augments the data, such as creatively using a one-dimensional discrete chaotic mapping to disturb the data to achieve data augmentation to improve the generalization capabilities of the model. Second, the model representation is learned by comparing the similarities and differences between samples, freeing it from the dependence on labels. Finally, the detection of HT is accomplished more efficiently by categorizing the side information during circuit operation through the backbone network. Experiments on data from nine different public HTs show that the proposed method exhibits better generalization capabilities using the same network model within a comparative learning framework. The model trained on the dataset of small Trojan T100 has a detection efficiency advantage of up to 44% in detecting large Trojans, while the model trained on the dataset of large Trojan T2100 has a detection efficiency advantage of up to 10% in detecting small Trojans. The results in data imbalanced and noisy environments also show that the contrastive learning framework in this paper can better fulfill the requirements of detecting unknown HT in unsupervised or weakly supervised scenarios.