The chip manufacturing of integrated circuits requires the participation of multiple parties, which greatly increases the possibility of hardware Trojan insertion and poses a significant threat to the entire hardware device landing; however, traditional hardware Trojan detection methods require gold chips, so the detection cost is relatively high. The attention mechanism can extract data with more adequate features, which can enhance the expressiveness of the network. This paper combines an attention module with a multilayer perceptron and convolutional neural network for hardware Trojan detection based on side-channel information, and evaluates the detection results by implementing specific experiments. The results show that the proposed method significantly outperforms machine learning classification methods and network-related methods, such as SVM and KNN, in terms of accuracy, precision, recall, and F1 value. In addition, the proposed method is effective in detecting data containing one or multiple hardware Trojans, and shows high sensitivity to the size of datasets.

Hardware Trojans are usually implanted by making malicious changes to a chip circuit, which can destroy chip functions or expose sensitive information once activated. The hardware Trojan detection method based on side channel information has now become one of the most widely used detection methods. However, due to the influence of the deviation of the acquisition equipment and the noise of the actual chip working environment, insufficient acquisition of useful information of the collected side channel information occurs, affecting the final results. To address the problem, this paper proposes a detection method based on a dual discriminator assisted conditional generation adversarial network (D2ACGAN), which combines the benefits of CGAN, ACGAN, and D2GAN models and can learn a variety of valid information of the tested chip. It can distinguish between side channel data with and without hardware Trojan and classify hardware Trojan using the extended data. Furthermore, to compare the performance of the proposed model, we use the existing CGAN and ACGAN models equally for side channel information expansion and hardware Trojan detection. Finally, the designed hardware Trojan is implanted in an encryption chip for generating data quality evaluation experiments and model method performance experiments. The results show that the average detection accuracy of the D2ACGAN-based hardware Trojan classification model can reach 97.08%, which is better than the detection models based on CNN, SVM, etc. The D2ACGAN model also outperforms the CGAN and ACGAN models in terms of generated data and hardware Trojan classification.