RLocHT: A Hardware Trojans Localization Method Utilizing Deep Learning at the Gate-Level
Yuanyuan Zhang, Chen Dong, Yi Xu, Yinan Yao, Chenxi Lyu · 2022 IEEE 10th Joint International Information Technology and Artificial Intelligence Conference (ITAIC) · 2022
Hardware is the foundation of the Cyber-Physical Systems, while the security threats brought by the hardware Tro-jan can make disastrous consequences. There are various studies on detecting hardware Trojans, but in order to defend against the hardware Trojans, the researches just staying at the shallow detection stage are far away from the goal. Finding the specific location of hardware Trojans is a prerequisite for more precise control of them. In this paper, a hardware Trojans localization method utilizing deep learning at the gate-level (RLocHT) is proposed. RLocHT is based on the hardware Trojans detection results of TextCNN, combined with the path division technology, which lets the convolutional neural network detect the divided paths in more detail to narrow the range of locating hardware Trojans and achieve the purpose of localization. In addition, two new evaluation metrics, localization loss rate (LoLR), and total locating deviation$(SD_{\Delta})$, are defined to evaluate the integrity and accuracy of RLocHT. Finally, the length of the division cutlen in the path division technology is also adjusted to optimize the performance. With the optimal parameter of each netlist, for all seven benchmarks, the results achieve an average localization loss rate$LoLR_{avg}$of 33.28% and an average total locating deviation$SD_{\Delta avg}$of 2.55.