BGNN-HT: Bidirectional Graph Neural Network for Hardware Trojan Cells Detection at Gate Level

Peiheng Zhan, Haihua Shen, Shan Li, Huawei Li

Abstract: Recently, complex process of production forces Integrated Circuit (IC) to be designed by third-party Electronic Design Automation (EDA) tool or outsourcing, which will create an opportunity for malicious circuits to be inserted into ICs, known as Hardware Trojan (HT). Up to now, there are still challenges in existing researches, such as dependence on the golden model, unclear position of HTs, and difficulty in unknown HT detection. In this paper, a HT detection model called BGNN-HT based on bidirectional graph neural network is proposed, which can detect HT cells by assessing the structure of its surrounding cells at gate level. BGNN-HT can precisely detect HT cells in ICs, and it does not require the golden model or manual feature extraction, which greatly reduces the difficulty of detection and can adapt to unknown HTs. Experiments are conducted on Trust-hub benchmarks including TRIT-TC and TRIT-TS to evaluate our model. The results show that when detecting unknown circuits and HTs, BGNN-HT can reach 96% True Positive Rate (TPR) and 99% True Negative Rate (TNR) in various datasets, and even 99% TPR and TNR in TRIT-TC and TRIT-TS.