GramsDet: Hardware Trojan Detection Based on Recurrent Neural Network

Abstract

Hardware Trojan (HT) has paid more and more attention to the academia and industry because of its significant potential threat. In this paper, we propose a novel approach, named GramsDet, to detect HT through capturing suspicious circuit connection structure using recurrent neural network. GramsDet considers that HT usually be inserted into the regions with low transition probability, so the circuit fragments associated with HT should have special connection structures. GramsDet models the target circuit using n-gram circuit segmentation technique, and implements the "gate embedding" by the order-sensitive co-occurrence matrix. Then, a stacked long short-term memory network is designed to build a robust HT detection model. The experimental results on different benchmarks show that GramsDet can detect effectively Trojan logic without the "Golden model" of the circuit under detection (CUD).