GramsDet: Hardware Trojan Detection Based on Recurrent Neural Network 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 ngram circuit segmentation technique, and implements the gate embedding by the ordersensitive cooccurrence matrix. Then, a stacked long shortterm 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).

A Novel ATPG Method to Increase Activation Probability of Hardware Trojan A hardware Trojan is defined as a malicious, undesired modification hidden in an electronic circuit. Since the logic test has the advantage of being free of process fluctuations and test noise, a logic test is used to test whether the circuit under test contains a hardware Trojan. This paper optimizes Ndetect ATPG detection scheme to generate optimized test vectors for logic testing. The simulation results on s5378 and s38417 benchmarks demonstrate that, compared with traditional logic test technique, the test vectors generated by this paper are able to successfully increase activation probability of a hardware Trojan and detect abnormal outputs.