Deep Learning-Based Hardware Trojan Detection With Block-Based Netlist Information Extraction

Abstract

With the globalization of the semiconductor industry, hardware Trojans (HTs) are an emergent security threat in modern integrated circuit (IC) production. Research is now being conducted into designing more accurate and efficient methods to detect HTs. Recently, a number of machine learning (ML)-based HT detection approaches have been proposed; however, most of them still use knowledge-driven approaches to design features and often use engineering intuition to carefully craft the detection model to improve accuracy. Therefore, in this work, we propose a data-driven HT detection system based on gate-level netlists. The system consists of four main parts: 1) Information extraction from netlist block; 2) Natural language processing (NLP) for translating netlist information; 3) Deel learning (DL)-based HT detection model; 4) HT component final voter. In the experiments, both a long short-term memory networks (LSTM) model and convolutional neural network (CNN) model are used as our detection models. We performed the experiments on the HT benchmarks from Trust-hub and K-fold crossing verification has been applied to evaluate different parameter settings in the training procedure. The experimental results show that the proposed HT detection system can achieve 79.29% TPR, 99.97% TNR, 87.75% PPV and 99.94% NPV for combinational Trojan detection and 93.46% TPR, 99.99% TNR, 98.92% PPV and 99.92% NPV for sequential Trojan detection after voting-based optimization using the LEDA library-based HT benchmarks ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">logic_level</i> =4, upsampling, LSTM, 5 epochs).