Hardware Trojan Detection at LUT: Where Structural Features Meet Behavioral Characteristics

Abstract

This work proposes a novel hardware Trojan detection method that leverages static structural features and behavioral characteristics in field programmable gate array (FPGA) netlists. Mapping of hardware design sources to look-up-table (LUT) networks makes these features explicit, allowing automated feature extraction and further effective Trojan detection through machine learning. Four-dimensional features are extracted for each signal and a random forest classifier is trained for Trojan net classification. Experiments using Trust-Hub benchmarks show promising Trojan detection results with accuracy, precision, and F1-measure of 99.986%, 100%, and 99.769% respectively on average.