Hardware Trojan Detection at LUT: Where Structural Features Meet Behavioral Characteristics
Lingjuan Wu, Xuelin Zhang, Siyi Wang, Wei Hu
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.
Topics & Concepts
TrojanHardware TrojanField-programmable gate arrayComputer scienceLookup tableFeature extractionClassifier (UML)Random forestTable (database)Artificial intelligenceComputer hardwareFeature (linguistics)Pattern recognition (psychology)Embedded systemData miningOperating systemComputer securityLinguisticsPhilosophyPhysical Unclonable Functions (PUFs) and Hardware SecurityIntegrated Circuits and Semiconductor Failure AnalysisAdversarial Robustness in Machine Learning