Hardware Trojans Detection at Register Transfer Level Based on Machine Learning

Title	Hardware Trojans Detection at Register Transfer Level Based on Machine Learning
Publication Type	Conference Paper
Year of Publication	2019
Authors	Han, Tao, Wang, Yuze, Liu, Peng
Conference Name	2019 IEEE International Symposium on Circuits and Systems (ISCAS)
ISBN Number	978-1-7281-0397-6
Keywords	circuit features extraction, cyber physical systems, database management systems, electronic engineering computing, feature extraction, Flip-flops, Hardware, Hardware Trojans detection, Hardware Trojans library, integrated circuit design, Integrated circuit modeling, integrated circuits design process, invasive software, learning (artificial intelligence), Libraries, machine-learning-based detection method, pubcrawl, register transfer level, resilience, Resiliency, RTL source codes, server-client mechanism, shift registers, source code (software), supply chain security, Training, training database, trojan horse detection, Trojan horses
Abstract	To accurately detect Hardware Trojans in integrated circuits design process, a machine-learning-based detection method at the register transfer level (RTL) is proposed. In this method, circuit features are extracted from the RTL source codes and a training database is built using circuits in a Hardware Trojans library. The training database is used to train an efficient detection model based on the gradient boosting algorithm. In order to expand the Hardware Trojans library for detecting new types of Hardware Trojans and update the detection model in time, a server-client mechanism is used. The proposed method can achieve 100% true positive rate and 89% true negative rate, on average, based on the benchmark from Trust-Hub.
URL	https://ieeexplore.ieee.org/document/8702479
DOI	10.1109/ISCAS.2019.8702479
Citation Key	han_hardware_2019