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Hardware-Trojan Detection at Gate-Level Netlists Using a Gradient Boosting Decision Tree Model and Its Extension Using Trojan Probability Propagation

Ryotaro NEGISHI, Tatsuki KURIHARA, Nozomu TOGAWA

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Summary :

Technological devices have become deeply embedded in people's lives, and their demand is growing every year. It has been indicated that outsourcing the design and manufacturing of integrated circuits, which are essential for technological devices, may lead to the insertion of malicious circuitry, called hardware Trojans (HTs). This paper proposes an HT detection method at gate-level netlists based on XGBoost, one of the best gradient boosting decision tree models. We first propose the optimal set of HT features among many feature candidates at a netlist level through thorough evaluations. Then, we construct an XGBoost-based HT detection method with its optimized hyperparameters. Evaluation experiments were conducted on the netlists from Trust-HUB benchmarks and showed the average F-measure of 0.842 using the proposed method. Also, we newly propose a Trojan probability propagation method that effectively corrects the HT detection results and apply it to the results obtained by XGBoost-based HT detection. Evaluation experiments showed that the average F-measure is improved to 0.861. This value is 0.194 points higher than that of the existing best method proposed so far.

Publication
IEICE TRANSACTIONS on Fundamentals Vol.E107-A No.1 pp.63-74
Publication Date
2024/01/01
Publicized
2023/08/16
Online ISSN
1745-1337
DOI
10.1587/transfun.2023KEP0005
Type of Manuscript
Special Section PAPER (Special Section on Circuits and Systems)
Category

Authors

Ryotaro NEGISHI
  Waseda University
Tatsuki KURIHARA
  Waseda University
Nozomu TOGAWA
  Waseda University

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