Hardware-Trojan Detection at Gate-level Netlists using Gradient Boosting Decision Tree Models

Ryotaro Negishi, Tatsuki Kurihara, Nozomu Togawa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Technological devices including consumer 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 ICs, which are essential for tech-nological devices, may lead to the insertion of hardware Trojans. This paper proposes a hardware-Trojan detection method at gate-level netlists based on the gradient boosting decision tree models. We firstly propose the optimal set of Trojan features among many feature candidates at a netlist level through thorough evaluations. Then, we evaluate various gradient boosting decision tree models and determine XGBoost is the best for hardware-Trojan detection. Finally, we construct an XGBoost-based hardware-Trojan 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. This value is 0.175 points higher than that of the existing best method.

Original languageEnglish
Title of host publication2022 IEEE 12th International Conference on Consumer Electronics, ICCE-Berlin 2022
PublisherIEEE Computer Society
ISBN (Electronic)9781665456760
DOIs
Publication statusPublished - 2022
Event12th IEEE International Conference on Consumer Electronics, ICCE-Berlin 2022 - Berlin, Germany
Duration: 2022 Sep 22022 Sep 6

Publication series

NameIEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin
Volume2022-September
ISSN (Print)2166-6814
ISSN (Electronic)2166-6822

Conference

Conference12th IEEE International Conference on Consumer Electronics, ICCE-Berlin 2022
Country/TerritoryGermany
CityBerlin
Period22/9/222/9/6

Keywords

  • gradient boosting decision tree
  • hardware security
  • hardware Trojan
  • machine learning
  • netlist
  • XGBoost

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Industrial and Manufacturing Engineering
  • Media Technology

Fingerprint

Dive into the research topics of 'Hardware-Trojan Detection at Gate-level Netlists using Gradient Boosting Decision Tree Models'. Together they form a unique fingerprint.

Cite this