R-HTDetector: Robust Hardware-Trojan Detection Based on Adversarial Training

Kento Hasegawa, Seira Hidano, Kohei Nozawa, Shinsaku Kiyomoto, Nozomu Togawa

Research output: Contribution to journalArticlepeer-review

Abstract

Hardware Trojans&#x00A0;(HTs) have become a serious problem, and extermination of them is strongly required for enhancing the security and safety of integrated circuits. An effective solution is to identify HTs at the gate level via machine learning techniques. However, machine learning has specific vulnerabilities, such as <italic>adversarial examples</italic>. In reality, it has been reported that adversarial modified HTs greatly degrade the performance of a machine learning-based HT detection method. Therefore, we propose a robust HT detection method using adversarial training (<italic>R-HTDetector</italic>). We formally describe the robustness of R-HTDetector in modifying HTs. Our work gives the world-first adversarial training for HT detection with theoretical backgrounds. We show through experiments with Trust-HUB benchmarks that R-HTDetector overcomes adversarial examples while maintaining its original accuracy.

Original languageEnglish
Pages (from-to)1-13
Number of pages13
JournalIEEE Transactions on Computers
DOIs
Publication statusAccepted/In press - 2022

Keywords

  • adversarial examples
  • adversarial training
  • Feature extraction
  • gate-level netlists
  • Hardware
  • hardware Trojans
  • Integrated circuits
  • Logic gates
  • Machine learning
  • machine learning
  • Training
  • Trojan horses

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Hardware and Architecture
  • Computational Theory and Mathematics

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