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

TL;DR

This paper introduces R-HTDetector, a novel adversarial training approach for hardware Trojan detection that enhances robustness against adversarial modifications, improving security in integrated circuits.

Contribution

It presents the first adversarial training method for hardware Trojan detection, providing theoretical analysis and experimental validation on Trust-HUB benchmarks.

Findings

R-HTDetector effectively resists adversarially modified HTs.

Maintains high detection accuracy under adversarial conditions.

Outperforms existing methods in robustness against attacks.

Abstract

Hardware Trojans (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 adversarial examples. 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 (R-HTDetector). 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.