论文标题
R-HTDETECTOR：基于对抗训练的强大硬件-Trojan检测
R-HTDetector: Robust Hardware-Trojan Detection Based on Adversarial Training
论文作者
论文摘要
硬件木马（HTS）已成为一个严重的问题，并且强烈需要灭绝它们来提高集成电路的安全性和安全性。一个有效的解决方案是通过机器学习技术在门水平上识别HTS。但是，机器学习具有特定的漏洞，例如对抗性示例。实际上，据报道，对抗性修改的HTS极大地降低了基于机器学习的HT检测方法的性能。因此，我们建议使用对抗训练（R-HTDetector）提出一种强大的HT检测方法。我们正式描述了R-HTDetector在修改HTS中的鲁棒性。我们的工作为具有理论背景的HT检测提供了世界第一的对抗训练。我们通过具有信任式基准测试的实验来展示R-HTDetector在保持其原始准确性的同时，可以克服对抗性示例。
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.
