ATTRITION: Attacking Static Hardware Trojan Detection Techniques Using Reinforcement Learning
ATTRITION: Attacking Static Hardware Trojan Detection Techniques Using Reinforcement Learning
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DOI：
10.1145/3548606.3560690
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发表时间：
2022-08
期刊：
影响因子：
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通讯作者：
Vasudev Gohil;Hao Guo;Satwik Patnaik;Jeyavijayan Rajendran
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作者：
Vasudev Gohil;Hao Guo;Satwik Patnaik;Jeyavijayan Rajendran
在集成电路制造过程中插入的隐形硬件木马（HT）可以绕过关键基础设施的安全性。虽然研究人员已经提出了许多技术来检测HT，存在几个关键的限制，包括：（i）HT检测的成功率低，（ii）算法复杂度高，以及（iii）大量的测试模式。此外，正如我们在这项工作中所示，现有（包括最先进的）检测技术的最相关缺点源于不正确的评估方法，即，他们假设对手随机插入HT。这种不适当的对抗性假设使检测技术能够声称高HT检测精度，导致“虚假的安全感”。“据我们所知，尽管在检测制造过程中插入的HT方面进行了十多年的研究，但还没有一致的努力来对HT检测技术进行系统的评估。在本文中，我们扮演一个现实的对手的角色，并通过使用强化学习（RL）开发一个自动化，可扩展和实用的攻击框架ATTRITION来质疑HT检测技术的有效性。ATTRITION规避了八种检测技术（发表在顶级安全场所，在学术界被广泛引用等）。跨越两个HT检测类别，展示其不可知行为。与随机插入的HT相比，ATTRITION实现了47倍和211倍的平均攻击成功率，对抗最先进的逻辑测试和侧通道技术。为了证明ATTRITION在规避检测技术方面的能力，我们评估了不同的设计，从广泛使用的学术套件（ISCAS-85，ISCAS-89）到更大的设计，如开源MIPS和mor 1 kx处理器到AES和GPS模块。此外，我们通过两个案例研究（特权提升和kill switch）展示了ATTRITION生成的HT对mor 1 kx处理器的影响。我们设想我们的工作，沿着我们发布的HT基准和模型，促进更好的HT检测技术的发展。
Stealthy hardware Trojans (HTs) inserted during the fabrication of integrated circuits can bypass the security of critical infrastructures. Although researchers have proposed many techniques to detect HTs, several critical limitations exist, including: (i) a low success rate of HT detection, (ii) high algorithmic complexity, and (iii) a large number of test patterns. Furthermore, as we show in this work the most pertinent drawback of prior (including state-of-the-art) detection techniques stems from an incorrect evaluation methodology, i.e., they assume that an adversary inserts HTs randomly. Such inappropriate adversarial assumptions enable detection techniques to claim high HT detection accuracy, leading to a "false sense of security." To the best of our knowledge, despite more than a decade of research on detecting HTs inserted during fabrication, there have been no concerted efforts to perform a systematic evaluation of HT detection techniques. In this paper, we play the role of a realistic adversary and question the efficacy of HT detection techniques by developing an automated, scalable, and practical attack framework, ATTRITION, using reinforcement learning (RL). ATTRITION evades eight detection techniques (published in premier security venues, well-cited in academia, etc.) across two HT detection categories, showcasing its agnostic behavior. ATTRITION achieves average attack success rates of 47x and 211x compared to randomly inserted HTs against state-of-the-art logic testing and side channel techniques. To demonstrate ATTRITION's ability in evading detection techniques, we evaluate different designs ranging from the widely-used academic suites (ISCAS-85, ISCAS-89) to larger designs such as the open-source MIPS and mor1kx processors to AES and a GPS module. Additionally, we showcase the impact of ATTRITION generated HTs through two case studies (privilege escalation and kill switch) on mor1kx processor. We envision that our work, along with our released HT benchmarks and models fosters the development of better HT detection techniques.