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arXiv 2304.13232cs.ARcs.CRcs.LG
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多准则硬件木马检测:一种强化学习方法

Multi-criteria Hardware Trojan Detection: A Reinforcement Learning Approach

New Mexico State University(新墨西哥州立大学);Miami University(迈阿密大学);North Carolina A&T State University(北卡罗来纳农工州立大学)

Amin Sarihi, Peter Jamieson, Ahmad Patooghy, Abdel-Hameed A. Badawy

AI总结:

针对现有硬件木马检测多依赖单一准则的局限,提出一种带可调奖励函数的多准则强化学习检测工具,可适配多场景,还给出公平比较方法,在ISCAS-85基准上检测成功率达84.2%。

摘要:

硬件木马(Hardware Trojans, HTs)是指在设计或制造阶段被恶意植入的非预期修改,会严重破坏数字集成电路的安全性与功能。HT可依据多种设计准则植入,例如网络开关活动、可观测性、可控性等。但据我们所知,现有大多数HT检测方法仅基于单一准则,即网络开关活动。本文提出一种多准则强化学习(reinforcement learning, RL)HT检测工具,其核心特点是配备可调节的奖励函数,能适配不同的HT检测场景。该工具既可探索现有检测策略,也能以极低代价适配新的检测场景。我们还提出了一种可公平比较HT检测方法的通用方法论。初步实验结果显示,在ISCAS-85基准测试集上,该方法的HT平均检测成功率达84.2%。

英文摘要:

Hardware Trojans (HTs) are undesired design or manufacturing modifications that can severely alter the security and functionality of digital integrated circuits. HTs can be inserted according to various design criteria, e.g., nets switching activity, observability, controllability, etc. However, to our knowledge, most HT detection methods are only based on a single criterion, i.e., nets switching activity. This paper proposes a multi-criteria reinforcement learning (RL) HT detection tool that features a tunable reward function for different HT detection scenarios. The tool allows for exploring existing detection strategies and can adapt new detection scenarios with minimal effort. We also propose a generic methodology for comparing HT detection methods fairly. Our preliminary results show an average of 84.2% successful HT detection in ISCAS-85 benchmark

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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