使用神经控制微分方程和功耗轨迹分析进行硬件木马的早期检测
Early Detection of Hardware Trojans Using Neural Controlled Differential Equations and Analysis of Power Traces
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中文总结 AI 辅助
针对硬件木马检测难题，提出用神经控制微分方程和功耗轨迹分析的新方法，利用NCDE模型学习正常功耗行为，结合LDA分类器区分不同情况，实验表明该方法准确率高，能处理休眠木马，在标准基准上验证了其性能。
中文摘要 AI 辅助
不断演变的硬件木马通过隐秘、自适应行为规避传统检测，对现代数字系统构成严重威胁。即使是利用机器学习进展的最新方法，也只能在激活后检测到它们，留下安全漏洞的关键窗口。为填补这一空白，我们提出一种使用神经控制微分方程（NCDEs）和功耗轨迹分析的硬件木马检测与预测新方法。我们的方法利用仅在无木马数据上训练的NCDE模型学习正常功耗行为，并结合在标记数据上校准的线性判别分析（LDA）分类器，以区分三种情况：无木马、休眠木马和活跃木马。我们的方法使用滑动窗口处理侧信道测量，能够检测到表明木马存在的细微功耗偏差，即使在休眠时也能检测到。实验结果表明，与传统机器学习方法相比，所提出的基于NCDE的方法具有更高的准确率，并且具有在灵敏度阈值以上处理休眠木马的额外优势。我们在标准硬件木马基准上验证了我们的方法，显示出强大的检测和预测性能。
英文摘要
Evolving Hardware Trojans pose a serious threat to modern digital systems by evading traditional detection through stealthy, adaptive behavior. Even recent methods that leverage advances in machine learning can only detect them after activation, leaving a critical window for potential security breaches. To address this gap, we propose a novel approach for hardware Trojan detection and prediction using Neural Controlled Differential Equations (NCDEs) and analysis of power traces. Our method leverages an NCDE model trained exclusively on Trojan-free data to learn nominal power behavior, combined with a Linear Discriminant Analysis (LDA) classifier calibrated on labeled data, to distinguish between three scenarios: no Trojan, dormant Trojan, and active Trojan. Our method uses a sliding window to process side-channel measurements, enabling detection of subtle power consumption deviations that indicate Trojan presence, even when dormant. Experimental results demonstrate that the proposed NCDE-based method achieves superior accuracy compared to traditional machine learning approaches, with the additional advantage of handling dormant Trojans above a sensitivity threshold. We validate our approach on standard hardware Trojan benchmarks, showing robust detection and prediction performance.
