Detection of hardware Trojans is vital to ensure the security and trustworthiness of System-on-Chip (SoC) designs. Side-channel analysis is effective for Trojan detection by analyzing various side-channel signatures such as power, current, and delay. In this article, we propose an efficient test generation technique to facilitate side-channel analysis utilizing dynamic current. While early work on current-aware test generation has proposed several promising ideas, there are two major challenges in applying it on large designs: (i) The test generation time grows exponentially with the design complexity, and (ii) it is infeasible to detect Trojans, since the side-channel sensitivity is marginal compared to the noise and process variations. Our proposed work addresses both challenges by effectively exploiting the affinity between the inputs and rare (suspicious) nodes. The basic idea is to quickly find the profitable ordered pairs of test vectors that can maximize side-channel sensitivity. This article makes two important contributions: (i) It proposed an efficient test generation algorithm that can produce the first patterns in the test vectors to maximize activation of suspicious nodes using an SMT solver, and (ii) it developed a genetic-algorithm based test generation technique to produce the second patterns in the test vectors to maximize the switching in the suspicious regions while minimizing the switching in the rest of the design. Our experimental results demonstrate that we can drastically improve both the side-channel sensitivity (62× on average) and time complexity (13× on average) compared to the state-of-the-art test generation techniques.

中文翻译：

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MaxSense

硬件木马的检测对于确保片上系统 (SoC) 设计的安全性和可信赖性至关重要。通过分析功率、电流和延迟等各种侧信道特征，侧信道分析对木马检测非常有效。在本文中，我们提出了一种有效的测试生成技术，以促进利用动态电流进行边信道分析。虽然电流感知测试生成的早期工作提出了几个有前途的想法，但将其应用于大型设计存在两个主要挑战：（i）测试生成时间随着设计复杂性呈指数增长，并且（ii）无法检测到特洛伊木马，因为与噪声和过程变化相比，侧信道敏感性是微不足道的。我们提出的工作通过有效利用输入和稀有（可疑）节点之间的亲和力来解决这两个挑战。其基本思想是快速找到可以最大化边信道灵敏度的有利可图的有序测试向量对。本文做出了两个重要贡献：（i）它提出了一种有效的测试生成算法，可以在测试向量中生成第一个模式，以使用 SMT 求解器最大化可疑节点的激活，以及（ii）它开发了一种基于遗传算法的测试生成技术以在测试向量中生成第二个模式，以最大化可疑区域的切换，同时最小化设计其余部分的切换。