论文标题
LASCA：学习辅助侧渠道延迟分析硬件特洛伊木马检测
LASCA: Learning Assisted Side Channel Delay Analysis for Hardware Trojan Detection
论文作者
论文摘要
在本文中，我们介绍了用于硬件特洛伊木马检测的学习辅助侧通道延迟分析（LASCA）方法。我们提出的解决方案，与先前的艺术不同，不需要黄金IC。取而代之的是，它训练神经网络作为一个过程跟踪看门狗，以将静态计时数据（在设计时间产生）与从时钟频率扫描（测试时间）获得的延迟信息相关联，以进行特洛伊木马检测。使用LASCA流，我们在模拟场景中检测到近90％的硬件Trojans。
In this paper, we introduce a Learning Assisted Side Channel delay Analysis (LASCA) methodology for Hardware Trojan detection. Our proposed solution, unlike the prior art, does not require a Golden IC. Instead, it trains a Neural Network to act as a process tracking watchdog for correlating the static timing data (produced at design time) to the delay information obtained from clock frequency sweeping (at test time) for the purpose of Trojan detection. Using the LASCA flow, we detect close to 90% of Hardware Trojans in the simulated scenarios.
