The More You Know: Improving Laser Fault Injection with Prior Knowledge

Marina Krček, T. Ordas, Daniele Fronte, S. Picek
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引用次数: 4

Abstract

We consider finding as many faults as possible on the target device in the laser fault injection security evaluation. Since the search space is large, we require efficient search methods. Recently, an evolutionary approach using a memetic algorithm was proposed and shown to find more interesting parameter combinations than random search, which is commonly used. Unfortunately, once a variation on the bench or target is introduced, the process must be repeated to find suitable parameter combinations anew.To negate the effect of variation, we propose a novel method combining a memetic algorithm with a machine learning approach called a decision tree. Our approach improves the memetic algorithm by using prior knowledge of the target introduced in the initial phase of the memetic algorithm. In our experiments, the decision tree rules enhance the performance of the memetic algorithm by finding more interesting faults in different samples of the same target. Our approach shows more than two orders of magnitude better performance than random search and up to 60% better performance than previous state-of-the-art results with a memetic algorithm. Another advantage of our approach is human-readable rules, allowing the first insights into the explainability of target characterization for laser fault injection.
你知道的越多:用先验知识改进激光故障注入
在激光故障注入安全性评估中,我们考虑在目标设备上发现尽可能多的故障。由于搜索空间很大,我们需要高效的搜索方法。最近,人们提出了一种基于模因算法的进化方法,并证明了它比常用的随机搜索方法能找到更多有趣的参数组合。不幸的是,一旦在实验台上或目标上引入了变化,必须重复该过程以重新找到合适的参数组合。为了消除变异的影响,我们提出了一种将模因算法与机器学习方法相结合的新方法,称为决策树。该方法利用模因算法初始阶段引入的目标先验知识对模因算法进行了改进。在我们的实验中,决策树规则通过在同一目标的不同样本中发现更多有趣的错误来增强模因算法的性能。我们的方法显示了比随机搜索好两个数量级以上的性能,比以前使用模因算法的最先进结果高出60%。我们的方法的另一个优点是人类可读的规则,允许第一次深入了解激光故障注入的目标表征的可解释性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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