部分轮廓定时攻击的信息论鉴别:解决空箱问题

Éloi de Chérisey, S. Guilley, O. Rioul, Darshana Jayasinghe
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引用次数: 0

摘要

在任何侧信道攻击中,都希望利用所有可用的泄漏数据来计算区分符的值。剖面阶段对于获得准确的泄漏模型至关重要,但它可能不是详尽的。因此,信息理论的区分者可能会出现在以前看不见的数据上,这是一种产生空箱的现象。严格应用最大似然方法会产生一个甚至不健全的区分符。忽略空箱子重新建立健全,但严重限制其成功率方面的表现。本文的目的就是要纠正这种情况。在这项研究中,我们提出了六种不同的技术来提高信息理论区分器的性能。我们通过将它们应用于定时攻击,包括合成泄漏和真实泄漏,对它们进行了深入的研究。也就是说,我们在成功率方面比较了它们,并表明它们的性能取决于分析的数量,并且可以通过偏差方差分析来解释。我们的工作结果是,存在一些用例,特别是当测量有噪声时,我们的新信息理论区分器(通常是软滴区分器)与已知的侧信道区分器相比表现最好,尽管是空箱情况。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Information Theoretic Distinguishers for Timing Attacks with Partial Profiles: Solving the Empty Bin Issue
In any side-channel attack, it is desirable to exploit all the available leakage data to compute the distinguisher’s values. The profiling phase is essential to obtain an accurate leakage model, yet it may not be exhaustive. As a result, information theoretic distinguishers may come up on previously unseen data, a phenomenon yielding empty bins. A strict application of the maximum likelihood method yields a distinguisher that is not even sound. Ignoring empty bins reestablishes soundness, but seriously limits its performance in terms of success rate. The purpose of this paper is to remedy this situation. In this research, we propose six different techniques to improve the performance of information theoretic distinguishers. We study them thoroughly by applying them to timing attacks, both with synthetic and real leakages. Namely, we compare them in terms of success rate, and show that their performance depends on the amount of profiling, and can be explained by a bias-variance analysis. The result of our work is that there exist use-cases, especially when measurements are noisy, where our novel information theoretic distinguishers (typically the soft-drop distinguisher) perform the best compared to known side-channel distinguishers, despite the empty bin situation.
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