Recently, many profiling side-channel attacks based on Machine Learning and Deep Learning have been proposed. Most of them focus on reducing the number of traces required for successful attacks by optimizing the modeling algorithms. In previous work, relatively sufficient traces need to be used for training a model. However, in the practical profiling phase, it is difficult or impossible to collect sufficient traces due to the constraint of various resources. In this case, the performance of profiling attacks is inefficient even if proper modeling algorithms are used. In this paper, the main problem we consider is how to conduct more efficient profiling attacks when sufficient profiling traces cannot be obtained. To deal with this problem, we first introduce the Conditional Generative Adversarial Network (CGAN) in the context of side-channel attacks. We show that CGAN can generate new traces to enlarge the size of the profiling set, which improves the performance of profiling attacks. For both unprotected and protected cryptographic algorithms, we find that CGAN can effectively learn the leakage of traces collected in their implementations. We also apply it to different modeling algorithms. In our experiments, the model constructed with the augmented profiling set can reduce the required attack traces by more than half, which means the generated traces can provide useful information as the real traces.

中文翻译：

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使用条件生成对抗网络提高实际分析侧信道攻击的性能

最近，已经提出了许多基于机器学习和深度学习的侧信道分析攻击。他们中的大多数专注于通过优化建模算法来减少成功攻击所需的跟踪数量。在之前的工作中，需要使用相对足够的trace来训练模型。然而，在实际分析阶段，由于各种资源的限制，很难或不可能收集到足够的痕迹。在这种情况下，即使使用了适当的建模算法，分析攻击的性能也是低效的。在本文中，我们考虑的主要问题是在无法获得足够的分析痕迹时如何进行更有效的分析攻击。为了解决这个问题，我们首先在侧信道攻击的背景下介绍条件生成对抗网络（CGAN）。我们表明 CGAN 可以生成新的痕迹来扩大分析集的大小，从而提高分析攻击的性能。对于不受保护和受保护的密码算法，我们发现 CGAN 可以有效地学习在其实现中收集的痕迹的泄漏。我们还将其应用于不同的建模算法。在我们的实验中，使用增强的分析集构建的模型可以将所需的攻击痕迹减少一半以上，这意味着生成的痕迹可以提供与真实痕迹一样的有用信息。对于不受保护和受保护的密码算法，我们发现 CGAN 可以有效地学习在其实现中收集的痕迹的泄漏。我们还将其应用于不同的建模算法。在我们的实验中，使用增强的分析集构建的模型可以将所需的攻击痕迹减少一半以上，这意味着生成的痕迹可以提供与真实痕迹一样的有用信息。对于不受保护和受保护的密码算法，我们发现 CGAN 可以有效地学习在其实现中收集的痕迹的泄漏。我们还将其应用于不同的建模算法。在我们的实验中，使用增强的分析集构建的模型可以将所需的攻击痕迹减少一半以上，这意味着生成的痕迹可以提供与真实痕迹一样的有用信息。