论文标题
基于对抗性攻击的对策，反对深度学习侧通道攻击
Adversarial Attack Based Countermeasures against Deep Learning Side-Channel Attacks
论文作者
论文摘要
以前的许多作品研究了在侧通道攻击的背景下应用的深度学习算法，该算法证明了执行成功的关键恢复的能力。这些研究表明，借助深度学习，现代的加密设备越来越受到侧向通道攻击的威胁。但是，现有的对策旨在抵抗经典的侧通道攻击，并且无法保护加密设备免受基于深度学习的侧向通道攻击。因此，由于对基于深度学习的侧渠攻击的对策非常有必要。尽管深度学习在解决复杂问题方面具有很高的潜力，但它很容易受到对抗攻击的影响，以微妙的扰动形式与导致模型进行错误预测的输入的扰动。 在本文中，我们提出了一种基于对抗性攻击的新颖对策，专门针对基于深度学习的侧通道攻击而设计。我们估计了基于深度学习的侧通道攻击中常用的几种模型，以评估所提出的对策。它表明，我们的方法可以有效地保护加密设备在实践中的基于深度学习的侧通道攻击中。此外，我们的实验表明，新的对策还可以抵抗经典的侧向通道攻击。
Numerous previous works have studied deep learning algorithms applied in the context of side-channel attacks, which demonstrated the ability to perform successful key recoveries. These studies show that modern cryptographic devices are increasingly threatened by side-channel attacks with the help of deep learning. However, the existing countermeasures are designed to resist classical side-channel attacks, and cannot protect cryptographic devices from deep learning based side-channel attacks. Thus, there arises a strong need for countermeasures against deep learning based side-channel attacks. Although deep learning has the high potential in solving complex problems, it is vulnerable to adversarial attacks in the form of subtle perturbations to inputs that lead a model to predict incorrectly. In this paper, we propose a kind of novel countermeasures based on adversarial attacks that is specifically designed against deep learning based side-channel attacks. We estimate several models commonly used in deep learning based side-channel attacks to evaluate the proposed countermeasures. It shows that our approach can effectively protect cryptographic devices from deep learning based side-channel attacks in practice. In addition, our experiments show that the new countermeasures can also resist classical side-channel attacks.
