Dual-Leak: Deep Unsupervised Active Learning for Cross-Device Profiled Side-Channel Leakage Analysis
Dual-Leak: Deep Unsupervised Active Learning for Cross-Device Profiled Side-Channel Leakage Analysis
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DOI：
10.1109/host55118.2023.10133491
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发表时间：
2023-05
期刊：
影响因子：
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通讯作者：
H. Yu;Shuo Wang;Haoqi Shan;Max Panoff;Michael Lee;Kaichen Yang;Yier Jin
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文献类型：
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作者：
H. Yu;Shuo Wang;Haoqi Shan;Max Panoff;Michael Lee;Kaichen Yang;Yier Jin
基于深度学习的侧信道分析（SCA）作为SCA攻击的一个新的分支，对密码算法的实现带来了严重的隐私和安全威胁。尽管它们对硬件安全性有影响，但现有的基于DL的SCA攻击并没有充分利用DL算法的潜力。因此，先前提出的基于DL的SCA攻击可能没有显示出从目标设计中提取敏感信息的真实的能力。在本文中，我们提出了一种新的跨设备SCA方法，名为Dual-Leak，该方法应用深度无监督主动学习来创建DL模型，用于破坏加密实现，即使部署了对策。在本地数据集和公开数据集上的实验结果表明，我们的Dual-Leak攻击显著优于最先进的作品，同时不需要来自受害者设备的标记跟踪（即，无监督学习）。还讨论了防止新攻击的对策，以确保硬件安全。
Deep Learning (DL)-based side-channel analysis (SCA), as a new branch of SCA attacks, poses a significant privacy and security threat to implementations of cryptographic algorithms. Despite their impacts on hardware security, existing DL-based SCA attacks have not fully leveraged the potential of DL algorithms. Therefore, previously proposed DL-based SCA attacks may not show the real capability to extract sensitive information from target designs. In this paper, we propose a novel cross-device SCA method, named Dual-Leak, that applies Deep Unsupervised Active Learning to create a DL model for breaking cryptographic implementations, even with countermeasures deployed. The experimental results on both the local dataset and publicly available dataset show that our Dual-Leak attack significantly outperforms state-of-the-art works while no labeled traces are required from victim devices (i.e., unsupervised learning). Countermeasures are also discussed to assure hardware security against new attacks.