Breaking AES-128: Machine Learning-Based SCA Under Different Scenarios and Devices
Breaking AES-128: Machine Learning-Based SCA Under Different Scenarios and Devices
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DOI：
10.1109/csr57506.2023.10225009
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发表时间：
2023-07
期刊：
影响因子：
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通讯作者：
Sara Tehranipoor;Nima Karimian;Jacky Edmonds
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文献类型：
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作者：
Sara Tehranipoor;Nima Karimian;Jacky Edmonds
基于机器学习的侧通道攻击（MLSCA）通过学习从功率轨迹或AES执行的时机之间的相关性来从AE提取秘密键的能力。先前的工作集中在揭露的AES上，从同一设备收集了用于分析和测试的捕获的功率迹线，它们主要是在微控制器上实现的。在本文中，我们提出了一个全面的MLSCA，该MLSCA考虑了在涉及两个目标委员会的四个方案下，在软件和硬件上运行蒙面和未面积的AE，并使用侧向通道泄漏模型（Artix-7 XC7AI00T FPGA和STM32F415微控制器），以及用于培训的不同键并测试模型。我们的实现结果表明，支持向量机在掩盖软件上的其他机器学习技术的表现和仅具有4个轨迹的未掩盖软件AE。发现长期的短期内存网络在未掩盖的硬件AES（FPGA）上的其他技术的表现仅超过283个功率迹线。
Machine learning-based side-channel attacks (MLSCAs) have demonstrated the capability to extract secret keys from AES by learning the correlation between leakages from power traces or timing of AES execution. Previous work has focused on unmasked AES, the captured power traces for profiling and testing have been collected from the same device, and they are primarily implemented on microcontrollers. In this paper, we present a comprehensive MLSCA that considers both masked and unmasked AES running on software and hardware with a side-channel leakage model under four scenarios involving two target boards (Artix-7 XC7AI00T FPGAs and STM32F415 microcontrollers) and different keys for training and testing the model. Our implementation results indicate that support vector machines outperformed other machine learning techniques on masked software and unmasked software AES with only 4 traces. Long short-term memory networks were found to outperform other techniques on unmasked hardware AES (FPGA) with only 283 power traces.