论文标题
SCAUL：无监督学习的功率侧通道分析
SCAUL: Power Side-Channel Analysis with Unsupervised Learning
论文作者
论文摘要
现有的功率分析技术依赖于具有泄漏或培训数据的先验知识的强大对手模型。我们通过无监督的学习（SCAUL）介绍了侧向通道分析，该分析可以恢复秘密密钥而无需先验知识或分析（培训）。我们采用LSTM自动编码器从具有高互信息的功率轨迹中提取具有测量数据依赖性样本的功率轨迹。我们证明，通过在经典的DPA攻击中用自动编码器特征替换原始测量值，就钥匙恢复所需的测量数量而言，效率提高了10倍。此外，我们采用这些功能来确定具有灵敏度分析和多层感知器（MLP）网络的泄漏模型。 SCAUL使用自动编码器功能和以无监督方法获得的泄漏模型来找到正确的键。在ARTIX-7 FPGA上AES轻巧实施时，我们表明SCAUL能够使用3700个功率测量结果恢复正确的键，而DPA攻击至少需要17400个测量值。使用未确定的迹线，不确定性等于硬件时钟周期的20 \％，SCAUL能够通过12300测量恢复秘密键，而DPA攻击未能检测到键。
Existing power analysis techniques rely on strong adversary models with prior knowledge of the leakage or training data. We introduce side-channel analysis with unsupervised learning (SCAUL) that can recover the secret key without requiring prior knowledge or profiling (training). We employ an LSTM auto-encoder to extract features from power traces with high mutual information with the data-dependent samples of the measurements. We demonstrate that by replacing the raw measurements with the auto-encoder features in a classical DPA attack, the efficiency, in terms of required number of measurements for key recovery, improves by 10X. Further, we employ these features to identify a leakage model with sensitivity analysis and multi-layer perceptron (MLP) networks. SCAUL uses the auto-encoder features and the leakage model, obtained in an unsupervised approach, to find the correct key. On a lightweight implementation of AES on Artix-7 FPGA, we show that SCAUL is able to recover the correct key with 3700 power measurements with random plaintexts, while a DPA attack requires at least 17400 measurements. Using misaligned traces, with an uncertainty equal to 20\% of the hardware clock cycle, SCAUL is able to recover the secret key with 12300 measurements while the DPA attack fails to detect the key.
