论文标题
使用侧向通道泄漏和深层神经网络分析拆卸攻击的数据集生成框架
A Dataset Generation Framework for profiling Disassembly attacks using Side-Channel Leakages and Deep Neural Networks
论文作者
论文摘要
侧通道攻击之间的各种研究试图通过电子设备的泄漏来提取信息，以达到某些设备的指示流。但是，以前的方法在很大程度上取决于追踪数据的分辨率。在实际攻击场景中，获得低噪声痕迹并不总是可行的。这项研究提出了两个深层模型，以从侧通道轨迹中提取低水平和高级特征，并将其分类为相关指令。我们旨在通过神经网络对低分辨率数据进行侧向通道攻击对低分辨率数据的准确性。正如提示的那样，实际程序中的指示流是可以预测的，并且遵循特定的分布。这导致提出一个LSTM模型来估计这些分布，这可以加快反向工程过程并提高准确性。提出的泄漏分类模型平均达到54.58％的精度，并且在我们的数据集中胜过其他现有方法。此外，LSTM模型达到94.39％的准确性，以预测加密算法的标准实现。
Various studies among side-channel attacks have tried to extract information through leakages from electronic devices to reach the instruction flow of some appliances. However, previous methods highly depend on the resolution of traced data. Obtaining low-noise traces is not always feasible in real attack scenarios. This study proposes two deep models to extract low and high-level features from side-channel traces and classify them to related instructions. We aim to evaluate the accuracy of a side-channel attack on low-resolution data with a more robust feature extractor thanks to neural networks. As inves-tigated, instruction flow in real programs is predictable and follows specific distributions. This leads to proposing a LSTM model to estimate these distributions, which could expedite the reverse engineering process and also raise the accuracy. The proposed model for leakage classification reaches 54.58% accuracy on average and outperforms other existing methods on our datasets. Also, LSTM model reaches 94.39% accuracy for instruction prediction on standard implementation of cryptographic algorithms.
