关键词: 深度学习, 侧信道攻击, 自注意力机制, 神经网络, 建模攻击

Abstract: Because deep learning can freely extract and combine features, an increasing number of academics are using it to perform sidechannel attacks without taking into consideration preprocessing processes like choosing sites of interest and alignment. The sidechannel attack model based on deep learning is built with multilayer perceptron networks, convolution neural networks, and recurrent neural networks, but it has several issues in the training stage, such as overfitting, gradient disappearance, and sluggish convergence speed. Meanwhile, the selfattention mechanism is capable of extracting characteristics in natural language processing, computer vision, and other domains. To make the selfattentiveness mechanism accessible to the area of deep learning sidechannel attacks, we present SADLSCA, a deep learning sidechannel attack model based on the selfattentiveness mechanism, based on the features of deep learningbased sidechannel attacks. SADLSCA addresses the issues of fast overfitting, gradient disappearance, and slow convergence of deep learningbased sidechannel attack models during training, and experimentally verifies that the energy traces required for a successful attack on public datasets ASCAD and CHES CTF 2018 are reduced by 23.1% and 41.7%, respectively.

Key words: deep learning, side channel attack, selfattention mechanism, neural network, modeling attack