Non-profiled Deep-Learning-Based Power Analysis of the SM4 and DES Algorithms

Power analysis methods are commonly used for evaluating the security of cryptographic devices. They are characteristically low-cost and display a high success rate and the ability to obtain important device information, e.g., keys. Given the current wide application of deep-learning technology, there is a growing tendency to incorporate power-analysis technology in development. This study investigates non-profiled deep-learning-based power analysis. The labels used in this attack are uncertain, and the attack conditions required are greatly reduced. We choose the Recurrent neural network (RNN), multilayer perceptron, and convolutional neural network algorithms, which use the same network structure, to recover the keys for the SM4 software and DES hardware implementations. We propose combining the RNN algorithm with power analysis, and validate the benefits experimentally. The experimental results show that they all successfully recover the correct key for the SM4 software implementation, although the RNN algorithm by itself achieves a better effect. This conclusion also applies to attacks on the DES hardware implementation but is limited to labels based on the bit model.