Research on Side-Channel Analysis Based on Deep Learning with Different Sample Data

Abstract

With the in-depth integration of deep learning and side-channel analysis (SCA) technology, the security threats faced by embedded devices based on the Internet of Things (IoT) have become increasingly prominent. By building a neural network model as a discriminator, the correlation between the side information leaked by the cryptographic device, the key of the cryptographic algorithm, and other sensitive data can be explored. Then, the security of cryptographic products can be evaluated and analyzed. For the AES-128 cryptographic algorithm, combined with the CW308T-STM32F3 demo board on the ChipWhisperer experimental platform, a Correlation Power Analysis (CPA) is performed using the four most common deep learning methods: the multilayer perceptron (MLP), the convolutional neural network (CNN), the recurrent neural network (RNN), and the long short-term memory network (LSTM) model. The performance of each model is analyzed in turn when the samples are small data sets, sufficient data sets, and data sets of different scales. Finally, each model is comprehensively evaluated by indicators such as classifier accuracy, network loss, training time, and rank of side-channel attacks. The experimental results show that the convolutional neural network CNN classifier has higher accuracy, lower loss, better robustness, stronger generalization ability, and shorter training time. The rank value is 2, that is, only two traces can recover the correct key byte information. The comprehensive performance effect is better.