# 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. deep learning; side-channel analysis; multilayer perceptron; convolutional neural network; recurrent neural network; long short-term memory network +
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DPA [9], correlation energy analysis CPA [10], and mutual information analysis MIA [11] have been proposed successively. Unlike non-profiling attacks, profiling attacks such as template attacks [12] and random attacks [13] include two stages: analysis and learning of the training set and attack testing. By learning the statistical model, we can test the generalization ability of the analysis model. In recent years, there has been a rapid development of deep learning [14], a branch of machine learning that takes neural networks as its basic architecture, which can be used as a universal tool for data representation learning. At the same time, it is also a representative technology leading the third revival of artificial intelligence. [PERSON] et al. [15] combined deep learning with SCA for the first time and studied the application of deep learning models such as MLP and CNN in SCA. Then, the research on SCA based on deep learning became a research hotspot [16; 17; 18]. [PERSON] et al. [19] applied a dendrite net to SCA and proposed a side-channel hardware encryption analysis method based on the dendrite net, which has a better analysis effect than MLP, CNN, and RNN. It is of great significance to study the impact of different models on attack results and the exact relationship between power traces and median values. [PERSON] et al. [20] proposed a Template Analysis Pre-trained DL Classification model named TAPDC based on convolutional neural networks and autoencoders. The TAPDC model completes the conversion of power traces to the intermediate value by detecting the periodicity of the power traces and mines deeper features through a multi-layer convolutional network. Using the same dataset to train the three network models of MLP, CNN, and TAPDC respectively, the results show that TAPDC greatly improves the efficiency of energy consumption attacks. [PERSON] et al. [21] proposed a side-channel analysis method based on a combination of embedded and LSTM by first using word embeddings to replace points of interest (POIs) and dimensionality reduction, and the power traces are transformed into a sensitivity vector by vectorizing the power values. Second, the sensitive vectors are combined with long short-term memory (LSTM) to perform SCA based on FPGA cryptographic implementation.