EM attack on AES cryptography chip based on deep residual neural network

An electromagnetic attack method is proposed in the case of completely unknown plaintext, cipher text and leakage intermediate value. In this paper, a deep residual neural network model is designed to achieve electromagnetic attack on advanced encryption standard(AES) encryption algorithm based on field programmable gate array(FPGA). The model consists of two parts, the data expansion layer and the depth residual layer. The data expansion layer extends the electromagnetic signal data from one dimension to two dimensions, effectively reducing the training difficulty of the model. The deep residual layer is a deep neural network based on residual blocks, effectively solving the problems of the difficulties of deep network convergence and difficulty in tuning. The experimental results show that the final two bits of the key can be obtained only through the collected electromagnetic leakage signals, and the accuracy rate is 91.8%. Under the same conditions, the accuracy of the model is nearly 8% higher than that of the support vector machine(SVM) model.