Backpropagation neural network algorithms are one of the most widely used algorithms in the current neural network algorithm.\nIt uses the output error rate to estimate the error rate of the direct front layer of the output layer, so that we can get the error rate of\neach layer through the layer-by-layer backpropagation.Thepurpose of this paper is to simulate the decryption process of DES with\nbackpropagation algorithm. By inputting a large number of plaintext and ciphertext pairs, a neural network simulator for the\ndecryption of the target cipher is constructed, and the ciphertext given is decrypted. In this paper, how to modify the backpropagation\nneural network classifier and apply it to the process of building the regression analysis model is introduced in detail.\nThe experimental results show that the final result of restoring plaintext of the neural network model built in this paper is ideal,\nand the fitting rate is higher than 90% compared with the true plaintext.
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