Affiliation:
1. State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou, China
2. Information Engineering University, Zhengzhou, China
Abstract
Backpropagation neural network algorithms are one of the most widely used algorithms in the current neural network algorithm. It 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 each layer through the layer-by-layer backpropagation. The purpose of this paper is to simulate the decryption process of DES with backpropagation algorithm. By inputting a large number of plaintext and ciphertext pairs, a neural network simulator for the decryption of the target cipher is constructed, and the ciphertext given is decrypted. In this paper, how to modify the backpropagation neural network classifier and apply it to the process of building the regression analysis model is introduced in detail. The experimental results show that the final result of restoring plaintext of the neural network model built in this paper is ideal, and the fitting rate is higher than 90% compared with the true plaintext.
Subject
Computer Networks and Communications,Information Systems
Cited by
2 articles.
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1. Enhancing Cryptanalysis of DES Encryption using Neural Networks and Firefly Algorithms;2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques (EASCT);2023-10-20
2. Deep Learning-Based Cryptanalysis of a Simplified AES Cipher;International Journal of Information Security and Privacy;2022-05-20