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Analysis of DES Plaintext Recovery Based on BP Neural Network
Joint Authors
Source
Security and Communication Networks
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-5, 5 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-11-11
Country of Publication
Egypt
No. of Pages
5
Main Subjects
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Fan, Sijie& Zhao, Yaqun. 2019. Analysis of DES Plaintext Recovery Based on BP Neural Network. Security and Communication Networks،Vol. 2019, no. 2019, pp.1-5.
https://search.emarefa.net/detail/BIM-1210653
Modern Language Association (MLA)
Fan, Sijie& Zhao, Yaqun. Analysis of DES Plaintext Recovery Based on BP Neural Network. Security and Communication Networks No. 2019 (2019), pp.1-5.
https://search.emarefa.net/detail/BIM-1210653
American Medical Association (AMA)
Fan, Sijie& Zhao, Yaqun. Analysis of DES Plaintext Recovery Based on BP Neural Network. Security and Communication Networks. 2019. Vol. 2019, no. 2019, pp.1-5.
https://search.emarefa.net/detail/BIM-1210653
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1210653