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Arabic text encryption using artificial neural networks
Author
Source
Engineering and Technology Journal
Issue
Vol. 34, Issue 5A (31 May. 2016), pp.887-899, 13 p.
Publisher
Publication Date
2016-05-31
Country of Publication
Iraq
No. of Pages
13
Main Subjects
Information Technology and Computer Science
Arabic language and Literature
Topics
Abstract EN
This research aims to build a cipher system using back propagation Algorithm with artificial neural network to encrypt any Arabic text and to prevent any data attack during the transition process.
Encryption information holds four stages: 1) A neural network was trained by using back propagation algorithm to encrypt the whole input Arabic text and grasp final weights and consider these weights as a public key.
2) Training a second neural network by using back propagation algorithm to decrypt the input Arabic text of first stage and grasp weights and consider the weights as a private key.
3) Encrypt any Arabic text by using the weights obtained from first stage.
4) Decrypt the Arabic text from third stage by using the weights obtained from second stage.
The four stages are achieved prosperously for data encryption process and decryption.
This work is executed by using Matlab program version 7 and Notepad++ for writing text because it supports Arabic numbers under windows 7 as operating system.
American Psychological Association (APA)
Hamid, Adi Kamil. 2016. Arabic text encryption using artificial neural networks. Engineering and Technology Journal،Vol. 34, no. 5A, pp.887-899.
https://search.emarefa.net/detail/BIM-696351
Modern Language Association (MLA)
Hamid, Adi Kamil. Arabic text encryption using artificial neural networks. Engineering and Technology Journal Vol. 34, no. 5A (2016), pp.887-899.
https://search.emarefa.net/detail/BIM-696351
American Medical Association (AMA)
Hamid, Adi Kamil. Arabic text encryption using artificial neural networks. Engineering and Technology Journal. 2016. Vol. 34, no. 5A, pp.887-899.
https://search.emarefa.net/detail/BIM-696351
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 899
Record ID
BIM-696351