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A Block Cipher Design Using Recurrent Neural Networks

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thesis
posted on 2021-05-24, 10:30 authored by Shuwei Wu

As security has become a necessary component for business applications in many areas, research  of new cryptography technology is desirable, especially the highly secure and efficient data encryption technique. A new block cipher  designed based on recurrent neural networks is proposed for  first time in the project. Recurrent neural networks have  dynamics characteristics and can express functions of time. By introducing recurrent neural networks to cryptography, the proposed block cipher releases the constraint on the length of secret key. The inherited high by parallel processing capability of neural networks can also improve the encryption performance greatly. The recurrent neural networks make the block cipher strong to resist different cryptanalysis attacks and to provide data integrity and authentication service at the same time. The design of the proposed block  cipher  is  presented and analyzed in detail. Simulation results provide illustrations. The proposed block cipher is flexible to be implemented either in software or in hardware for efficient data encryption purpose.

History

Language

eng

Degree

Master of Engineering

Program

Electrical and Computer Engineering

Granting Institution

Ryerson University

LAC Thesis Type

Thesis Project

Thesis Advisor

Alireza Sadeghian