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A Hybrid Connectionist/Substitution Approach for Data Encryption

, Muhammed Jassem Al-Muhammed
Published in IGI Global
2020
Pages: 23 - 42
Abstract

The authors believe that the hybridization of two different approaches results in more complex encryption outcomes. The proposed method combines a symbolic approach, which is a table substitution method, with another paradigm that models real-life neurons (connectionist approach). This hybrid model is compact, nonlinear, and parallel. The neural network approach focuses on generating keys (weights) based on a feedforward neural network architecture that works as a mirror. The weights are used as an input for the substitution method. The hybrid model is verified and validated as a successful encryption method.

About the journal
JournalImplementing Computational Intelligence Techniques for Security Systems Design
PublisherIGI Global
Open AccessNo