Symmetric Cipher Design Using Recurrent Neural Networks
Maryam Arvandi, Shuxin Wu, Alireza Sadeghian, William Melek, Isaac Woungang · The 2006 IEEE International Joint Conference on Neural Network Proceedings · 2006
In this paper, a neural network-based symmetric cipher design methodology is proposed to provide high performance data encryption. The proposed approach is a novel attempt to apply the parallel processing capability of neural networks for cryptography purposes. By incorporating neural networks approach, the proposed cipher releases the constraint on the length of the secret key. The proposed cipher is robust in resisting different cryptanalysis attacks and provides efficient data integrity and authentication services. The design of the symmetric cipher is presented and its security is analyzed. Simulation results are presented to validate the effectiveness of the proposed cipher design.