On the use of recurrent neural networks to design symmetric ciphers
Maryam Arvandi, Shuxin Wu, Alireza Sadeghian · IEEE Computational Intelligence Magazine · 2008
In this article, we describe an innovative form of cipher design based on the use of recurrent neural networks. The well-known characteristics of neural networks, such as parallel distributed structure, high computational power, ability to learn and represent knowledge as a black box, are successfully applied to cryptography. The proposed cipher has a relatively simple architecture and, by incorporating neural networks, it releases the constraint on the length of the secret key. The design of the symmetric cipher is described in detail and its security is analyzed. The cipher is robust in resisting different cryptanalysis attacks and provides efficient data integrity and authentication services. Simulation results are presented to validate the effectiveness of the proposed cipher design.