Cryptography depends on two components, an algorithm and a key. Keys are used for encryption of information as well as in other cryptographic schemes such as digital signature and message authentication codes. Neural cryptography is a way to create shared secret key. Key generation in Tree Parity Machine neural network is done by mutual learning. Neural networks receive common inputs to synchronize using a suitable learning rule. Because of this effect neural synchronization can be used to construct a cryptographic key-exchange protocol. Faster synchronization of the neural network has been achieved by generating the optimal weights for the sender and receiver from a genetic process. In this paper the performance of the genetic algorithm has been analysed by varying the neural network and genetic parameters.
Dr. S. Santhanalakshmi, K., S., and Patra, G. K., “Analysis of Neural Synchronization Using Genetic Approach for Secure Key Generation”, in Security in Computing and Communications, Cham, 2015.