Publication Type : Conference Paper
Publisher : CCIS-Springer link
Source : International Conference on Security in Computer Networks Distributed Systems (SNDS-2012), Recent trends in Computer Network and Distributed System Security (CCIS-Springer link), Volume 335, IITM-K Techno park at Trivandrum, p.422-431 (2012)
Url : https://link.springer.com/chapter/10.1007/978-3-642-34135-9_41
ISBN : 9783642341359
Campus : Bengaluru
School : Department of Computer Science and Engineering, School of Engineering
Department : Computer Science
Year : 2012
Abstract : Neural cryptography is a new way to create shared secret key. It is based on synchronization of Tree Parity Machines (TPM) by mutual learning. Two neural networks trained on their mutual output bits synchronize to a state with identical time dependent weights. This has been used for creation of a secure cryptographic secret key using a public channel. In this paper a genetic approach has been used in the field of neural cryptography for synchronizing tree parity machines by mutual learning process. Here a best fit weight vector is found using a genetic algorithm and then the training process is done for the feed forward network. The proposed approach improves the process of synchronization.
Cite this Research Publication : S .Santhanalakshmi, Sudarshan, T. S. B., and Patra, G. K., “Neural Synchronization by Mutual Learning Using Genetic Approach for Secure Key Generation”, in International Conference on Security in Computer Networks Distributed Systems (SNDS-2012), IITM-K Techno park at Trivandrum, 2012, vol. 335, pp. 422-431.