Secured communication using efficient artificial neural synchronization

In this paper, an efficient artificial neural group synchronization is proposed for secured neural key exchange over public channels. To share the key over a public network, two Artificial Neural Networks (ANNs) are coordinated by mutual learning. The primary issue of neural coordination is assessing the synchronization of two parties' ANNs in the absence of weights from the other. There is a delay in coordination measurement in existing techniques, which affects the confidentiality of neural coordination. Furthermore, research into the mutual learning of a cluster of ANNs is limited. This paper introduces a mutual learning methodology for measuring the entire synchronization of the set of ANNs quickly and efficiently. The measure of coordination is determined by the frequency with which the two networks have had the same outcome in prior rounds. When a particular threshold is reached, the hash is used to decide whether all networks are properly coordinated. The modified methodology uses has value of the weight vectors to achieve full coordination between two communicating entities. This technique has several advantages, including (1) Generation of session key via complete binary tree-based group mutual neural synchronization of ANNs over the public channel. (2) Unlike existing methods, the suggested method allows two communication entities to recognize full coordination faster. (3) Brute force, geometric, impersonation, and majority attacks are all considered in this proposed scheme. Tests to validate the performance of the proposed methodology are carried out, and the results show that the proposed methodology outperforms similar approaches already in use.