An Improved Method to Evaluate the Synchronization in Neural Key Exchange Protocol

• Published in: Security and communication networks Vol. 2020; no. 2020; pp. 1 - 10
• Main Authors: Zhu, Shuai Shuai, Li, Zhe, Li, Yu, Han, Yi Liang
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 2020; Hindawi; Hindawi Limited
• Subjects: Communication; Cooperation; Cryptography; Efficiency; Evaluation; Exchanging; Genetic algorithms; Neural networks; Protocol; Security; Synchronism; Weight
• DOI: 10.1155/2020/8869688
• ISSN: 1939-0114

The synchronization between two neural networks by mutual learning can be used to design the neural key exchange protocol. The critical issue is how to evaluate the synchronization without a weight vector. All existing methods have a delay in evaluating the synchronization, which affects the security of the neural key exchange. To evaluate the full synchronization of neural networks more timely and accurately, an improved method for evaluating the synchronization is proposed. First, the frequency that the two networks have the same output in previous steps is used for assessing the degree of them roughly. Second, the hash function is utilized to judge whether the two networks have achieved full synchronization precisely when the degree exceeds a given threshold. The improved method can find the full synchronization between two networks with no information other than the hash value of the weight vector. Compared with other methods, the full synchronization can be detected earlier by two communication partners which adopt the method proposed in this paper. As a result, the successful probability of geometric is reduced. Therefore, the proposed method can enhance the security of the neural exchange protocol.