A symmetric neural cryptographic key generation scheme for Iot security

The use of CMOS technology to generate neural session keys is presented in this research for incorporation with the Internet of Things (IoT) to improve security. Emerging technology advancements in the IoT era have enabled better tactics to exacerbate energy efficiency and security difficulties. Current safety solutions do not effectively address the security of IoT. Regarding IoT integration, a tiny logic area ASIC design of a re-keying enabled Triple Layer Vector-Valued Neural Network (TLVVNN) is presented utilizing CMOS designs with measurements of 65 and 130 nanometers. There hasn't been much study into optimizing the value of neural weights for faster neural synchronization. Harris' Hawks is used in this instance to optimize the neural network's weight vector for faster coordination. Once this process is completed, the synchronized weight becomes the session key. This method offers several advantages, namely (1) production of the session key by mutual neural synchronization over the public channel is one of the advantages of this technology. (2) It facilitates Hawks-based neural weight vector optimization for faster neural synchronization across public channels. (3) As per behavioral modeling, the synchronization duration might be reduced from 1.25 ms to less than 0.7 ms for a 20% weight imbalance in the re-keying phase. (4) Geometric, brute force, and majority attacks are all prohibited. Experiments to validate the suggested method's functionality are carried out, and the results show that the proposed approach outperforms current similar techniques in terms of efficiency.