Novel Empirical Block Chain Ecosystem with Deep Neural Key Exchange Technique

One of the key challenges faced by public networks is the establishment of a secure communication channel. Generally, authentication is accomplished by sharing a secret key for encrypting and decrypting the data by the sender and receiver. However, if an attacker has the ability to determine the secret key, then the data is no longer safe. Thus, effective techniques are required to improve the security of data transmitted in the Internet of Things (IoT) environment. This paper devises a Blockchain (BC) based Neural Key Exchange (NKE) for providing data protection while transferring information. The proposed system consists of various entities, like user, owner, server, BC, and Trusted Authority (TA). The introduced authentication approach is realized using different steps, such as initialization, registration, login, public key generation, authentication, data protection, validation, and data sharing. Further, the Deep Neural Network(DNN) is employed for generating the key to provide the data protection. The devised BC_DNN-SecKeyAuth technique is verified for its effectiveness considering different performance measures, like memory usage and computational cost, and the experimentation of the devised scheme reveals the superiority of the developed BC_DNN-SecKeyAuth scheme by attaining minimal values of memory usage and minimal computational cost at 125MB and 3.263sec.