Deep Learning Guided Double Hidden Layer Neural Synchronization Through Mutual Learning

In this paper, a Double Hidden Layer Neural Networks synchronization mechanism using Generative Adversarial Network (GAN) and mutual learning is used for the development of the cryptographic key exchange protocol. This protocol is used to exchange sensitive information through a public channel. At the time of exchanging sensitive information, an intruder can easily attack the vital information/signals by sniffing, spoofing, phishing, or Man-In-The-Middle attack. There is, however, hardly some investigation to investigate the randomness of the common input vector in the synchronization of two neural networks. This proposed technique provides GAN generated random input vectors for neural synchronization which are very sensitive to the seed value. To enhance the security of the synchronization process, GAN generates the best random sequence of the input vector. Two neural networks use GAN generated input vector and different random weight vector and swap their output. In some steps, it results in complete synchronization by setting the discrete weights according to the common learning rule. The synchronized weight vector serves as a session key at the end of the neural synchronization process. An increase in the weight range increases the complexity of a successful attack exponentially but the effort to build the neural key decreases over the polynomial time. The proposed technique offers synchronization and authentication steps in parallel. It is difficult for the attacker to distinguish between synchronization and authentication steps. This proposed technique has been passed through different parametric tests. The results have shown effective and robust potential. Simulations of the process show effectiveness in terms of cited results in the paper.