Artificial Neural Synchronization Using Nature Inspired Whale Optimization

IEEE Access

Arindam Sarkar¹  Moirangthem Marjit Singh²  Subhendu Kumar Pani³  Mohammad Zubair Khan⁴  Abdulfattah Noorwali⁵  Chinmay Chakraborty⁶ 

[1] Department of Computer Science and Electronics, Ramakrishna Mission Vidyamandira, Howrah, India;Department of Computer Science and Engineering, North Eastern Regional Institute of Science and Technology, Nirjuli, India;Department of Computer Science and Engineering, Orissa Engineering College, Biju Patnaik University of Technology (BPUT), Odisha, India;Department of Computer Science, College of Computer Science and Engineering Taibah University, Madinah, Saudi Arabia;Department of Electrical Engineering, Umm Al-Qura University, Makkah, Saudi Arabia;Department of Electronics and Communication Engineering, Birla Institute of Technology at Mesra, Ranchi, India;

关键词: Neural synchronization;    tree parity machine (TPM);    session key;    neural network;    mutual learning;    double layer tree parity machine (DLTPM);   

DOI  :  10.1109/ACCESS.2021.3052884

来源: DOAJ

【 摘 要 】

In this article, a whale optimization-based neural synchronization has been proposed for the development of the key exchange protocol. At the time of exchange of sensitive information, intruders can effortlessly perform sniffing, spoofing, phishing, or Man-In-The-Middle (MITM) attack to tamper the vital information. Information needs to be secretly transmitted with high level of encryption by preserving the authentication, confidentiality, and integrity factors. Such stated requirements urge the researchers to develop a neural network-based fast and robust security protocol. A special neural network structure called Double Layer Tree Parity Machine (DLTPM) is proposed for neural synchronization. Two DLTPMs accept the common input and different weight vectors and update the weights using neural learning rules by exchanging their output. In some steps, it results in complete synchronization, and the weights of the two DLTMs become identical. These identical weights serve as a secret key. There is, however, hardly any research in the field of neural weight vector optimization using a nature-inspired algorithm for faster neural synchronization. In this article, whale optimization-based DLTPM is proposed. For faster synchronization, this proposed DLTPM model uses a whale algorithm optimized weight vector. This proposed DLTPM model is faster and has better security. This proposed technique has been passed through a series of parametric tests. The results have been compared with some recent techniques. The results of the proposed technique have shown effective and has robust potential.

【 授权许可】

Unknown