Quantum Key Distribution Protocol Selector Based on Machine Learning for Next-Generation Networks

Author:

Okey Ogobuchi DanielORCID,Maidin Siti SarahORCID,Lopes Rosa RenataORCID,Toor Waqas TariqORCID,Carrillo Melgarejo DickORCID,Wuttisittikulkij LunchakornORCID,Saadi MuhammadORCID,Zegarra Rodríguez DemóstenesORCID

Abstract

In next-generation networks, including the sixth generation (6G), a large number of computing devices can communicate with ultra-low latency. By implication, 6G capabilities present a massive benefit for the Internet of Things (IoT), considering a wide range of application domains. However, some security concerns in the IoT involving authentication and encryption protocols are currently under investigation. Thus, mechanisms implementing quantum communications in IoT devices have been explored to offer improved security. Algorithmic solutions that enable better quantum key distribution (QKD) selection for authentication and encryption have been developed, but having limited performance considering time requirements. Therefore, a new approach for selecting the best QKD protocol based on a Deep Convolutional Neural Network model, called Tree-CNN, is proposed using the Tanh Exponential Activation Function (TanhExp) that enables IoT devices to handle more secure quantum communications using the 6G network infrastructure. The proposed model is developed, and its performance is compared with classical Convolutional Neural Networks (CNN) and other machine learning methods. The results obtained are superior to the related works, with an Area Under the Curve (AUC) of 99.89% during testing and a time-cost performance of 0.65 s for predicting the best QKD protocol. In addition, we tested our proposal using different transmission distances and three QKD protocols to demonstrate that the prediction and actual results reached similar values. Hence, our proposed model obtained a fast, reliable, and precise solution to solve the challenges of performance and time consumption in selecting the best QKD protocol.

Funder

the INTI International University, Negeri Sembilan, Malaysia

the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Brazil

Publisher

MDPI AG

Subject

Management, Monitoring, Policy and Law,Renewable Energy, Sustainability and the Environment,Geography, Planning and Development,Building and Construction

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5. Harnessing Quantum Systems for Simulating Complex Physical Phenomena;2024 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE);2024-01-24

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