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

Ogobuchi Daniel Okey, Siti Sarah Maidin (), Renata Lopes Rosa, Waqas Tariq Toor, Dick Carrillo Melgarejo, Lunchakorn Wuttisittikulkij, Muhammad Saadi and Demóstenes Zegarra Rodríguez ()
Additional contact information
Ogobuchi Daniel Okey: Department of System and Automation Engineering, Federal University of Lavras, Minas Gerais 37203-202, Brazil
Siti Sarah Maidin: Faculty of Data Science and Information Technology (FDSIT), INTI International University, Nilai 71800, Malaysia
Renata Lopes Rosa: Department of Computer Science, Federal University of Lavras, Minas Gerais 37200-000, Brazil
Waqas Tariq Toor: Department of Electrical Engineering, University of Engineering and Technology, Lahore 54000, Pakistan
Dick Carrillo Melgarejo: Department of Electrical Engineering, School of Energy Systems, Lappeenranta-Lahti University of Technology, 53850 Lappeenranta, Finland
Lunchakorn Wuttisittikulkij: Department of Electrical Engineering, Wireless Communication Ecosystem Research Unit, Chulalongkorn University, Bangkok 10900, Thailand
Muhammad Saadi: Department of Electrical Engineering, University of Central Punjab, Lahore 54000, Pakistan
Demóstenes Zegarra Rodríguez: Department of System and Automation Engineering, Federal University of Lavras, Minas Gerais 37203-202, Brazil

Sustainability, 2022, vol. 14, issue 23, 1-15

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.

Keywords: internet of things; quantum communications; deep convolutional neural network; quantum security; 6G networks (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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