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

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.

Empowering Precision Medicine: Unlocking Revolutionary Insights through Blockchain-Enabled Federated Learning and Electronic Medical Records

“…To demonstrate resistance to phishing attacks, we show that the proposed framework incorporates robust authentication and verification mechanisms to prevent unauthorized access and data breaches. The framework ensures that user credentials and sensitive information are securely transmitted and stored, minimizing the risk of phishing attacks [12].…”

“…Complying with these regulations can be burdensome, particularly when sharing data across international borders. Non-compliance can result in severe penalties, further deterring data sharing efforts [12].…”

A Survey of Machine Learning Assisted Continuous-Variable Quantum Key Distribution

“…While ML has shown potential to enhance several aspects of CV-QKD, its application needs to be carefully scrutinized with respect to the introduction of security vulnerabilities (e.g., [41,42]), where existing proofs may no longer hold. Finally, ML also has potential for QKD protocol selection (as was investigated in [65] for discrete-variable QKD) and could additionally be expanded to protocol optimization and design.…”