Detecting Attackers during Quantum Key Distribution in IoT Networks using Neural Networks

Abstract

Internet of Things (IoT) deployments face significant security challenges due to the limited energy and computational power of IoT devices. These challenges are more serious in the quantum communications era, where certain attackers might have quantum computing capabilities, which renders IoT devices more vulnerable. This paper addresses the problem of IoT security by investigating quantum key distribution (QKD) in beyond 5G networks. An architecture for implementing QKD in beyond 5G IoT networks is proposed, offloading the heavy computational tasks to IoT controllers, while considering the use case of sensors deployed in railroad networks. Neural Network (NN) techniques are proposed in order to detect the presence of an attacker during QKD without the need to disrupt the key distribution process. The results show that the proposed techniques can reach 99% accuracy.