Towards IoT Node Authentication Mechanism in Next Generation Networks

Although the next generation networks (5G-NGNs) provide a flexible infrastructure to support latency-sensitive and bandwidth-hungry mission-critical Internet of Things (IoT) applications, however, the 5G-IoT integration in NGNs has increased the threat surface. Unfortunately, IoT devices are resource constrained, and the traditional Intrusion Detection Systems (IDS) approaches based on cryptography are not effective on 5G-IoT ecosystems. In this paper, we propose an effective 5GIoT node authentication approach that leverages unique RF (Radio Frequency) fingerprinting data to train the Deep learning model to detect legitimate and non-legitimate IoT nodes. Our approach is based on Mahalanobis Distance theory and Chisquare distribution theories. The proposed approach achieves a higher detection accuracy (99.35%) as well as lower training time compared to other existing approaches which is a key benefit of our approach in NGNs. The experiments are conducted using ETSI-Open Source NFV Management and Orchestration (OSM-MANO) platform on AWS (Amazon Web Services) cloud platform to verify how the proposed approach would fit in reallife scenarios. The method can be used as a standalone security system or as a part of multi-factor authentication.