5G Specific Threats Evaluation using Dataset-Driven Approach in IIoT

Abstract

The advancement of fifth-generation (5G) technology is facilitating the expansion of the Internet of Things (IoT), which is becoming complicated when blended with industrial operations in terms of secured communication over 5G networks or imminent technologies. Industrial IoT (IIoT) applications in the 5G era provide massive connectivity with ultra-low latency and enhanced mobile broadband, leading to the inception and awareness of numerous 5G security challenges apart from various traditional security concerns. This work highlights the significance of using a dataset-driven methodology to study, discuss and evaluate various 5G network performance metrics of the derived augmented dataset in 5G-enabled IIoT network scenarios, which is achieved by integrating distinct 7 new features/labels and simulating various 5G-specific security attacks such as jamming attacks, network slicing exploits and service-based architecture (SBA) vulnerabilities, primarily on and over the ML-Edge-IIoTset dataset.