Zero-Day Guardian: A Dual Model Enabled Federated Learning Framework for Handling Zero-Day Attacks in 5G Enabled IIoT

Abstract

5G emerges as the bedrock for the Industrial Internet of Things (IIoT), it facilitates the seamless, low-latency fusion of artificial intelligence and cloud computing, thereby fortifying the entire industrial procedure within a framework of smart and intelligent IIoT ecosystems. Concurrently, the continuously changing landscape of cybersecurity threats in the realm of the Internet of Things (IoT) is giving rise to unparalleled security complexities. These challenges are particularly pronounced in the context of zero-day attacks, and integration of 5G technology further exacerbates the intricacy of the situation. Thus this paper introduces a cutting-edge 5G-enabled framework for cyberthreat detection leveraging Federated Learning (FL) without the need for data sharing. It employs a dual Autoencoder (AE) based model. Distinctly, our model utilizes two synchronized AEs for each client, integral to FL mechanism. While one AE evaluates the IIoT environment based on normal network patterns, another focuses on attack scenarios. For decisive threat assessment, the system uses the capabilities of a one-class SVM classifier with AEs. Furthermore, our method ensures a synergistic blend of selflearning and collaborative learning by implementing a polling mechanism between overarching AE classifier and those tailored to individual client data and counters zero-day threats and out performs traditional AI/ML techniques.