Digital Twin-Empowered Smart Attack Detection System for 6G Edge of Things Networks

TL;DR

This paper presents a digital twin-based intelligent attack detection system for 6G Edge of Things networks, enhancing security through real-time monitoring, simulation, and online learning to proactively identify threats in expanding IoT environments.

Contribution

It introduces a novel digital twin-empowered security framework for 6G EoT networks, integrating real-time simulation and online learning for proactive attack detection.

Findings

Effective in real-time threat detection

Robust against diverse attack types

Adaptive to network changes

Abstract

As global Internet of Things (IoT) devices connectivity surges, a significant portion gravitates towards the Edge of Things (EoT) network. This shift prompts businesses to deploy infrastructure closer to end-users, enhancing accessibility. However, the growing EoT network expands the attack surface, necessitating robust and proactive security measures. Traditional solutions fall short against dynamic EoT threats, highlighting the need for proactive and intelligent systems. We introduce a digital twin-empowered smart attack detection system for 6G EoT networks. Leveraging digital twin and edge computing, it monitors and simulates physical assets in real time, enhancing security. An online learning module in the proposed system optimizes the network performance. Our system excels in proactive threat detection, ensuring 6G EoT network security. The performance evaluations demonstrate its effectiveness, robustness, and adaptability using real datasets.