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

arXiv: https://arxiv.org/abs/2310.03554 · HTML: https://mlsanity.com/paper/2310.03554/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Oct 2023.

## Built on by (most cited first, 4 of 4)

- [Review of Generative AI Methods in Cybersecurity](https://mlsanity.com/paper/2403.08701/) (2024)
- [LLM-Twin: Mini-Giant Model-driven Beyond 5G Digital Twin Networking Framework with Semantic Secure Communication and Computation](https://mlsanity.com/paper/2312.10631/) (2023)
- [A Survey on Cloud-Edge-Terminal Collaborative Intelligence in AIoT Networks](https://mlsanity.com/paper/2508.18803/) (2025)
- [Global Spatio-Temporal Fusion-based Traffic Prediction Algorithm with Anomaly Aware](https://mlsanity.com/paper/2412.14569/) (2024)

## Abstract (arXiv)

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.