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.

翻译：随着全球物联网（IoT）设备连接数的激增，大部分设备正趋向于边缘物联（EoT）网络。这一趋势促使企业将基础设施部署到更接近最终用户的位置，以提升可访问性。然而，不断扩展的EoT网络也扩大了攻击面，亟需稳健且主动的安全防护措施。传统解决方案难以应对动态变化的EoT威胁，凸显了对主动智能系统的需求。我们提出了一种面向6G EoT网络的数字孪生赋能智能攻击检测系统。该系统利用数字孪生与边缘计算技术，实时监控并模拟物理资产，从而增强安全性。系统内置的在线学习模块可优化网络性能。本系统在主动威胁检测方面表现卓越，为6G EoT网络安全提供保障。基于真实数据集的性能评估验证了其有效性、鲁棒性与自适应性。