In the era of 5G and beyond, the increasing complexity of wireless networks necessitates innovative frameworks for efficient management and deployment. Digital twins (DTs), embodying real-time monitoring, predictive configurations, and enhanced decision-making capabilities, stand out as a promising solution in this context. Within a time-series data-driven framework that effectively maps wireless networks into digital counterparts, encapsulated by integrated vertical and horizontal twinning phases, this study investigates the security challenges in distributed network DT systems, which potentially undermine the reliability of subsequent network applications such as wireless traffic forecasting. Specifically, we consider a minimal-knowledge scenario for all attackers, in that they do not have access to network data and other specialized knowledge, yet can interact with previous iterations of server-level models. In this context, we spotlight a novel fake traffic injection attack designed to compromise a distributed network DT system for wireless traffic prediction. In response, we then propose a defense mechanism, termed global-local inconsistency detection (GLID), to counteract various model poisoning threats. GLID strategically removes abnormal model parameters that deviate beyond a particular percentile range, thereby fortifying the security of network twinning process. Through extensive experiments on real-world wireless traffic datasets, our experimental evaluations show that both our attack and defense strategies significantly outperform existing baselines, highlighting the importance of security measures in the design and implementation of DTs for 5G and beyond network systems.

中文翻译：

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确保分布式网络数字孪生系统免受模型中毒攻击


在 5G 及以后的时代，无线网络的复杂性日益增加，需要创新的框架来实现高效的管理和部署。数字孪生 (DT) 体现了实时监控、预测配置和增强的决策能力，在这方面脱颖而出，成为一种有前景的解决方案。在一个时间序列数据驱动的框架内，该框架有效地将无线网络映射到数字对应部分，并由集成的垂直和水平孪生阶段封装，本研究调查了分布式网络DT系统中的安全挑战，这可能会破坏后续网络应用程序的可靠性，例如无线流量预测。具体来说，我们考虑了所有攻击者的最小知识场景，因为他们无法访问网络数据和其他专业知识，但可以与服务器级模型的先前迭代进行交互。在此背景下，我们重点关注一种新颖的虚假流量注入攻击，旨在破坏用于无线流量预测的分布式网络 DT 系统。作为回应，我们提出了一种称为全局局部不一致检测（GLID）的防御机制，以应对各种模型中毒威胁。 GLID策略性地删除偏离特定百分位范围的异常模型参数，从而增强网络孪生过程的安全性。通过对真实世界无线流量数据集的广泛实验，我们的实验评估表明，我们的攻击和防御策略都显着优于现有基线，凸显了安全措施在 5G 及其他网络系统 DT 设计和实施中的重要性。