  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.
