@article{zhang_liu_peng_chen_xu_cui_2024, title={Digital Twin-Assisted Data-Driven Optimization for Reliable Edge Caching in Wireless Networks}, volume={42}, ISSN={["1558-0008"]}, url={https://doi.org/10.1109/JSAC.2024.3431575}, DOI={10.1109/JSAC.2024.3431575}, abstractNote={Optimizing edge caching is crucial for the advancement of next-generation (nextG) wireless networks, ensuring high-speed and low-latency services for mobile users. Existing data-driven optimization approaches often lack awareness of the distribution of random data variables and focus solely on optimizing cache hit rates, neglecting potential reliability concerns, such as base station overload and unbalanced cache issues. This oversight can result in system crashes and degraded user experience. To bridge this gap, we introduce a novel digital twin-assisted optimization framework, called D-REC, which integrates reinforcement learning (RL) with diverse intervention modules to ensure reliable caching in nextG wireless networks. We first develop a joint vertical and horizontal twinning approach to efficiently create network digital twins, which are then employed by D-REC as RL optimizers and safeguards, providing ample datasets for training and predictive evaluation of our cache replacement policy. By incorporating reliability modules into a constrained Markov decision process, D-REC can adaptively adjust actions, rewards, and states to comply with advantageous constraints, minimizing the risk of network failures. Theoretical analysis demonstrates comparable convergence rates between D-REC and vanilla data-driven methods without compromising caching performance. Extensive experiments validate that D-REC outperforms conventional approaches in cache hit rate and load balancing while effectively enforcing predetermined reliability intervention modules.}, number={11}, journal={IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS}, author={Zhang, Zifan and Liu, Yuchen and Peng, Zhiyuan and Chen, Mingzhe and Xu, Dongkuan and Cui, Shuguang}, year={2024}, month={Nov}, pages={3306–3320} }
 @article{zhang_fang_chen_li_lin_liu_2024, title={Securing Distributed Network Digital Twin Systems Against Model Poisoning Attacks}, volume={11}, ISSN={["2327-4662"]}, url={https://doi.org/10.1109/JIOT.2024.3421895}, DOI={10.1109/JIOT.2024.3421895}, abstractNote={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 (NDT) 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 NDT 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 data sets, 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.}, number={21}, journal={IEEE INTERNET OF THINGS JOURNAL}, author={Zhang, Zifan and Fang, Minghong and Chen, Mingzhe and Li, Gaolei and Lin, Xi and Liu, Yuchen}, year={2024}, month={Nov}, pages={34312–34324} }