Research Project

Collaborative Research: SaTC: CORE: Small: Towards Secure, Resilient, Privacy-enhancing Digital World Experiences

Project Information
- Award numbers: NSF SaTC-2350075
- Project period: 07/01/2024 – 06/30/2027
- Principal investigators: Dr. Anupam Das (PI), Dr. Yuchen Liu (Co-PI)
- Graduate students: Zifan Zhang, Zhijin Yang, Dongming Wu

Project Overview
- The intersection of virtual and physical realms gives rise to distinctive security concerns, encompassing inter-realm adversarial attacks creating counterfeit representations, the risk of information leakage across distributed virtual reality devices, and the exposure of user privacy at both the network and application levels. These less-recognized threats arise from the intricate interplay and added complexity of the digital realm, coupled with the reliance on cutting-edge technologies like digital mapping, machine learning, and data analysis in mixed environments. In response to these challenges, the project develops pioneering strategies to fortify the metaverse ecosystem, with the goal of creating secure, resilient, and privacy-enhanced digital world experiences. The project’s broader significance and importance span digital twin networks, manufacturing, and automation testing, where the developed security and privacy prevention techniques can be employed in various data analysis tasks, including medical data processing, road traffic prediction, user mobility, and trajectory predictions. The project integrates the research insights into new modules for computer security and privacy courses and hosts outreach activities like Data Privacy Week.

Publications
- Z. Zhang, M. Fang, D. Chen, X. Yang, and Y. Liu, ‘‘Synergizing AI and Digital Twins for Next-Generation Network Optimization, Forecasting, and Security’’, IEEE Wireless Communications (WCM), 2025.
- D. Wei, H. Yu, S. Mao, Y. Liu, and M. Chen, ‘‘Joint Optimization of Communication and Device Clustering for Secure Clustered Federated Learning’’, Proc. of IEEE International Conference on Communications (ICC), to appear, 2025.
- D. Wei, Q. Zhang, Y. Liu, M. Chen, ‘‘Joint Optimization of Model Splitting and Device Task Assignment for Private Multi-Hop Split Learning’’, 59th IEEE Annual Conference on Information Science and Systems (CISS), to appear, 2025
- D. Wu, Z. Peng, M. Chen, and Y. Liu, ‘‘Transforming Network Intrusion Detection Using Large Language Models’’, Proc. of IEEE Consumer Communications & Networking Conference (CCNC), to appear, 2025.
- Z. Zhang, M. Fang, M. Chen, G. Li, X. Lin, and Y. Liu, ‘‘Securing Distributed Network Digital Twin Systems Against Model Poisoning Attacks’’, IEEE Internet of Things Journal (IoT-J) [J], 2024.
- M. Fang, Z. Zhang, F. Hairi, P. Khanduri, J. Liu, S. Lu, Y. Liu, and N. Gong, ‘‘Toward Byzantine-Robust Decentralized Federated Learning’’, ACM Conference on Computer and Communications Security (CCS), 2024.

Tools and Platforms
- The interactive platform to dynamically engage users with visualization data, allowing in-depth exploration of data poisoning behavious in federated learning systems. Link
- The platform for enhancing network intrusion detection by integrating decision trees with large language models (LLMs) to improve attack detection, analysis, and interpretability. Link
- The repository for securing digital twins against various model poisoning attacks based on network traffic analysis. Link (to be announced)

Education, Outreach and Broader Impacts
- Organize Summer Residential Camp: Seeing through a Digital World, inspiring high-school students to explore the cyber threats and complex network simulations within the digital realm.