Robust and Privacy-Aware Network Flow Watermarking for Digital Twin Communications in 5G/6G Edge Environments

Digital Twin (DT) is gradually becoming the core of future communication infrastructure and has been widely applied in key areas such as intelligent transportation, manufacturing, and healthcare. However, with the deep integration of 5G/6G and edge computing, real-time communication in DT systems faces significant cybersecurity threats and data privacy challenges. To address this issue, this paper proposes a robust and privacy-aware network watermarking framework (RPAW) for edge environments, designed for tracing communication paths and enabling real-time detection of data leakage. The framework innovatively combines time-domain and sequence-domain features to form a hybrid-domain embedding strategy, ensuring low latency and high concealment. To tackle the synchronization loss problem caused by time disturbances, we develop an adaptive synchronization mechanism based on the sequence domain, enabling stable watermark extraction in any network flow segment. Additionally, we design an intelligent watermark extraction model based on TabNet, significantly enhancing robustness under packet loss and delay jitter conditions. Extensive experiments demonstrate that the RPAW excels in terms of concealment and extraction robustness, and effectively supports secure and traceable data exchange in edge DT communication scenarios, offering a new approach to building a trustworthy DT communication ecosystem.