UniNet: A Unified Multi-Granular Traffic Modeling Framework for Network Security

As modern networks grow increasingly complex– driven by diverse devices, encrypted protocols, and evolving threats–network traffic analysis has become critically important. Existing machine learning models often rely only on a single representation of packets or flows, limiting their ability to capture the contextual relationships essential for robust analysis. Furthermore, task-specific architectures for supervised, semisupervised, and unsupervised learning lead to inefficiencies in adapting to varying data formats and security tasks. To address these gaps, we propose UniNet, a unified framework that introduces a novel multi-granular traffic representation (TMatrix) with rich contextual information, integrating session, flow, and packet-level features to provide comprehensive contextual information. Combined with T-Attent, a specially designed lightweight attention-based model, UniNet efficiently learns latent embeddings for diverse security tasks. Extensive evaluations across four key network security and privacy problems–anomaly detection, attack classification, IoT device identification, and encrypted website fingerprinting–demonstrate UniNet’s significant performance gain over state-of-the-art methods, achieving higher accuracy, lower false positive rates, and improved scalability across various datasets. By addressing the limitations of singlelevel models and unifying traffic analysis paradigms, UniNet sets a new benchmark for modern network security.