Dual-Branch Multimodal Deep Learning Framework for Encrypted Traffic Analysis and Zero-Day Anomaly Detection in Next-Generation Networks

This paper presents a dual-branch multimodal deep learning framework for encrypted traffic analysis and zero-day anomaly detection. The framework combines temporal packet dynamics and statistical flow features without requiring traffic decryption, with SHAP-based explainability for transparent security analysis.