Intelligent and adaptable security monitoring frameworks with real-time threat prediction are required due to the growing complexity of contemporary network infrastructures and the quick evolution of cyber threats. The Adaptive Transformer-Based Deep Learning Framework (ATDLF) for predictive security analytics and intelligent dynamic network traffic monitoring is presented in this research. The suggested design captures both short-term and long-term interdependence in network traffic patterns by combining an adaptive attribute refinement module with a multi-head self-attention transformer encoder. A Graph Attention Network (GAT)-based classifier is used to simulate intricate connections between network traffic constituents and boost cyber-attack classification performance in order to improve attack discrimination. Moreover, a dynamic risk prediction layer is added to predict the future risks as per the different traffic flow. To deal with idea drift and new assault patterns, the system uses continuous learning techniques and adaptive weight optimization. Experimental evaluation on benchmark intrusion detection datasets shows better performance in comparison with traditional methods. The solution proposed is a viable solution to proactive threat intelligence systems and next-generation intelligent cybersecurity monitoring.
