Uncertainty-Aware Intrusion Detection: Selective Reclassification Using Error-Driven Auxiliary Models
Modern intrusion detection systems (IDS) increasingly rely on machine learning and deep learning models to identify complex attack patterns in network traffic. However, such models often produce uncertain predictions under class imbalance, overlapping feature distributions, and evolving attack behaviors. This paper pro...