Network Intrusion Detection System (NIDS) using Few-Shot Learning

A Siamese few-shot model for spotting zero-day attacks when labelled data is thin. Trained on paired traffic samples, evaluated with 5-fold cross-validation across five held-out attack classes. Hit 94 percent detection accuracy, beat an XGBoost supervised baseline by 11 points, and showed clean separation between benign flows and malicious patterns in the learned embedding.