Evaluating Tabular Representation Learning for Network Intrusion Detection

ArXi:2605.02519v1 Announce Type: new Classic Network Intrusion Detection Systems (NIDS) often rely on manual feature engineering to extract meaningful patterns from network traffic data. However, this approach requires domain expertise and runs counter to the widely adopted principle of modern machine learning and neural networks: that models themselves should learn meaningful representations directly from data.