Network Intrusion Detection

Network intrusion detection studies how to identify attacks and suspicious behavior from network flows, packet sequences, and related telemetry, often without inspecting encrypted payloads. Common approaches include supervised flow classifiers such as random forests and gradient boosting, anomaly detectors such as autoencoders, and temporal or graph models; federated learning lets organizations collaborate without sharing raw traffic. Researchers also explore detecting unseen attacks, explaining alerts, and resisting adversarial evasion, while adapting models to settings such as IoT, industrial control, and vehicle networks. A central challenge is that high benchmark scores may not transfer across networks or time, and reliable deployment must balance rare-attack detection against false alarms, latency, privacy, and resource limits.