Web attacks remain the most common initial vector in modern incidents. Classic signature and rule-based defenses are necessary, but insufficient: they miss novel patterns, produce high noise, and struggle with complex, multi-step attacks. Neural networks — from autoencoders to graph neural networks and Transformers — bring a contextual, pattern-oriented layer that detects subtle anomalies across time, entities and relationships. When deployed thoughtfully (hybridized with rules, instrumented for explainability, and operated with retraining and feedback loops), NN-driven systems can significantly reduce mean time to detect (MTTD), lower analyst load, and cut false positives.
