Using Generalization and Characterization Techniques in the Anomaly-based Detection of Web Attacks

The custom, ad hoc nature of web applications makes learning-based anomaly detection systems a suitable approach to provide early warning about the exploitation of novel vulnerabilities. However, anomaly-based systems are known for producing a large number of false positives and for providing poor or non-existent information about the type of attack that is associated with