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Companies, public services and other institutions are increasingly turning to web-based applications, but attacks are increasing in both number and variance.
Previous approaches to avoid attacks by using web application firewalls rely primarily on pattern-based detection. This document evaluates if and which machine learning methods can be used to reliably detect web-based attacks. Classifiers such as Support Vector Machines, Neural Networks, Naïve Bayes, Decision Trees and Logistic Regression are used.
Furthermore possible use cases and visualizations of the decisions are suggested.