Machine Learning based Web Application Firewalls

  • 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.

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Metadaten
Author:Dennis Uckel
Referee:Roland Müller
Advisor:Markus Löcher
Document Type:Master's Thesis
Language:English
Date of first Publication:2019/09/03
Publishing Institution:Hochschulbibliothek HWR Berlin
Granting Institution:Hochschule für Wirtschaft und Recht Berlin
Date of final exam:2018/01/29
Release Date:2019/09/03
Page Number:74
Institutes:FB I - Wirtschaftswissenschaften / Business Intelligence and Process Management M.Sc.
Licence (German):License LogoUrheberrechtsschutz