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Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12008/29282 How to cite
Title: Web application attacks detection using machine learning techniques
Authors: Betarte, Gustavo
Martínez, Rodrigo
Pardo, Alvaro
Type: Preprint
Keywords: Web Application Firewall, Web Application Security, Machine Learning, Pattern Recognition
Issue Date: 2018
Abstract: Web applications are permanently being exposed to attacks that exploit their vulnerabilities. In this work we investigate the use of machine learning techniques to leverage the performance of Web Application Firewalls (WAFs), systems that are used to detect and prevent attacks. We propose a characterization of the problem by defining different scenarios depending if we have valid and/or attack data available for training. We also propose two solutions: first a multi-class approach for the scenario when valid and attack data is available; and second a one-class solution when only valid data is at hand. We present results using both approaches that outperform MODSECURITY configured with the OWASP Core Rule Set out of the box, which is the baseline configuration setting of a widely deployed WAF technology.We also propose a tagged dataset based on the DRUPAL content management framework.
Description: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), 2018, pp. 1065-1072.
Publisher: IEEE
Citation: Betarte, G., Martínez, R. y Pardo, A. Web application attacks detection using machine learning techniques [Preprint] Publicado en : 17th IEEE International Conference on Machine Learning and Applications (ICMLA), 2018, pp. 1065-1072, doi: 10.1109/ICMLA.2018.00174.
License: Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)
Appears in Collections:Reportes Técnicos - Instituto de Computación

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