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Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12008/29285 How to cite
Title: Enhancing web application attack detection using machine learning
Authors: Martínez, Rodrigo
Type: Preprint
Keywords: Web Application Firewall, Web Application Security, Machine Learning, Pattern Recognition
Issue Date: 2018
Abstract: The exploit of vulnerabilities present in Web applications has been the main attack vector in the last decade biggest data breaches. In this work we put forward a framework to leverage the performance of Web Application Firewalls (WAFs) using machine learning techniques. We propose the use of two types of machine learning models: a multi-class approach for the scenario when valid and attack data is available and alternatively a one-class model when only valid data is at hand. The use of both models to predict potential malicious traffic has shown to outperform MODSECURITY, a widely deployed WAF technology, configured with the OWASP Core Rule Set out of the box. We also present a prototype that integrates the one-class model with MODSECURITY.
Description: LADC 2018, 8th Latin-American Symposium on Dependable Computing, Foz de Iguaçu, Brazil, 8-10 October 2018.
Citation: Martínez, R. Enhancing web application attack detection using machine learning [Preprint]. Publicado en : LADC 2018, 8th Latin-American Symposium on Dependable Computing, Foz de Iguaçu, Brazil, 8-10 October 2018.
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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