english Icono del idioma   español Icono del idioma  

Por favor, use este identificador para citar o enlazar este ítem: https://hdl.handle.net/20.500.12008/29283 Cómo citar
Título: Machine learning-assisted virtual patching of web applications
Autor: Betarte, Gustavo
Giménez, Eduardo
Martínez, Rodrigo
Pardo, Álvaro
Tipo: Preprint
Palabras clave: Web Application Firewalls, Machine Learning, Anomaly Detection, One-class Classification, n-grams
Fecha de publicación: 2018
Resumen: Web applications are permanently being exposed to attacks that exploit their vulnerabilities. In this work we investigate the application of machine learning techniques to leverage Web Application Firewall (WAF), a technology that is used to detect and prevent attacks. We propose a combined approach of machine learning models, based on one-class classification and n-gram analysis, to enhance the detection and accuracy capabilities of MODSECURITY, an open source and widely used WAF. The results are promising and outperform MODSECURITY when configured with the OWASP Core Rule Set, the baseline configuration setting of a widely deployed, rule-based WAF technology. The proposed solution, combining both approaches, allow us to deploy a WAF when no training data for the application is available (using one-class classification), and an improved one using n-grams when training data is available.
Descripción: Computing Research Repository (CoRR), ArXiv, abs/1803.05529, mar. 2018.
Citación: Betarte, G., Giménez, E., Martínez, R. y otros. Machine learning-assisted virtual patching of web applications [Preprint]. Publicado en: Computing Research Repository (CoRR), ArXiv, abs/1803.05529, mar. 2018.
Licencia: Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)
Aparece en las colecciones: Reportes Técnicos - Instituto de Computación

Ficheros en este ítem:
Fichero Descripción Tamaño Formato   
BGMP18.pdfPreprint466,23 kBAdobe PDFVisualizar/Abrir


Este ítem está sujeto a una licencia Creative Commons Licencia Creative Commons Creative Commons