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Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12008/29283 How to cite
Title: Machine learning-assisted virtual patching of web applications
Authors: Betarte, Gustavo
Giménez, Eduardo
Martínez, Rodrigo
Pardo, Álvaro
Type: Preprint
Keywords: Web Application Firewalls, Machine Learning, Anomaly Detection, One-class Classification, n-grams
Issue Date: 2018
Abstract: 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.
Description: Computing Research Repository (CoRR), ArXiv, abs/1803.05529, mar. 2018.
Citation: 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.
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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