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Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12008/29280 How to cite
Title: Improving web application firewalls through anomaly detection
Authors: Betarte, Gustavo
Giménez, Eduardo
Martínez, Rodrigo
Pardo, Alvaro
Type: Preprint
Keywords: Web Application Firewalls, Machine Learning, Anomaly Detection, One-class Classification, N-gram Analysis
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 Firewalls (WAF)s, a technology that is used to detect and prevent attacks. We put forward an approach of complementary 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.
Description: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), 2018, pp. 779-784.
Publisher: IEEE
Citation: Betarte, G., Giménez, E., Martínez, R. y otros. Improving web application firewalls through anomaly detection [Preprint]. Publicado en : 17th IEEE International Conference on Machine Learning and Applications (ICMLA), 2018, pp. 779-784, doi: 10.1109/ICMLA.2018.00124.
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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