Título:
Improving web application firewalls through anomaly detection
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Autor:
Betarte, Gustavo
Giménez, Eduardo
Martínez, Rodrigo
Pardo, Alvaro
Giménez, Eduardo
Martínez, Rodrigo
Pardo, Alvaro
Tutor:
Tipo de documento:
Preprint
Editor:
Palabras clave:
Web Application Firewalls
Machine Learning
Anomaly Detection
One-class Classification
N-gram Analysis
Machine Learning
Anomaly Detection
One-class Classification
N-gram Analysis
Descriptores:
Año de publicación:
2018
Contenido:
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 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.
Descripción:
2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), 2018, pp. 779-784.
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Editorial:
IEEE
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Citación:
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.
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Licencia:
Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)
Colecciones:
| Ficheros | Descripción | Tamaño | Formato | ||
|---|---|---|---|---|---|
| BGMP18.pdf | Preprint | 329.27 KB | Adobe PDF |