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Campo DC | Valor | Lengua/Idioma |
---|---|---|
dc.contributor.author | Betarte, Gustavo | - |
dc.contributor.author | Martínez, Rodrigo | - |
dc.contributor.author | Pardo, Alvaro | - |
dc.date.accessioned | 2021-09-01T12:34:05Z | - |
dc.date.available | 2021-09-01T12:34:05Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | Betarte, G., Martínez, R. y Pardo, A. Web application attacks detection using machine learning techniques [Preprint] Publicado en : 17th IEEE International Conference on Machine Learning and Applications (ICMLA), 2018, pp. 1065-1072, doi: 10.1109/ICMLA.2018.00174. | es |
dc.identifier.uri | https://hdl.handle.net/20.500.12008/29282 | - |
dc.description | 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), 2018, pp. 1065-1072. | es |
dc.description.abstract | Web applications are permanently being exposed to attacks that exploit their vulnerabilities. In this work we investigate the use of machine learning techniques to leverage the performance of Web Application Firewalls (WAFs), systems that are used to detect and prevent attacks. We propose a characterization of the problem by defining different scenarios depending if we have valid and/or attack data available for training. We also propose two solutions: first a multi-class approach for the scenario when valid and attack data is available; and second a one-class solution when only valid data is at hand. We present results using both approaches that outperform MODSECURITY configured with the OWASP Core Rule Set out of the box, which is the baseline configuration setting of a widely deployed WAF technology.We also propose a tagged dataset based on the DRUPAL content management framework. | es |
dc.format.extent | 8 p. | es |
dc.format.mimetype | application/pdf | es |
dc.language.iso | en | es |
dc.publisher | IEEE | es |
dc.rights | Las obras depositadas en el Repositorio se rigen por la Ordenanza de los Derechos de la Propiedad Intelectual de la Universidad de la República.(Res. Nº 91 de C.D.C. de 8/III/1994 – D.O. 7/IV/1994) y por la Ordenanza del Repositorio Abierto de la Universidad de la República (Res. Nº 16 de C.D.C. de 07/10/2014) | es |
dc.subject | Web Application Firewall | es |
dc.subject | Web Application Security | es |
dc.subject | Machine Learning | es |
dc.subject | Pattern Recognition | es |
dc.title | Web application attacks detection using machine learning techniques | es |
dc.type | Preprint | es |
dc.contributor.filiacion | Betarte Gustavo, Universidad de la República (Uruguay). Facultad de Ingeniería. | - |
dc.contributor.filiacion | Martínez Rodrigo, Universidad de la República (Uruguay). Facultad de Ingeniería | - |
dc.contributor.filiacion | Pardo Alvaro, Universidad Católica del Uruguay. Departamento de Ingeniería Eléctrica, Facultad de Ingeniería y Tecnologías. | - |
dc.rights.licence | Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) | es |
Aparece en las colecciones: | Reportes Técnicos - Instituto de Computación |
Ficheros en este ítem:
Fichero | Descripción | Tamaño | Formato | ||
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BMP18.pdf | Preprint | 262,12 kB | Adobe PDF | Visualizar/Abrir |
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