Web application attacks detection using machine learning techniques
Resumen:
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.
| 2018 | |
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Web Application Firewall Web Application Security Machine Learning Pattern Recognition |
|
| Inglés | |
| Universidad de la República | |
| COLIBRI | |
| https://hdl.handle.net/20.500.12008/29282 | |
| Acceso abierto | |
| Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| _version_ | 1807522945574109184 |
|---|---|
| author | Betarte, Gustavo |
| author2 | Martínez, Rodrigo Pardo, Alvaro |
| author2_role | author author |
| author_facet | Betarte, Gustavo Martínez, Rodrigo Pardo, Alvaro |
| author_role | author |
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| collection | COLIBRI |
| dc.contributor.filiacion.none.fl_str_mv | Betarte Gustavo, Universidad de la República (Uruguay). Facultad de Ingeniería. Martínez Rodrigo, Universidad de la República (Uruguay). Facultad de Ingeniería Pardo Alvaro, Universidad Católica del Uruguay. Departamento de Ingeniería Eléctrica, Facultad de Ingeniería y Tecnologías. |
| dc.creator.none.fl_str_mv | Betarte, Gustavo Martínez, Rodrigo Pardo, Alvaro |
| dc.date.accessioned.none.fl_str_mv | 2021-09-01T12:34:05Z |
| dc.date.available.none.fl_str_mv | 2021-09-01T12:34:05Z |
| dc.date.issued.none.fl_str_mv | 2018 |
| dc.description.abstract.none.fl_txt_mv | 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. |
| dc.description.es.fl_txt_mv | 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), 2018, pp. 1065-1072. |
| dc.format.extent.es.fl_str_mv | 8 p. |
| dc.format.mimetype.es.fl_str_mv | application/pdf |
| dc.identifier.citation.es.fl_str_mv | 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. |
| dc.identifier.uri.none.fl_str_mv | https://hdl.handle.net/20.500.12008/29282 |
| dc.language.iso.none.fl_str_mv | en eng |
| dc.publisher.es.fl_str_mv | IEEE |
| dc.rights.license.none.fl_str_mv | Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| dc.rights.none.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.source.none.fl_str_mv | reponame:COLIBRI instname:Universidad de la República instacron:Universidad de la República |
| dc.subject.es.fl_str_mv | Web Application Firewall Web Application Security Machine Learning Pattern Recognition |
| dc.title.none.fl_str_mv | Web application attacks detection using machine learning techniques |
| dc.type.es.fl_str_mv | Preprint |
| dc.type.none.fl_str_mv | info:eu-repo/semantics/preprint |
| dc.type.version.none.fl_str_mv | info:eu-repo/semantics/submittedVersion |
| description | 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), 2018, pp. 1065-1072. |
| eu_rights_str_mv | openAccess |
| format | preprint |
| id | COLIBRI_3a60e17e32b43a6c4efdee67d6015884 |
| identifier_str_mv | 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. |
| instacron_str | Universidad de la República |
| institution | Universidad de la República |
| instname_str | Universidad de la República |
| language | eng |
| language_invalid_str_mv | en |
| network_acronym_str | COLIBRI |
| network_name_str | COLIBRI |
| oai_identifier_str | oai:colibri.udelar.edu.uy:20.500.12008/29282 |
| publishDate | 2018 |
| reponame_str | COLIBRI |
| repository.mail.fl_str_mv | mabel.seroubian@seciu.edu.uy |
| repository.name.fl_str_mv | COLIBRI - Universidad de la República |
| repository_id_str | 4771 |
| rights_invalid_str_mv | Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| spelling | Betarte Gustavo, Universidad de la República (Uruguay). Facultad de Ingeniería.Martínez Rodrigo, Universidad de la República (Uruguay). Facultad de IngenieríaPardo Alvaro, Universidad Católica del Uruguay. Departamento de Ingeniería Eléctrica, Facultad de Ingeniería y Tecnologías.2021-09-01T12:34:05Z2021-09-01T12:34:05Z2018Betarte, 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.https://hdl.handle.net/20.500.12008/292822018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), 2018, pp. 1065-1072.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.Submitted by Machado Jimena (jmachado@fing.edu.uy) on 2021-08-31T17:50:06Z No. of bitstreams: 2 license_rdf: 23149 bytes, checksum: 1996b8461bc290aef6a27d78c67b6b52 (MD5) BMP18.pdf: 268410 bytes, checksum: c3417527d32c55f9c828aed14434be60 (MD5)Approved for entry into archive by Machado Jimena (jmachado@fing.edu.uy) on 2021-08-31T18:56:21Z (GMT) No. of bitstreams: 2 license_rdf: 23149 bytes, checksum: 1996b8461bc290aef6a27d78c67b6b52 (MD5) BMP18.pdf: 268410 bytes, checksum: c3417527d32c55f9c828aed14434be60 (MD5)Made available in DSpace by Luna Fabiana (fabiana.luna@seciu.edu.uy) on 2021-09-01T12:34:05Z (GMT). No. of bitstreams: 2 license_rdf: 23149 bytes, checksum: 1996b8461bc290aef6a27d78c67b6b52 (MD5) BMP18.pdf: 268410 bytes, checksum: c3417527d32c55f9c828aed14434be60 (MD5) Previous issue date: 20188 p.application/pdfenengIEEELas 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)info:eu-repo/semantics/openAccessLicencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)Web Application FirewallWeb Application SecurityMachine LearningPattern RecognitionWeb application attacks detection using machine learning techniquesPreprintinfo:eu-repo/semantics/preprintinfo:eu-repo/semantics/submittedVersionreponame:COLIBRIinstname:Universidad de la Repúblicainstacron:Universidad de la RepúblicaBetarte, GustavoMartínez, RodrigoPardo, AlvaroLICENSElicense.txtlicense.txttext/plain; charset=utf-84267http://localhost:8080/xmlui/bitstream/20.500.12008/29282/5/license.txt6429389a7df7277b72b7924fdc7d47a9MD55CC-LICENSElicense_urllicense_urltext/plain; charset=utf-850http://localhost:8080/xmlui/bitstream/20.500.12008/29282/2/license_urla006180e3f5b2ad0b88185d14284c0e0MD52license_textlicense_texttext/html; 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- Universidad de la Repúblicafalse |
| spellingShingle | Web application attacks detection using machine learning techniques Betarte, Gustavo Web Application Firewall Web Application Security Machine Learning Pattern Recognition |
| status_str | submittedVersion |
| title | Web application attacks detection using machine learning techniques |
| title_full | Web application attacks detection using machine learning techniques |
| title_fullStr | Web application attacks detection using machine learning techniques |
| title_full_unstemmed | Web application attacks detection using machine learning techniques |
| title_short | Web application attacks detection using machine learning techniques |
| title_sort | Web application attacks detection using machine learning techniques |
| topic | Web Application Firewall Web Application Security Machine Learning Pattern Recognition |
| url | https://hdl.handle.net/20.500.12008/29282 |
