Improving web application firewalls through anomaly detection
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.
| 2018 | |
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Web Application Firewalls Machine Learning Anomaly Detection One-class Classification N-gram Analysis |
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| Inglés | |
| Universidad de la República | |
| COLIBRI | |
| https://hdl.handle.net/20.500.12008/29280 | |
| Acceso abierto | |
| Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| _version_ | 1807522945590886400 |
|---|---|
| author | Betarte, Gustavo |
| author2 | Giménez, Eduardo Martínez, Rodrigo Pardo, Alvaro |
| author2_role | author author author |
| author_facet | Betarte, Gustavo Giménez, Eduardo 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. Giménez Eduardo, Tilsor SA, Uruguay 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 Giménez, Eduardo Martínez, Rodrigo Pardo, Alvaro |
| dc.date.accessioned.none.fl_str_mv | 2021-09-01T12:33:31Z |
| dc.date.available.none.fl_str_mv | 2021-09-01T12:33:31Z |
| 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 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. |
| dc.description.es.fl_txt_mv | 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), 2018, pp. 779-784. |
| dc.format.extent.es.fl_str_mv | 6 p. |
| dc.format.mimetype.es.fl_str_mv | application/pdf |
| dc.identifier.citation.es.fl_str_mv | 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. |
| dc.identifier.uri.none.fl_str_mv | https://hdl.handle.net/20.500.12008/29280 |
| 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 Firewalls Machine Learning Anomaly Detection One-class Classification N-gram Analysis |
| dc.title.none.fl_str_mv | Improving web application firewalls through anomaly detection |
| 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. 779-784. |
| eu_rights_str_mv | openAccess |
| format | preprint |
| id | COLIBRI_9cda53430890070d8a6bb1f67c69adb6 |
| identifier_str_mv | 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. |
| 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:20.500.12008/29280 |
| 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.Giménez Eduardo, Tilsor SA, UruguayMartínez Rodrigo, Universidad de la República (Uruguay). Facultad de Ingeniería.Pardo Alvaro, Universidad Católica del Uruguay. Departamento de Ingeniería Eléctrica, Facultad de Ingeniería y Tecnologías.2021-09-01T12:33:31Z2021-09-01T12:33:31Z2018Betarte, 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.https://hdl.handle.net/20.500.12008/292802018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), 2018, pp. 779-784.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.Submitted by Machado Jimena (jmachado@fing.edu.uy) on 2021-08-31T18:03:53Z No. of bitstreams: 2 license_rdf: 23149 bytes, checksum: 1996b8461bc290aef6a27d78c67b6b52 (MD5) BGMP18.pdf: 337168 bytes, checksum: 36b1e696152a3b7257bb7b1818a37207 (MD5)Approved for entry into archive by Machado Jimena (jmachado@fing.edu.uy) on 2021-08-31T18:55:55Z (GMT) No. of bitstreams: 2 license_rdf: 23149 bytes, checksum: 1996b8461bc290aef6a27d78c67b6b52 (MD5) BGMP18.pdf: 337168 bytes, checksum: 36b1e696152a3b7257bb7b1818a37207 (MD5)Made available in DSpace by Luna Fabiana (fabiana.luna@seciu.edu.uy) on 2021-09-01T12:33:31Z (GMT). No. of bitstreams: 2 license_rdf: 23149 bytes, checksum: 1996b8461bc290aef6a27d78c67b6b52 (MD5) BGMP18.pdf: 337168 bytes, checksum: 36b1e696152a3b7257bb7b1818a37207 (MD5) Previous issue date: 20186 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 FirewallsMachine LearningAnomaly DetectionOne-class ClassificationN-gram AnalysisImproving web application firewalls through anomaly detectionPreprintinfo:eu-repo/semantics/preprintinfo:eu-repo/semantics/submittedVersionreponame:COLIBRIinstname:Universidad de la Repúblicainstacron:Universidad de la RepúblicaBetarte, GustavoGiménez, EduardoMartínez, RodrigoPardo, AlvaroLICENSElicense.txtlicense.txttext/plain; charset=utf-84267http://localhost:8080/xmlui/bitstream/20.500.12008/29280/5/license.txt6429389a7df7277b72b7924fdc7d47a9MD55CC-LICENSElicense_urllicense_urltext/plain; charset=utf-850http://localhost:8080/xmlui/bitstream/20.500.12008/29280/2/license_urla006180e3f5b2ad0b88185d14284c0e0MD52license_textlicense_texttext/html; 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- Universidad de la Repúblicafalse |
| spellingShingle | Improving web application firewalls through anomaly detection Betarte, Gustavo Web Application Firewalls Machine Learning Anomaly Detection One-class Classification N-gram Analysis |
| status_str | submittedVersion |
| title | Improving web application firewalls through anomaly detection |
| title_full | Improving web application firewalls through anomaly detection |
| title_fullStr | Improving web application firewalls through anomaly detection |
| title_full_unstemmed | Improving web application firewalls through anomaly detection |
| title_short | Improving web application firewalls through anomaly detection |
| title_sort | Improving web application firewalls through anomaly detection |
| topic | Web Application Firewalls Machine Learning Anomaly Detection One-class Classification N-gram Analysis |
| url | https://hdl.handle.net/20.500.12008/29280 |
