Machine learning-assisted virtual patching of web applications
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 Firewall (WAF), a technology that is used to detect and prevent attacks. We propose a combined approach of 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. The proposed solution, combining both approaches, allow us to deploy a WAF when no training data for the application is available (using one-class classification), and an improved one using n-grams when training data is available.
| 2018 | |
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Web Application Firewalls Machine Learning Anomaly Detection One-class Classification n-grams |
|
| Inglés | |
| Universidad de la República | |
| COLIBRI | |
| https://hdl.handle.net/20.500.12008/29283 | |
| Acceso abierto | |
| Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| _version_ | 1807522945557331968 |
|---|---|
| author | Betarte, Gustavo |
| author2 | Giménez, Eduardo Martínez, Rodrigo Pardo, Álvaro |
| author2_role | author author author |
| author_facet | Betarte, Gustavo Giménez, Eduardo Martínez, Rodrigo Pardo, Álvaro |
| 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 Álvaro, 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, Álvaro |
| dc.date.accessioned.none.fl_str_mv | 2021-09-01T12:34:27Z |
| dc.date.available.none.fl_str_mv | 2021-09-01T12:34:27Z |
| 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 Firewall (WAF), a technology that is used to detect and prevent attacks. We propose a combined approach of 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. The proposed solution, combining both approaches, allow us to deploy a WAF when no training data for the application is available (using one-class classification), and an improved one using n-grams when training data is available. |
| dc.description.es.fl_txt_mv | Computing Research Repository (CoRR), ArXiv, abs/1803.05529, mar. 2018. |
| dc.format.extent.es.fl_str_mv | 14 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. Machine learning-assisted virtual patching of web applications [Preprint]. Publicado en: Computing Research Repository (CoRR), ArXiv, abs/1803.05529, mar. 2018. |
| dc.identifier.uri.none.fl_str_mv | https://hdl.handle.net/20.500.12008/29283 |
| dc.language.iso.none.fl_str_mv | en eng |
| 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-grams |
| dc.title.none.fl_str_mv | Machine learning-assisted virtual patching of web applications |
| 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 | Computing Research Repository (CoRR), ArXiv, abs/1803.05529, mar. 2018. |
| eu_rights_str_mv | openAccess |
| format | preprint |
| id | COLIBRI_c80440ee481dad471cd434fa243b2fdf |
| identifier_str_mv | Betarte, G., Giménez, E., Martínez, R. y otros. Machine learning-assisted virtual patching of web applications [Preprint]. Publicado en: Computing Research Repository (CoRR), ArXiv, abs/1803.05529, mar. 2018. |
| 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:20.500.12008/29283 |
| 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 Álvaro, Universidad Católica del Uruguay. Departamento de Ingeniería Eléctrica, Facultad de Ingeniería y Tecnologías.2021-09-01T12:34:27Z2021-09-01T12:34:27Z2018Betarte, G., Giménez, E., Martínez, R. y otros. Machine learning-assisted virtual patching of web applications [Preprint]. Publicado en: Computing Research Repository (CoRR), ArXiv, abs/1803.05529, mar. 2018.https://hdl.handle.net/20.500.12008/29283Computing Research Repository (CoRR), ArXiv, abs/1803.05529, mar. 2018.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 Firewall (WAF), a technology that is used to detect and prevent attacks. We propose a combined approach of 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. The proposed solution, combining both approaches, allow us to deploy a WAF when no training data for the application is available (using one-class classification), and an improved one using n-grams when training data is available.Submitted by Machado Jimena (jmachado@fing.edu.uy) on 2021-08-31T16:22:16Z No. of bitstreams: 2 license_rdf: 23149 bytes, checksum: 1996b8461bc290aef6a27d78c67b6b52 (MD5) BGMP18.pdf: 477415 bytes, checksum: 21a71a340756f5bb78be69cc36721d82 (MD5)Approved for entry into archive by Machado Jimena (jmachado@fing.edu.uy) on 2021-08-31T18:58:59Z (GMT) No. of bitstreams: 2 license_rdf: 23149 bytes, checksum: 1996b8461bc290aef6a27d78c67b6b52 (MD5) BGMP18.pdf: 477415 bytes, checksum: 21a71a340756f5bb78be69cc36721d82 (MD5)Made available in DSpace by Luna Fabiana (fabiana.luna@seciu.edu.uy) on 2021-09-01T12:34:27Z (GMT). No. of bitstreams: 2 license_rdf: 23149 bytes, checksum: 1996b8461bc290aef6a27d78c67b6b52 (MD5) BGMP18.pdf: 477415 bytes, checksum: 21a71a340756f5bb78be69cc36721d82 (MD5) Previous issue date: 201814 p.application/pdfenengLas 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-gramsMachine learning-assisted virtual patching of web applicationsPreprintinfo:eu-repo/semantics/preprintinfo:eu-repo/semantics/submittedVersionreponame:COLIBRIinstname:Universidad de la Repúblicainstacron:Universidad de la RepúblicaBetarte, GustavoGiménez, EduardoMartínez, RodrigoPardo, ÁlvaroLICENSElicense.txtlicense.txttext/plain; charset=utf-84267http://localhost:8080/xmlui/bitstream/20.500.12008/29283/5/license.txt6429389a7df7277b72b7924fdc7d47a9MD55CC-LICENSElicense_urllicense_urltext/plain; charset=utf-850http://localhost:8080/xmlui/bitstream/20.500.12008/29283/2/license_urla006180e3f5b2ad0b88185d14284c0e0MD52license_textlicense_texttext/html; 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- Universidad de la Repúblicafalse |
| spellingShingle | Machine learning-assisted virtual patching of web applications Betarte, Gustavo Web Application Firewalls Machine Learning Anomaly Detection One-class Classification n-grams |
| status_str | submittedVersion |
| title | Machine learning-assisted virtual patching of web applications |
| title_full | Machine learning-assisted virtual patching of web applications |
| title_fullStr | Machine learning-assisted virtual patching of web applications |
| title_full_unstemmed | Machine learning-assisted virtual patching of web applications |
| title_short | Machine learning-assisted virtual patching of web applications |
| title_sort | Machine learning-assisted virtual patching of web applications |
| topic | Web Application Firewalls Machine Learning Anomaly Detection One-class Classification n-grams |
| url | https://hdl.handle.net/20.500.12008/29283 |
