idMAS-SQL: Intrusion Detection Based on MAS to Detect and Block SQL injection through data mining
This study presents a multiagent architecture aimed at detecting SQL injection attacks, which are one of the most prevalent threats for modern databases. The proposed architecture is based on a hierarchical and distributed strategy where the functionalities are structured on layers. SQL-injection at...
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| Materyal Türü: | article |
| Dil: | İngilizce |
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2018
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| Online Erişim: | https://www.sciencedirect.com/science/article/pii/S0020025511003148 http://ridda2.utp.ac.pa/handle/123456789/4780 |
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| _version_ | 1869652456912191488 |
|---|---|
| author | Pinzón Trejos, Cristian |
| author2 | De Paz, Juan Herrero, Álvaro Corchado, Emilio Bajo, Javier Corchado, Juan |
| author2_role | author author author author author |
| author_browse | Bajo, Javier Corchado, Emilio Corchado, Juan De Paz, Juan Herrero, Álvaro Pinzón Trejos, Cristian |
| author_facet | Pinzón Trejos, Cristian De Paz, Juan Herrero, Álvaro Corchado, Emilio Bajo, Javier Corchado, Juan |
| author_role | author |
| collection | Repositorio Institucional de documento digitales de acceso abierto de la UTP |
| dc.creator.none.fl_str_mv | Pinzón Trejos, Cristian De Paz, Juan Herrero, Álvaro Corchado, Emilio Bajo, Javier Corchado, Juan |
| dc.date.none.fl_str_mv | 05/10/2013 05/10/2013 2018-06-05T18:46:39Z 2018-06-05T18:46:39Z 2018-06-05T18:46:39Z 2018-06-05T18:46:39Z |
| dc.format.none.fl_str_mv | application/pdf text/html |
| dc.identifier.none.fl_str_mv | https://www.sciencedirect.com/science/article/pii/S0020025511003148 http://ridda2.utp.ac.pa/handle/123456789/4780 http://ridda2.utp.ac.pa/handle/123456789/4780 |
| dc.language.none.fl_str_mv | eng |
| dc.rights.none.fl_str_mv | info:eu-repo/semantics/embargoedAccess |
| dc.source.none.fl_str_mv | reponame:Repositorio Institucional de documento digitales de acceso abierto de la UTP instname:Universidad Tecnológica de Panamá instacron:U Tecnológica de Panamá |
| dc.subject.none.fl_str_mv | Intrusion Detection SQL injection attacks Data mining CBR SVM Neural networks Intrusion Detection SQL injection attacks Data mining CBR SVM Neural networks |
| dc.title.none.fl_str_mv | idMAS-SQL: Intrusion Detection Based on MAS to Detect and Block SQL injection through data mining |
| dc.type.none.fl_str_mv | info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
| description | This study presents a multiagent architecture aimed at detecting SQL injection attacks, which are one of the most prevalent threats for modern databases. The proposed architecture is based on a hierarchical and distributed strategy where the functionalities are structured on layers. SQL-injection attacks, one of the most dangerous attacks to online databases, are the focus of this research. The agents in each one of the layers are specialized in specific tasks, such as data gathering, data classification, and visualization. This study presents two key agents under a hybrid architecture: a classifier agent that incorporates a Case-Based Reasoning engine employing advanced algorithms in the reasoning cycle stages, and a visualizer agent that integrates several techniques to facilitate the visual analysis of suspicious queries. The former incorporates a new classification model based on a mixture of a neural network and a Support Vector Machine in order to classify SQL queries in a reliable way. The latter combines clustering and neural projection techniques to support the visual analysis and identification of target attacks. The proposed approach was tested in a real-traffic case study and its experimental results, which validate the performance of the proposed approach, are presented in this paper. |
| eu_rights_str_mv | embargoedAccess |
| format | article |
| id | lrtest_1d383f2f7474827de08697a388a2d355 |
| instacron_str | U Tecnológica de Panamá |
| institution | U Tecnológica de Panamá |
| instname_str | Universidad Tecnológica de Panamá |
| language | eng |
| network_acronym_str | lrtest |
| network_name_str | lr |
| oai_identifier_str | oai:ridda2.utp.ac.pa:123456789/4780 |
| publishDate | 2018 |
| publishDateSort | 2018 |
| reponame_str | Repositorio Institucional de documento digitales de acceso abierto de la UTP |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | idMAS-SQL: Intrusion Detection Based on MAS to Detect and Block SQL injection through data miningPinzón Trejos, CristianDe Paz, JuanHerrero, ÁlvaroCorchado, EmilioBajo, JavierCorchado, JuanIntrusion DetectionSQL injection attacksData miningCBRSVMNeural networksIntrusion DetectionSQL injection attacksData miningCBRSVMNeural networksThis study presents a multiagent architecture aimed at detecting SQL injection attacks, which are one of the most prevalent threats for modern databases. The proposed architecture is based on a hierarchical and distributed strategy where the functionalities are structured on layers. SQL-injection attacks, one of the most dangerous attacks to online databases, are the focus of this research. The agents in each one of the layers are specialized in specific tasks, such as data gathering, data classification, and visualization. This study presents two key agents under a hybrid architecture: a classifier agent that incorporates a Case-Based Reasoning engine employing advanced algorithms in the reasoning cycle stages, and a visualizer agent that integrates several techniques to facilitate the visual analysis of suspicious queries. The former incorporates a new classification model based on a mixture of a neural network and a Support Vector Machine in order to classify SQL queries in a reliable way. The latter combines clustering and neural projection techniques to support the visual analysis and identification of target attacks. The proposed approach was tested in a real-traffic case study and its experimental results, which validate the performance of the proposed approach, are presented in this paper.This study presents a multiagent architecture aimed at detecting SQL injection attacks, which are one of the most prevalent threats for modern databases. The proposed architecture is based on a hierarchical and distributed strategy where the functionalities are structured on layers. SQL-injection attacks, one of the most dangerous attacks to online databases, are the focus of this research. The agents in each one of the layers are specialized in specific tasks, such as data gathering, data classification, and visualization. This study presents two key agents under a hybrid architecture: a classifier agent that incorporates a Case-Based Reasoning engine employing advanced algorithms in the reasoning cycle stages, and a visualizer agent that integrates several techniques to facilitate the visual analysis of suspicious queries. The former incorporates a new classification model based on a mixture of a neural network and a Support Vector Machine in order to classify SQL queries in a reliable way. The latter combines clustering and neural projection techniques to support the visual analysis and identification of target attacks. The proposed approach was tested in a real-traffic case study and its experimental results, which validate the performance of the proposed approach, are presented in this paper.2018-06-05T18:46:39Z2018-06-05T18:46:39Z2018-06-05T18:46:39Z2018-06-05T18:46:39Z05/10/201305/10/2013info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdftext/htmlhttps://www.sciencedirect.com/science/article/pii/S0020025511003148http://ridda2.utp.ac.pa/handle/123456789/4780http://ridda2.utp.ac.pa/handle/123456789/4780enginfo:eu-repo/semantics/embargoedAccessreponame:Repositorio Institucional de documento digitales de acceso abierto de la UTPinstname:Universidad Tecnológica de Panamáinstacron:U Tecnológica de Panamáoai:ridda2.utp.ac.pa:123456789/47802021-07-06T15:35:04Z |
| spellingShingle | idMAS-SQL: Intrusion Detection Based on MAS to Detect and Block SQL injection through data mining Pinzón Trejos, Cristian Intrusion Detection SQL injection attacks Data mining CBR SVM Neural networks Intrusion Detection SQL injection attacks Data mining CBR SVM Neural networks |
| status_str | publishedVersion |
| title | idMAS-SQL: Intrusion Detection Based on MAS to Detect and Block SQL injection through data mining |
| title_full | idMAS-SQL: Intrusion Detection Based on MAS to Detect and Block SQL injection through data mining |
| title_fullStr | idMAS-SQL: Intrusion Detection Based on MAS to Detect and Block SQL injection through data mining |
| title_full_unstemmed | idMAS-SQL: Intrusion Detection Based on MAS to Detect and Block SQL injection through data mining |
| title_short | idMAS-SQL: Intrusion Detection Based on MAS to Detect and Block SQL injection through data mining |
| title_sort | idMAS-SQL: Intrusion Detection Based on MAS to Detect and Block SQL injection through data mining |
| topic | Intrusion Detection SQL injection attacks Data mining CBR SVM Neural networks Intrusion Detection SQL injection attacks Data mining CBR SVM Neural networks |
| url | https://www.sciencedirect.com/science/article/pii/S0020025511003148 http://ridda2.utp.ac.pa/handle/123456789/4780 |