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dc.contributor.authorPinzón Trejos, Cristian
dc.contributor.authorDe Paz, Juan
dc.contributor.authorHerrero, Álvaro
dc.contributor.authorCorchado, Emilio
dc.contributor.authorBajo, Javier
dc.contributor.authorCorchado, Juan
dc.date.accessioned2018-06-05T18:46:39Z
dc.date.accessioned2018-06-05T18:46:39Z
dc.date.available2018-06-05T18:46:39Z
dc.date.available2018-06-05T18:46:39Z
dc.date.issued05/10/2013
dc.date.issued05/10/2013
dc.identifierhttps://www.sciencedirect.com/science/article/pii/S0020025511003148
dc.identifier.urihttp://ridda2.utp.ac.pa/handle/123456789/4780
dc.identifier.urihttp://ridda2.utp.ac.pa/handle/123456789/4780
dc.descriptionThis 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.en_US
dc.description.abstractThis 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.en_US
dc.formatapplication/pdf
dc.formattext/html
dc.languageeng
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.subjectIntrusion Detectionen_US
dc.subjectSQL injection attacksen_US
dc.subjectData miningen_US
dc.subjectCBRen_US
dc.subjectSVMen_US
dc.subjectNeural networksen_US
dc.subjectIntrusion Detection
dc.subjectSQL injection attacks
dc.subjectData mining
dc.subjectCBR
dc.subjectSVM
dc.subjectNeural networks
dc.titleidMAS-SQL: Intrusion Detection Based on MAS to Detect and Block SQL injection through data miningen_US
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion


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