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dc.contributor.authorPinzón Trejos, Cristian
dc.contributor.authorHerrero, Álvaro
dc.contributor.authorDe Paz, Juan
dc.contributor.authorCorchado, Emilio
dc.contributor.authorBajo, Javier
dc.date.accessioned2018-06-05T19:20:00Z
dc.date.available2018-06-05T19:20:00Z
dc.date.issued06/23/2010
dc.date.issued06/23/2010
dc.identifier.urihttp://ridda2.utp.ac.pa/handle/123456789/4783
dc.descriptionOne of the most serious security threats to recently deployed databases has been the SQL Injection attack. This paper presents an agent specialised in the detection of SQL injection attacks. The agent incorporates a Case-Based Reasoning engine which is equipped with a learning and adaptation capacity for the classification of malicious codes. The agent also incorporates advanced algorithms in the reasoning cycle stages. The reuse phase uses an innovative classification model based on a mixture of a neuronal network together with a Support Vector Machine in order to classify the received SQL queries in the most reliable way. Finally, a visualisation neural technique is incorporated, which notably eases the revision stage carried out by human experts in the case of suspicious queries. The Classifier Agent was tested in a real-traffic case study and its experimental results, which validate the performance of the proposed approach, are presented here.en_US
dc.description.abstractOne of the most serious security threats to recently deployed databases has been the SQL Injection attack. This paper presents an agent specialised in the detection of SQL injection attacks. The agent incorporates a Case-Based Reasoning engine which is equipped with a learning and adaptation capacity for the classification of malicious codes. The agent also incorporates advanced algorithms in the reasoning cycle stages. The reuse phase uses an innovative classification model based on a mixture of a neuronal network together with a Support Vector Machine in order to classify the received SQL queries in the most reliable way. Finally, a visualisation neural technique is incorporated, which notably eases the revision stage carried out by human experts in the case of suspicious queries. The Classifier Agent was tested in a real-traffic case study and its experimental results, which validate the performance of the proposed approach, are presented here.en_US
dc.formatapplication/pdf
dc.languageeng
dc.language.isoengen_US
dc.rightshttps://creativecommons.org/licenses/by-nc-sa/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectSQL Injectionen_US
dc.subjectIntrusion Detectionen_US
dc.subjectCBRen_US
dc.subjectSVMen_US
dc.subjectNeural Networksen_US
dc.subjectSQL Injection
dc.subjectIntrusion Detection
dc.subjectCBR
dc.subjectSVM
dc.subjectNeural Networks
dc.titleCBRid4SQL: A CBR Intrusion Detector for SQL Injection Attacksen_US
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion


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