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dc.contributor.authorPinzón Trejos, Cristian
dc.contributor.authorDe Paz, Juan
dc.contributor.authorBajo, Javier
dc.contributor.authorHerrero, Álvaro
dc.contributor.authorHerrero, Emilio
dc.date.accessioned2018-06-05T19:12:13Z
dc.date.accessioned2018-06-05T19:12:13Z
dc.date.available2018-06-05T19:12:13Z
dc.date.available2018-06-05T19:12:13Z
dc.date.issued08/23/2010
dc.date.issued08/23/2010
dc.identifierhttps://ieeexplore.ieee.org/abstract/document/5600026/
dc.identifier.urihttp://ridda2.utp.ac.pa/handle/123456789/4782
dc.identifier.urihttp://ridda2.utp.ac.pa/handle/123456789/4782
dc.descriptionSQL Injection attacks on web applications have become one of the most important information security concerns over the past few years. This paper presents a hybrid approach based on the Adaptive Intelligent Intrusion Detector Agent (AIIDA-SQL) for the detection of those attacks. The AIIDA-SQL agent incorporates a Case-Based Reasoning (CBR) engine which is equipped with learning and adaptation capabilities for the classification of SQL queries and detection of malicious user requests. To carry out the tasks of attack classification and detection, the agent incorporates advanced algorithms in the reasoning cycle stages. Concretely, an innovative classification model based on a mixture of an Artificial Neuronal Network together with a Support Vector Machine is applied in the reuse stage of the CBR cycle. This strategy enables to classify the received SQL queries in a reliable way. Finally, a projection neural technique is incorporated, which notably eases the revision stage carried out by human experts in the case of suspicious queries. The experimental results obtained on a real-traffic case study show that AIIDA-SQL performs remarkably well in practice.en_US
dc.description.abstractSQL Injection attacks on web applications have become one of the most important information security concerns over the past few years. This paper presents a hybrid approach based on the Adaptive Intelligent Intrusion Detector Agent (AIIDA-SQL) for the detection of those attacks. The AIIDA-SQL agent incorporates a Case-Based Reasoning (CBR) engine which is equipped with learning and adaptation capabilities for the classification of SQL queries and detection of malicious user requests. To carry out the tasks of attack classification and detection, the agent incorporates advanced algorithms in the reasoning cycle stages. Concretely, an innovative classification model based on a mixture of an Artificial Neuronal Network together with a Support Vector Machine is applied in the reuse stage of the CBR cycle. This strategy enables to classify the received SQL queries in a reliable way. Finally, a projection neural technique is incorporated, which notably eases the revision stage carried out by human experts in the case of suspicious queries. The experimental results obtained on a real-traffic case study show that AIIDA-SQL performs remarkably well in practice.en_US
dc.formatapplication/pdf
dc.formattext/html
dc.languageeng
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.subjectIntrusion Detectionen_US
dc.subjectAgenten_US
dc.subjectCase-Based Reasoningen_US
dc.subjectSupport Vector Machineen_US
dc.subjectArtificial Neural Networken_US
dc.subjectSQL Injectionen_US
dc.subjectIntrusion Detection
dc.subjectAgent
dc.subjectCase-Based Reasoning
dc.subjectSupport Vector Machine
dc.subjectArtificial Neural Network
dc.subjectSQL Injection
dc.titleAIIDA-SQL: An Adaptive Intelligent Intrusion Detector Agent for detecting SQL Injection attacksen_US
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion


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