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dc.creatorDe-La-Hoz-Franco, Emiro
dc.creatorOrtiz García, Andrés
dc.creatorOrtega Lopera, Julio
dc.creatorDe la Hoz Correa, Eduardo Miguel
dc.creatorPrieto Espinosa, Carlos Antonio
dc.identifier.citationde la Hoz Franco E., Ortiz García A., Ortega Lopera J., de la Hoz Correa E., Prieto Espinosa A. (2013) Network Anomaly Detection with Bayesian Self-Organizing Maps. In: Rojas I., Joya G., Gabestany J. (eds) Advances in Computational Intelligence. IWANN 2013. Lecture Notes in Computer Science, vol 7902. Springer, Berlin, Heidelberg.
dc.description.abstractThe growth of the Internet and consequently, the number of interconnected computers through a shared medium, has exposed a lot of relevant information to intruders and attackers. Firewalls aim to detect violations to a predefined rule set and usually block potentially dangerous incoming traffic. However, with the evolution of the attack techniques, it is more difficult to distinguish anomalies from the normal traffic. Different intrusion detection approaches have been proposed, including the use of artificial intelligence techniques such as neural networks. In this paper, we present a network anomaly detection technique based on Probabilistic Self-Organizing Maps (PSOM) to differentiate between normal and anomalous traffic. The detection capabilities of the proposed system can be modified without retraining the map, but only modifying the activation probabilities of the units. This deals with fast implementations of Intrusion Detection Systems (IDS) necessary to cope with current link
dc.publisherCorporación Universidad de la Costaspa
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 International*
dc.sourceAdvances in Computational Intelligencespa
dc.subjectGaussian Mixture Modelspa
dc.subjectIntrusion Detection Systemspa
dc.subjectReceiver Operating Curf Curvespa
dc.subjectBest Match Unitspa
dc.subjectReceiver Operating Curfspa
dc.titleNetwork Anomaly Detection with Bayesian Self-Organizing Mapsspa
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