Show simple item record

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.date.accessioned2020-11-10T21:57:45Z
dc.date.available2020-11-10T21:57:45Z
dc.date.issued2013
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. https://doi.org/10.1007/978-3-642-38679-4_53eng
dc.identifier.isbn978-3-642-38679-4
dc.identifier.urihttps://hdl.handle.net/11323/7247
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 bandwidths.spa
dc.format.mimetypeapplication/pdfspa
dc.language.isoengspa
dc.publisherCorporación Universidad de la Costaspa
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/*
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
dc.typearticlespa
dcterms.referencesAlhoniemi, E., Himberg, J., Vesanto, J.: Probabilistic measures for responses of self-organizing map units. In: Proc. of the International ICSC Congress on Computational Intelligence Methods and Applications (CIMA), vol. 1, pp. 286–290 (1999)spa
dcterms.referencesGhosh, J., Wanken, J., Charron, F.: Detecting anomalous and unknown intrusions against programs. In: Proc. of the Annual Computer Security Applications Conference, vol. 1, pp. 259–267 (1998)spa
dcterms.referencesHaykin, S.: Neural Networks, 2nd edn. Prentice-Hall (1999)spa
dcterms.referencesHoffman, A., Schimitz, C., Sick, B.: Intrussion detection in computer networks with neural and fuzzy classifiers. In: International Conference on Artificial Neural Networks (ICANN), vol. 1, pp. 316–324 (2003)spa
dcterms.referencesKohonen, T.: Self-Organizing Maps. Springer (2001)spa
dcterms.referencesLippmann, R.P., Fried, D.J., Graf, I., Haines, J.W., Kendball, K.R., McClung, D., Weber, D., Webster, S.E., Wyschgrod, D., Cuningham, R.K., Zissman, M.A.: Evaluating intrusion detection systems: the 1998 darpa off-line intrusion detection evaluation. Descex 2, 1012–1027 (2000)spa
dcterms.referencesMcHugh, J.: Testing intrusion detection systems: a critique of the 1998 and 1999 darpa instrusion detection systems evaluation as performed by lyncoln laboratory. ACM Transactions on Information and Systems Security 3(4), 262–294 (2000)spa
dcterms.referencesNetwork Security Lab - Knowledge Discovery and Data MininG (NSL-KDD) (2007), http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.htmlspa
dcterms.referencesPadilla, P., López, M., Górriz, J.M., Ramírez, J., Salas-González, D., Álvarez, I.: The Alzheimer’s Disease Neuroimaging Initiative. NMF-SVM based CAD tool applied to functional brain images for the diagnosis of Alzheimer’s disease. IEEE Transactions on Medical Imaging 2, 207–216 (2012)spa
dcterms.referencesPanda, M., Abraham, A., Patra, M.R.: Discriminative multinomial naïve bayes for network intrusion detection. In: Proc. of the 6th International Conference on Information Assurance and Security, IAS (2010)spa
dcterms.referencesRiveiro, M., Johansson, F., Falkman, G., Ziemke, T.: Supporting maritime situation awareness using self organizing maps and gaussian mixture models. In: Proceedings of the 2008 Conference on Tenth Scandinavian Conference on Artificial Intelligence (SCAI), vol. 1, pp. 84–91 (2008)spa
dcterms.referencesTheodoridis, S., Koutroumbas, K.: Pattern Recognition. Academic Press (2009)spa
dcterms.referencesVesanto, J., Himberg, J., Alhoniemi, E., Parhankangas, J.: Som toolbox. Helsinki University of Technology (2000)spa
dc.type.hasVersioninfo:eu-repo/semantics/draftspa
dc.source.urlhttps://link.springer.com/chapter/10.1007/978-3-642-38679-4_53spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.identifier.doihttps://doi.org/10.1007/978-3-642-38679-4_53


Files in this item

Thumbnail
Thumbnail

This item appears in the following Collection(s)

Show simple item record

Attribution-NonCommercial-ShareAlike 4.0 International
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-ShareAlike 4.0 International