Network Anomaly Detection with Bayesian Self-Organizing Maps
The 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. How...
- Autores:
-
De-La-Hoz-Franco, Emiro
Ortiz García, Andrés
Ortega Lopera, Julio
De la Hoz Correa, Eduardo Miguel
Prieto Espinosa, Carlos Antonio
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2013
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/7247
- Acceso en línea:
- https://hdl.handle.net/11323/7247
https://doi.org/10.1007/978-3-642-38679-4_53
https://repositorio.cuc.edu.co/
- Palabra clave:
- Gaussian Mixture Model
Intrusion Detection System
Receiver Operating Curf Curve
Best Match Unit
Receiver Operating Curf
- Rights
- openAccess
- License
- Attribution-NonCommercial-ShareAlike 4.0 International
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Network Anomaly Detection with Bayesian Self-Organizing Maps |
| title |
Network Anomaly Detection with Bayesian Self-Organizing Maps |
| spellingShingle |
Network Anomaly Detection with Bayesian Self-Organizing Maps Gaussian Mixture Model Intrusion Detection System Receiver Operating Curf Curve Best Match Unit Receiver Operating Curf |
| title_short |
Network Anomaly Detection with Bayesian Self-Organizing Maps |
| title_full |
Network Anomaly Detection with Bayesian Self-Organizing Maps |
| title_fullStr |
Network Anomaly Detection with Bayesian Self-Organizing Maps |
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Network Anomaly Detection with Bayesian Self-Organizing Maps |
| title_sort |
Network Anomaly Detection with Bayesian Self-Organizing Maps |
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De-La-Hoz-Franco, Emiro Ortiz García, Andrés Ortega Lopera, Julio De la Hoz Correa, Eduardo Miguel Prieto Espinosa, Carlos Antonio |
| dc.contributor.author.spa.fl_str_mv |
De-La-Hoz-Franco, Emiro Ortiz García, Andrés Ortega Lopera, Julio De la Hoz Correa, Eduardo Miguel Prieto Espinosa, Carlos Antonio |
| dc.subject.spa.fl_str_mv |
Gaussian Mixture Model Intrusion Detection System Receiver Operating Curf Curve Best Match Unit Receiver Operating Curf |
| topic |
Gaussian Mixture Model Intrusion Detection System Receiver Operating Curf Curve Best Match Unit Receiver Operating Curf |
| description |
The 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. |
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2013 |
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2013 |
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2020-11-10T21:57:45Z |
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2020-11-10T21:57:45Z |
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Artículo de revista |
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de 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_53 |
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978-3-642-38679-4 |
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https://hdl.handle.net/11323/7247 |
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https://doi.org/10.1007/978-3-642-38679-4_53 |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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de 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_53 978-3-642-38679-4 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
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https://hdl.handle.net/11323/7247 https://doi.org/10.1007/978-3-642-38679-4_53 https://repositorio.cuc.edu.co/ |
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eng |
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eng |
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Alhoniemi, 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) Ghosh, 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) Haykin, S.: Neural Networks, 2nd edn. Prentice-Hall (1999) Hoffman, 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) Kohonen, T.: Self-Organizing Maps. Springer (2001) Lippmann, 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) McHugh, 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) Network Security Lab - Knowledge Discovery and Data MininG (NSL-KDD) (2007), http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html Padilla, 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) Panda, 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) Riveiro, 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) Theodoridis, S., Koutroumbas, K.: Pattern Recognition. Academic Press (2009) Vesanto, J., Himberg, J., Alhoniemi, E., Parhankangas, J.: Som toolbox. Helsinki University of Technology (2000) |
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De-La-Hoz-Franco, EmiroOrtiz García, AndrésOrtega Lopera, JulioDe la Hoz Correa, Eduardo MiguelPrieto Espinosa, Carlos Antonio2020-11-10T21:57:45Z2020-11-10T21:57:45Z2013de 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_53978-3-642-38679-4https://hdl.handle.net/11323/7247https://doi.org/10.1007/978-3-642-38679-4_53Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The 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.De-La-Hoz-Franco, Emiro-will be generated-orcid-0000-0002-4926-7414-600Ortiz García, AndrésOrtega Lopera, JulioDe la Hoz Correa, Eduardo Miguel-will be generated-orcid-0000-0001-7468-6058-600Prieto Espinosa, Carlos Antonio-will be generated-orcid-0000-0001-9133-4636-600application/pdfengCorporación Universidad de la CostaAttribution-NonCommercial-ShareAlike 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Advances in Computational Intelligencehttps://link.springer.com/chapter/10.1007/978-3-642-38679-4_53Gaussian Mixture ModelIntrusion Detection SystemReceiver Operating Curf CurveBest Match UnitReceiver Operating CurfNetwork Anomaly Detection with Bayesian Self-Organizing MapsArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersionAlhoniemi, 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)Ghosh, 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)Haykin, S.: Neural Networks, 2nd edn. Prentice-Hall (1999)Hoffman, 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)Kohonen, T.: Self-Organizing Maps. Springer (2001)Lippmann, 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)McHugh, 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)Network Security Lab - Knowledge Discovery and Data MininG (NSL-KDD) (2007), http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.htmlPadilla, 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)Panda, 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)Riveiro, 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)Theodoridis, S., Koutroumbas, K.: Pattern Recognition. Academic Press (2009)Vesanto, J., Himberg, J., Alhoniemi, E., Parhankangas, J.: Som toolbox. Helsinki University of Technology (2000)PublicationORIGINALNetwork Anomaly Detection with Bayesian Self - 2013.pdfNetwork Anomaly Detection with Bayesian Self - 2013.pdfapplication/pdf557937https://repositorio.cuc.edu.co/bitstreams/deb1d93c-82b4-4207-8ab6-92d96a32c864/download32780579afb9ee909121ae8325d9d5d5MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-81031https://repositorio.cuc.edu.co/bitstreams/9e34955a-ab75-424d-92d7-9cd4fd11479c/download934f4ca17e109e0a05eaeaba504d7ce4MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/77b94c04-287e-4183-96fc-ce274301aede/downloade30e9215131d99561d40d6b0abbe9badMD53THUMBNAILNetwork Anomaly Detection with Bayesian Self - 2013.pdf.jpgNetwork Anomaly Detection with Bayesian Self - 2013.pdf.jpgimage/jpeg42807https://repositorio.cuc.edu.co/bitstreams/aa8c5eb0-ecf4-4494-a051-0eeb0ef13eee/download91c4b8405e6a3a3b754a19b7c28d40e2MD54TEXTNetwork Anomaly Detection with Bayesian Self - 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