PCA filtering and probabilistic SOM for network intrusion detection
The growth of the Internet and, consequently, the number of interconnected computers, has exposed significant amounts of information to intruders and attackers. Firewalls aim to detect violations according to a predefined rule-set and usually block potentially dangerous incoming traffic. However, wi...
- Autores:
-
De la Hoz Correa, Eduardo Miguel
De la Hoz, Emiro
Ortiz, Andrés
Ortega, Julio
Prieto, Beatriz
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2015
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/1011
- Acceso en línea:
- https://hdl.handle.net/11323/1011
https://repositorio.cuc.edu.co/
- Palabra clave:
- Bayesian SOM
IDS
PCA filtering
Probabilistic SOM
Self-organizing maps
- Rights
- openAccess
- License
- Atribución – No comercial – Compartir igual
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oai:repositorio.cuc.edu.co:11323/1011 |
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|
dc.title.eng.fl_str_mv |
PCA filtering and probabilistic SOM for network intrusion detection |
title |
PCA filtering and probabilistic SOM for network intrusion detection |
spellingShingle |
PCA filtering and probabilistic SOM for network intrusion detection Bayesian SOM IDS PCA filtering Probabilistic SOM Self-organizing maps |
title_short |
PCA filtering and probabilistic SOM for network intrusion detection |
title_full |
PCA filtering and probabilistic SOM for network intrusion detection |
title_fullStr |
PCA filtering and probabilistic SOM for network intrusion detection |
title_full_unstemmed |
PCA filtering and probabilistic SOM for network intrusion detection |
title_sort |
PCA filtering and probabilistic SOM for network intrusion detection |
dc.creator.fl_str_mv |
De la Hoz Correa, Eduardo Miguel De la Hoz, Emiro Ortiz, Andrés Ortega, Julio Prieto, Beatriz |
dc.contributor.author.spa.fl_str_mv |
De la Hoz Correa, Eduardo Miguel De la Hoz, Emiro Ortiz, Andrés Ortega, Julio Prieto, Beatriz |
dc.subject.eng.fl_str_mv |
Bayesian SOM IDS PCA filtering Probabilistic SOM Self-organizing maps |
topic |
Bayesian SOM IDS PCA filtering Probabilistic SOM Self-organizing maps |
description |
The growth of the Internet and, consequently, the number of interconnected computers, has exposed significant amounts of information to intruders and attackers. Firewalls aim to detect violations according to a predefined rule-set and usually block potentially dangerous incoming traffic. However, with the evolution of attack techniques, it is more difficult to distinguish anomalies from normal traffic. Different detection approaches have been proposed, including the use of machine learning techniques based on neural models such as Self-Organizing Maps (SOMs). In this paper, we present a classification approach that hybridizes statistical techniques and SOM for network anomaly detection. Thus, while Principal Component Analysis (PCA) and Fisher Discriminant Ratio (FDR) have been considered for feature selection and noise removal, Probabilistic Self-Organizing Maps (PSOM) aim to model the feature space and enable distinguishing between normal and anomalous connections. |
publishDate |
2015 |
dc.date.issued.none.fl_str_mv |
2015 |
dc.date.accessioned.none.fl_str_mv |
2018-11-14T21:20:38Z |
dc.date.available.none.fl_str_mv |
2018-11-14T21:20:38Z |
dc.type.spa.fl_str_mv |
Artículo de revista |
dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_2df8fbb1 |
dc.type.coar.spa.fl_str_mv |
http://purl.org/coar/resource_type/c_6501 |
dc.type.content.spa.fl_str_mv |
Text |
dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.redcol.spa.fl_str_mv |
http://purl.org/redcol/resource_type/ART |
dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/acceptedVersion |
format |
http://purl.org/coar/resource_type/c_6501 |
status_str |
acceptedVersion |
dc.identifier.issn.spa.fl_str_mv |
0925-2312 |
dc.identifier.uri.spa.fl_str_mv |
https://hdl.handle.net/11323/1011 |
dc.identifier.instname.spa.fl_str_mv |
Corporación Universidad de la Costa |
dc.identifier.reponame.spa.fl_str_mv |
REDICUC - Repositorio CUC |
dc.identifier.repourl.spa.fl_str_mv |
https://repositorio.cuc.edu.co/ |
identifier_str_mv |
0925-2312 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
url |
https://hdl.handle.net/11323/1011 https://repositorio.cuc.edu.co/ |
dc.language.iso.none.fl_str_mv |
eng |
language |
eng |
dc.rights.spa.fl_str_mv |
Atribución – No comercial – Compartir igual |
dc.rights.accessrights.spa.fl_str_mv |
info:eu-repo/semantics/openAccess |
dc.rights.coar.spa.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
rights_invalid_str_mv |
Atribución – No comercial – Compartir igual http://purl.org/coar/access_right/c_abf2 |
eu_rights_str_mv |
openAccess |
dc.publisher.spa.fl_str_mv |
Neurocomputing |
dc.source.spa.fl_str_mv |
Neurocomputing |
institution |
Corporación Universidad de la Costa |
dc.source.url.spa.fl_str_mv |
https://www.sciencedirect.com/science/article/abs/pii/S0925231215002982 |
bitstream.url.fl_str_mv |
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repdigital@cuc.edu.co |
_version_ |
1811760828547858432 |
spelling |
De la Hoz Correa, Eduardo MiguelDe la Hoz, EmiroOrtiz, AndrésOrtega, JulioPrieto, Beatriz2018-11-14T21:20:38Z2018-11-14T21:20:38Z20150925-2312https://hdl.handle.net/11323/1011Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The growth of the Internet and, consequently, the number of interconnected computers, has exposed significant amounts of information to intruders and attackers. Firewalls aim to detect violations according to a predefined rule-set and usually block potentially dangerous incoming traffic. However, with the evolution of attack techniques, it is more difficult to distinguish anomalies from normal traffic. Different detection approaches have been proposed, including the use of machine learning techniques based on neural models such as Self-Organizing Maps (SOMs). In this paper, we present a classification approach that hybridizes statistical techniques and SOM for network anomaly detection. Thus, while Principal Component Analysis (PCA) and Fisher Discriminant Ratio (FDR) have been considered for feature selection and noise removal, Probabilistic Self-Organizing Maps (PSOM) aim to model the feature space and enable distinguishing between normal and anomalous connections.De la Hoz Correa, Eduardo Miguel-will be generated-orcid-0000-0001-7468-6058-0De la Hoz, Emiro-will be generated-orcid-0000-0002-4926-7414-600Ortiz, Andrés-3743e2e5-f13e-4950-8c12-d42d0ab7ccfe-0Ortega, Julio-3b8c20e7-bbcc-4bbd-8ad8-37acc5756525-0Prieto, Beatriz-36d7ad57-9aa1-4c19-82c0-422bdfb40aa7-0engNeurocomputingAtribución – No comercial – Compartir igualinfo:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Neurocomputinghttps://www.sciencedirect.com/science/article/abs/pii/S0925231215002982Bayesian SOMIDSPCA filteringProbabilistic SOMSelf-organizing mapsPCA filtering and probabilistic SOM for network intrusion detectionArtículo 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/acceptedVersionPublicationORIGINALPCA filtering and probabilistic SOM for network intrusion detection.pdfPCA filtering and probabilistic SOM for network intrusion detection.pdfapplication/pdf177764https://repositorio.cuc.edu.co/bitstreams/3d4f6a9a-70d2-4090-8bf2-9b7aac33d937/download7067826286c106335df9d2fa0a36ab53MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorio.cuc.edu.co/bitstreams/416384b7-73df-4c19-9d04-96e3e4d3e34b/download8a4605be74aa9ea9d79846c1fba20a33MD52THUMBNAILPCA filtering and probabilistic SOM for network intrusion detection.pdf.jpgPCA filtering and probabilistic SOM for network intrusion detection.pdf.jpgimage/jpeg36188https://repositorio.cuc.edu.co/bitstreams/ad987693-9594-408b-a113-c8d77c0596fb/download5a14cd246f1f676110c2d9c43795ede4MD54TEXTPCA filtering and probabilistic SOM for network intrusion detection.pdf.txtPCA filtering and probabilistic SOM for network intrusion detection.pdf.txttext/plain1242https://repositorio.cuc.edu.co/bitstreams/74289359-1519-43b5-a675-694835af5197/download3b92c8a082366409f53868bdc0f4fe32MD5511323/1011oai:repositorio.cuc.edu.co:11323/10112024-09-17 14:06:31.184open.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.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 |