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Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/1374

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contributor.authorPanigrahy, S K-
contributor.authorMahapatra, J R-
contributor.authorMohanty, J-
contributor.authorJena, S K-
date.accessioned2011-02-02T10:12:47Z-
date.available2011-02-02T10:12:47Z-
date.issued2011-01-
identifier.citationInternational Conference on Advances in Computing, Communication and Control, 2011 (ICAC3'11), P 300-305en
identifier.isbn978-3-642-18439-0-
identifier.urihttp://dx.doi.org/10.1007/978-3-642-18440-6_38-
identifier.urihttp://hdl.handle.net/2080/1374-
descriptionCopyright belongs to Proceedings Publisher-Springer Verlagen
description.abstractAnomaly detection attempts to recognize abnormal behavior to detect intrusions. We have concentrated to design a prototype UNIX Anomaly Detection System. Neural Networks are tolerant of imprecise data and uncertain information. A tool has been devised for detecting such intrusions into the network. The tool uses the machine learning approaches and clustering techniques like Self Organizing Map and compares it with the K-means approach. Our system is described for applying hierarchical unsupervised neural network to intrusion detection system.en
format.extent219251 bytes-
format.mimetypeapplication/pdf-
language.isoen-
publisherSpringeren
relation.ispartofseriesCCIS;Vol 125-
subjectIntrusion detectionen
subjectanomaly detectionen
subjectself organizing mapen
subjectneural networksen
titleAnomaly Detection in Ethernet Networks Using Self Organizing Mapsen
typeArticleen
typeBook chapteren
Appears in Collections:Conference Papers

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