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dc.contributor.authorPanigrahy, S K-
dc.contributor.authorMahapatra, J R-
dc.contributor.authorMohanty, J-
dc.contributor.authorJena, S K-
dc.identifier.citationInternational Conference on Advances in Computing, Communication and Control, 2011 (ICAC3'11), P 300-305en
dc.descriptionCopyright belongs to Proceedings Publisher-Springer Verlagen
dc.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
dc.format.extent219251 bytes-
dc.relation.ispartofseriesCCIS;Vol 125-
dc.subjectIntrusion detectionen
dc.subjectanomaly detectionen
dc.subjectself organizing mapen
dc.subjectneural networksen
dc.titleAnomaly Detection in Ethernet Networks Using Self Organizing Mapsen
dc.typeBook chapteren
Appears in Collections:Conference Papers

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