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http://hdl.handle.net/2080/1374
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| DC Field | Value | Language |
|---|
| contributor.author | Panigrahy, S K | - |
| contributor.author | Mahapatra, J R | - |
| contributor.author | Mohanty, J | - |
| contributor.author | Jena, S K | - |
| date.accessioned | 2011-02-02T10:12:47Z | - |
| date.available | 2011-02-02T10:12:47Z | - |
| date.issued | 2011-01 | - |
| identifier.citation | International Conference on Advances in Computing, Communication and Control, 2011 (ICAC3'11), P 300-305 | en |
| identifier.isbn | 978-3-642-18439-0 | - |
| identifier.uri | http://dx.doi.org/10.1007/978-3-642-18440-6_38 | - |
| identifier.uri | http://hdl.handle.net/2080/1374 | - |
| description | Copyright belongs to Proceedings Publisher-Springer Verlag | en |
| description.abstract | Anomaly 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.extent | 219251 bytes | - |
| format.mimetype | application/pdf | - |
| language.iso | en | - |
| publisher | Springer | en |
| relation.ispartofseries | CCIS;Vol 125 | - |
| subject | Intrusion detection | en |
| subject | anomaly detection | en |
| subject | self organizing map | en |
| subject | neural networks | en |
| title | Anomaly Detection in Ethernet Networks Using Self Organizing Maps | en |
| type | Article | en |
| type | Book chapter | en |
| Appears in Collections: | Conference Papers
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Description |
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| adsom.pdf | | 214Kb | Adobe PDF | View/Open |
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