Please use this identifier to cite or link to this item:
http://hdl.handle.net/2080/1374
Title: | Anomaly Detection in Ethernet Networks Using Self Organizing Maps |
Authors: | Panigrahy, S K Mahapatra, J R Mohanty, J Jena, S K |
Keywords: | Intrusion detection anomaly detection self organizing map neural networks |
Issue Date: | Jan-2011 |
Publisher: | Springer |
Citation: | International Conference on Advances in Computing, Communication and Control, 2011 (ICAC3'11), P 300-305 |
Series/Report no.: | CCIS;Vol 125 |
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. |
Description: | Copyright belongs to Proceedings Publisher-Springer Verlag |
URI: | http://dx.doi.org/10.1007/978-3-642-18440-6_38 http://hdl.handle.net/2080/1374 |
ISBN: | 978-3-642-18439-0 |
Appears in Collections: | Conference Papers |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.