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 ad 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. © 2011 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Panigrahy, S. K., Mahapatra, J. R., Mohanty, J., & Jena, S. K. (2011). Anomaly detection in ethernet networks using self organizing maps. In Communications in Computer and Information Science (Vol. 125 CCIS, pp. 300–305). https://doi.org/10.1007/978-3-642-18440-6_38
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