Anomaly Detection in Ethernet Networks Using Self Organizing Maps

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.