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In this paper we present an approach for identifying networkanomalies by visualizing network flow data which is stored inweblogs. Various clustering techniques can be used to identifydifferent anomalies in the network. Here, we present a newapproach based on simple K-Means for analyzing networkflow data using different attributes like IP address, Protocol,Port number etc. to detect anomalies. By using visualization,we can identify which sites are more frequently accessed bythe users. In our approach we provide overview about givendataset by studying network key parameters. In this processwe used preprocessing techniques to eliminate unwantedattributes from weblog data.

Identifying Network Anomalies Using Clustering Technique in Weblog Data