In this paper we present an approach for identifying network anomalies by visualizing network flow data which is stored in weblogs. Various clustering techniques can be used to identify different anomalies in the network. Here, we present a new approach based on simple K-Means for analyzing network flow 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 by the users. In our approach we provide overview about given dataset by studying network key parameters. In this process we used preprocessing techniques to eliminate unwanted attributes from weblog data.
In this process they used preprocessing techniques to eliminate unwanted attributes from weblog data REF .
Identifying Network Anomalies Using Clustering Technique in Weblog Data
BIOINFORMATICS 2012
Computer Science