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With the increasing number of hostile network attacks, anomaly detection for network security has become an urgent task. As there have not been highly effective solutions for automatic intrusion detection, es- pecially for detecting newly emerging attacks, network traffic visualiza- tion has become a promising technique for assisting network administra- tors to monitor network traffic and detect abnormal behaviors. In this paper we present VisFlowCluster-IP, a powerful tool for visualiz- ing network traffic flows using network logs. It models the network as a graph by modeling hosts as graph nodes. It utilizes the force model to ar- range graph nodes on a two-dimensional space, so that groups of related nodes can be visually clustered in a manner apparent to human eyes. We also propose an automated method for finding clusters of closely con- nected hosts in the visualization space. We present three real cases that validate the effectiveness of VisFlowCluster-IP in identifying abnormal behaviors.

VisFlowCluster-IP: Connectivity-Based Visual Clustering of Network Hosts