VisFlowCluster-IP: Connectivity-Based Visual Clustering of Network Hosts
Xiaoxin Yin, William J. Yurcik, Adam Slagell · Kluwer Academic Publishers eBooks · 2006
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, especially for detecting newly emerging attacks, network traffic visualization has become a promising technique for assisting network administrators to monitor network traffic and detect abnormal behaviors. In this paper we present VisFlowCluster-IP , a powerful tool for visualizing 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 arrange 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 connected hosts in the visualization space. We present three real cases that validate the effectiveness of VisFlowCluster-IP in identifying abnormal behaviors. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.