Exploring Anomalous Behavior in Wireless Networks with Visual Analytics

Traffic on wireless networks generates metadata in the form of logs on a continuous basis across the various servers involved in the communication session. These networks are engineered for high reliability and only a small proportion of the logs represent anomalous behavior. Such anomalies are indicators of vulnerabilities in the network. In this paper and poster we present ongoing work on applying visual analytics for the exploration of anomalous behavior in wireless networks.