Multiresolution Semantic Visualization of Network Traffic

Abstract

Multi resolution semantic analysis of data involves inferring increasing levels of meaning from data. Due to the large volume and complexity of network data, multi resolution visualizations allow the user to rapidly focus on meaningful and relevant information. Current tools and techniques for visual analysis of network data are limited in their ability to operate at semantically relevant resolutions and impose great cognitive burden on users to manually infer semantics from low-level details. Extending our previous work that allows users to define semantics as abstract models, we apply these models to construct multi resolution visualizations of network traffic data. Our methodology for visual exploration allows the user to rapidly analyze and understand network traces, by providing intuitive and interactive representations of the network. We demonstrate the effectiveness of our approach by applying it to analyzing network trace data from a cyber security incident involving DNS cache poisoning.