Presenter: Eugene Tan – Computer and Information Science
Faculty Mentor(s): Reza Rejaie, Chris Misa
Session: (Virtual) Poster Presentation
A critical challenge in understanding the traffic flowing through modern computer networks is the visualization of traffic features associated with a large number of networked devices. These observed IP addresses from these devices are known to cluster within IP address prefixes formulating a multifractal structure. Leveraging the Hilbert curve we simultaneously visualize the multifractal structure of these observed addresses and the traffic features associated with each address, enabling new observations to be made by combining both aspects of network traffic data into a single visual presentation. This is done through the Hilbert curve’s property of locality which enables addresses sharing the same prefix to be mapped to similar locations within the curve resulting in observable clusters in regions of the visualization. We approach this challenge by implementing this visualization tool of mapping addresses to the Hilbert space, utilizing color theory to draw visual feature relationships and patterns that may appear. Therefore, the primary goal of this work is to leverage this visualization tool to examine the relationships between traffic features and the multifractal distribution of observed addresses through a series of case studies.