Tag: Chris Misa

On the Multifractal Structure of Observed Internet Addresses

Presenter: Megan Walter – Computer and Information Science

Faculty Mentor(s): Reza Rejaie, Chris Misa

Session: (Virtual) Oral Panel—Inner Space and Internet

As a result of society’s increasing dependence on the internet, we observe an uptick in internet attacks and network management issues. However, the growing speed and volume of internet traffic makes finding portions of traffic responsible for creating problems difficult. Current approaches to classifying connections as harmful or benign tend to regard each connection independently of one another. However, the nature of IP addresses points to correlations between addresses located in similar parts of the IP address space. Understanding the structural characteristics of the IP address space could lead to novel ways to create network management algorithms that deal with aggregates of flows.

We examine the structure of observed IP addresses in network traffic collected from border routers at the University of Oregon. Previous work indicates that the characteristics of observed IPv4 address structures are consistent with a multifractal model. We work to solidify the existence of this multifractal structure and provide an initial contribution to the development of network security and management solutions that aggregate flows by IP address. We use a brand new method of multifractal analysis using the method of moments to produce an initial characterization of how observed IPv4 addresses relate to one another. We applied this process across traffic samples representing three different timescales, allowing us to look at the temporal dynamics of these multifractal characteristics.

Visualizations of the IP Address Space with Hilbert Curves to Expose Multifractal Patterns

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.