Tag: Reza Rejaie

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.

Determining the Location of Connections Among Top Internet Service Providers in the United States

Presenter: Phillip Kriegel

Faculty Mentor: Reza Rejaie

Presentation Type: Poster 20

Primary Research Area: Science

Major: Computer Science, Mathematics

Funding Source: Research Experiences for Undergraduates (NSF), $1500 a month

The Internet is a network of networks. There are more than 40,000 networks that create what we understand as the Internet today. Understanding where and how these networks interconnect is important for the purpose of meaningfully investigating a wide range of critical Internet-related problems, such as the vulnerability of the Internet to physical damage, such as earthquakes or power surges. Published work on Internet topology and structure focus primarily on finding the existence of these interconnections, and characterize the structure of the Internet based solely on these connections. By using RIPE Atlas, a distributed traceroute software, in addition to other methods, it is possible to estimate which networks exchange traffic in which cities. The purpose of this thesis is to gain a better understanding of the geographic nature of the Internet by pinpointing exactly where these interconnections occur on a physical level. This project is able to provide a city level understanding of the autonomous systems present in each city, as well as which of those systems connect. This serves as a resource for further research.

Characterizing the Structure of Twitter Network Through Socially-Aware Clustering of Users

Presenter(s): Eugene Tan—Computer Information Science

Faculty Mentor(s): Reza Rejaie

Session: Prerecorded Poster Presentation

Popular online social networks (OSN) such as Twitter form a networked system where millions of users interconnect and exchange information . Characterizing the structural properties of the resulting”relationship graph” among the OSN users is very informative but inherently challenging because of its huge size and complex connectivity patterns . This project explores a novel “socially- aware” approach to classify Twitter users and thus partition the structure of Twitter relationship graph . To this end, we consider the top 10K most-followed Twitter users, called Twitter elite, and show that these users form coherent and socially meaningful communities, called Twitter elite communities . We define a “social interest vector” for each regular (i .e . non-elite) Twitter user where each element of this vector captures the user’s relative level of interest to a specific elite community based on the fraction of her followings in that elite community . We then rely on this multi-dimensional measure of user’s social interest to cluster millions of randomly selected Twitter users . We collect profile information, list of friends and followers along with available tweets for selected Twitter users in each cluster to assess (i) whether the resulting clusters of users are socially coherent, (ii) relative degree of connectivity between different pairs of clusters, and (iii) the key social attributes of each cluster . Overall, our analysis will illustrate if elite communities can serve as “landmarks” to meaningfully classify regular Twitter users and characterize the structure of the Twitter network .

Longitudinal Analysis of Major Video Streaming Services in the US

Presenter(s): Donna Hooshmand—Computer Science, Mathematics

Faculty Mentor(s): Reza Rejaie

Session: Prerecorded Poster Presentation

This study relies on several years of NETFLOW data for exchanged traffic between the University of Oregon network (UOnet) and the Internet to perform a longitudinal analysis on the characteristics of popular Internet Applications . We develop techniques to identify connections related to video streams from their NETFLOW records . We then investigate how the fraction of UOnet traffic associated with (i .e . popularity of) major video streaming applications (e .g . YoutTube, Netflix, Amazon Prime), the basic characteristics of their video (e .g . bandwidth and duration) and their delivery mechanism have evolved over the past few years . Our empirical findings will offer valuable insights into important practical aspects of video streams services and their evolution over time .