Photos courtesy of San Francisco Convention & Visitors Bureau

 
 
Tutorial 3: Data Mining the Internet
Speakers Michalis and Christos Faloutsos
Date Monday, March 31, 2003
Time 9am - 5pm

Abstract:

In this tutorial, we address two questions: what do we know about the Internet? And, how can you learn more about it? First, we present the state of the art of what we know about modeling and simulating the Internet. Second, we present cutting edge techniques of how to further our understanding of the network.

The motivation is that despite the significant research efforts, we know very little al)out the Internet. Furthermore, most network researchers are unaware of the wealth of analysis tools from the areas of data mining and statistics. Data analysis based on averages, standard derivation and Poisson processes has exhausted its capabilities.

We present two scenarios that describe eloquently the two main thrusts of this tutorial.

  1. Scenario one (i.e., what): You want to simulate your new protocol. What topology should you use:? What is the distribution of sources and destinations? What is the traffic intensity of each connection? What kind of background traffic should you use?
  2. Scenario two (i.e., how): You just obtained large measurement data of round trip delays among several node pairs over a few hours. How can you characterize? How do you compare the delays between different end-points? How do you cluster "similar"' round-trip behavior? How can you identify abnormal behavior such as a Distributed Denial of Service Attack (DDoS)?
In a nutshell, the main goal of this tutorial is to present what we know about modeling the Internet, and how we can learn more, The tutorial intends to bridge the gap between network researchers and datamining research.
 
Foils:
Part 1 and Part 2

Presenter Biographies:
 

The instructors have been in collaboration for 4 years, with multiple joint papers. This joint work has been a fusion of the two research areas of the collaborators: networks and datamining. The work has focused on Internet modeling using the advanced data-mining techniques and has lead to discoveries that would not have been feasible otherwise.
Michalis Faloutsos received the B.Sc. degree in Electrical engineering (1993) from the National Technical University of Athens, Greece and the M.Sc. and Ph.D. degrees in Computer Science from the University of Toronto, Canada (1999). he is currently an assistant professor at the University of California Riverside. He has received the CAREER award from NSF (2000), and two major DARPA grants. He has co-authored with Christos and Petros Faloutsos the highly-cite paper "On Powerlaws and the Internet
Topology" (SIGCOMM '99), which renewed the interest of the community in modeling the Internet topology. hes interests include Internet measurements, multicast protocols, real-time communications, and wireless networks.

Christos Faloutsos received the B.Sc. degree in Electrical Engineering (1981) from the National Technical University of Athens, Greece and the M.Sc. and Ph.D. degrees in Computer Science from the University of Toronto, Canada. he is currently a professor at Carnegie Mellon University. He has received the Presidential Young Investigator Award by the National science Foundation (1989), several "best paper" awards (SIGMOD 94, VLDB 97, KDD01 (runner-up), Performance 2002 (best student paper)), and four teaching awards. He has published over 120 refereed articles, one monograph, and holds four patents. His research interests include data mining, network analysis, indexing in relational and multimedia databases.