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[CCICADA-announce] DIMACS/CCICADA Interdisciplinary Seminar Series - Monday, October 4, 2010

Linda Casals lindac at dimacs.rutgers.edu
Mon Oct 4 09:42:08 EDT 2010


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    DIMACS/CCICADA Interdisciplinary Seminar Series Presents
               
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Title: Metric Forensics: A Multi-Level Approach for Mining Volatile Graphs

Speaker: Tina Eliassi-Rad, Rutgers University

Date: Monday, October 4, 2010 12:00 - 1:00 pm

Location: DIMACS Center, CoRE Bldg, Room 431, Rutgers University, 
           Busch Campus, Piscataway, NJ

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Abstract:

Advances in data collection and storage capacity have made it
increasingly possible to collect highly volatile graph data for
analysis. Existing graph analysis techniques are not appropriate for
such data, especially in cases where streaming or near real-time
results are required. An example that has drawn significant research
interest is the cyber-security domain, where internet communication
traces are collected and real-time discovery of events, behaviors,
patterns, and anomalies is desired. We propose Metric Forensics, a
scalable framework for analysis of volatile graphs. Metric Forensics
combines a multi-level "drill down" approach, a collection of
user-selected graph metrics, and a collection of analysis
techniques. At each successive level, more sophisticated metrics are
computed and the graph is viewed at finer temporal resolutions. In
this way, Metric Forensics scales to highly volatile graphs by only
allocating resources for computationally expensive analysis when an
interesting event is discovered at a coarser resolution first. We test
Metric Forensics on three real-world graphs: an enterprise IP trace, a
trace of legitimate and malicious network traffic from a research
institution, and the MIT Reality Mining proximity sensor data. Our
largest graph has ~3M vertices and ~32M edges, spanning 4.5 days. The
results demonstrate the scalability and capability of Metric Forensics
in analyzing volatile graphs; and highlight four novel phenomena in
such graphs: elbows, broken correlations, prolonged spikes, and
lightweight stars.

Bio: Tina Eliassi-Rad is an Assistant Professor at the Department of
Computer Science at Rutgers University. She is also a member of the
Rutgers Center for Computational Biomedicine, Imaging, and Modeling
(CBIM) and Rutgers Center for Cognitive Science (RuCCS). Until
September 2010, Tina was a Member of Technical Staff at Lawrence
Livermore National Laboratory. Tina earned her Ph.D. in Computer
Sciences (with a minor in Mathematical Statistics) at the University
of Wisconsin-Madison in 2001. Broadly speaking, Tina's research
interests include machine learning, data mining, and artificial
intelligence. Her work has been applied to the World-Wide Web, text
corpora, large-scale scientific simulation data, and complex
networks. Tina is an action editor for the Data Mining and Knowledge
Discovery Journal. She received a US DOE Office of Science Outstanding
Mentor Award in 2010. For more details, visit http://eliassi.org.



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