[Dmbu-l] Fwd: Limits of Data Mining in Malicious Activity Detection: Friday @ 11AM, MCS 148

Charalampos Mavroforakis cmav at bu.edu
Thu Feb 6 15:46:44 EST 2014

Hi all,

This is a reminder for tomorrow's talk.


*Limits of Data Mining in Malicious Activity Detection*
*Murat Kantarcioglu, Director of UTD Data Security and Privacy Lab,
University of Texas at Dallas *
*Friday, February 7, 2013 at 11AM in MCS 148*

*Abstract: *Many data mining applications, such as spam filtering and
detection, are faced with active adversaries. In all these applications, the
future data sets and the training data set are no longer from the same
population, due to the transformations employed by the adversaries. Hence a
main assumption for the existing data mining techniques no longer holds and
initially successful data mining models degrade easily. This becomes a game
between the adversary and the data miner: The adversary modifies its
strategy to avoid being detected by the current classifier; the data miner
then updates its classifier based on the new threats. In this talk, we
investigate the possibility of an equilibrium in this seemingly never ending
game, where neither party has an incentive to change. Modifying the data
mining algorithm causes too many false positives with too little increase in
true positives; changes by the adversary decrease the utility of the false
negative items that are not detected. We discuss our game theoretic
framework where equilibrium behavior of adversarial classification
applications can be analyzed, and provide solutions for finding an
equilibrium point. A classifier's equilibrium performance indicates its
eventual success or failure. The data miner could then select attributes
based on their equilibrium performance, and construct an effective data
mining model. In addition, we discuss how our framework could be applied for
building support vector machines that are more resilient to adversarial

In the remainder of this talk, we discuss the implications of our game
theoretic adversarial data mining framework in the context of social network
mining. We discuss how data mining techniques could be applied to predict
undisclosed private information. More specifically, we discuss how to launch
inference attacks using released social networking data to predict
undisclosed private information about individuals, such as their political
affiliation or sexual orientation. We then discuss various techniques that
could be employed to prevent learning of such sensitive data and the
effectiveness of these techniques in practice. We show that we can decrease
the effectiveness of data mining algorithms by sanitizing data.
*Speaker Bio:*Dr. Murat Kantarcioglu is an Associate Professor in the
Computer Science Department and Director of the UTD Data Security and
Privacy Lab at the University of Texas at Dallas. He holds a B.S. in
Computer Engineering from Middle East Technical University, and M.S. and
Ph.D degrees in Computer Science from Purdue University. He is a recipient
of NSF CAREER award and Purdue CERIAS Diamond Award for Academic
excellence. Currently, he is a visiting scholar at Harvard Data Privacy Lab.
Dr. Kantarcioglu's research focuses on creating technologies that can
extract useful information from any data without sacrificing privacy or
security. His research has been supported by grants from NSF, AFOSR, ONR,
NSA, and NIH.  He has published over 100 peer reviewed papers. Some of his
research work has been covered by the media outlets such as Boston Globe,
ABC News  etc. and has received two best paper awards.
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