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  \textbf{\textsc{STANFORD UNIVERSITY}}\\[5pt]
  \textbf{\textsc{DEPARTMENT OF STATISTICS}}\\[5pt]
  \Large{\textbf\textsc{{DEPARTMENTAL SEMINAR}}}
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\begin{center}
  4:15 p.m., Tuesday, July 25, 2006\\
  Sequoia Hall Room 200\\
  (Cookies at 3:45 in 1st Floor Lounge)
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\begin{center}
  \textsl{ Bowei Xi   }\\
Purdue University
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\begin{center}
  \textbf{ Adversarial Learning   } 
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\noindent
Many data mining applications, ranging from spam filtering to intrusion
detection, are faced with active adversaries. In all these applications,
initially successful classifiers could 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. We investigate the
possibility of an equilibrium in this seemingly never ending game, where
neither party has an incentive to change: Modifying the classifier 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 aren't detected.

\noindent
We develop a game theoretic framework where the equilibrium behavior of
adversarial learning applications can be analyzed, and provide a solution
for estimating the equilibrium point. The data miner could evaluate the
eventual effectiveness of its current approach, and decide whether it is
necessary to change the rules of the game (e.g., find new information as
input to the classification problem). We also describe how to apply our
techniques in a spam filtering scenario.

\noindent
This is joint work with Murat Kantarcioglu and Chris Clifton.

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