• Computer Science
  • Published 2008

A game theoretic framework for adversarial learning

@inproceedings{Kantarcioglu2008AGT,
  title={A game theoretic framework for adversarial learning},
  author={Murat Kantarcioglu and Bowei Xi and Chris Clifton},
  year={2008}
}
Many data mining applications, ranging from spam filtering to intrusion detection, are faced with active adversaries. In all these applications, initially successful classifiers will 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 paper, we investigate the possibility of an equilibrium in this seemingly… CONTINUE READING

Citations

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Game Theoretic Optimization of Detecting Malicious Behavior

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  • 2009
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