SPAM DETECTION BY STACKELBERG GAME

Many data mining applications, ranging from Spam filtering to intrusion detection, are forced with active adversaries. Adversary deliberately manipulate data in order to reduce the classifier's accuracy, in all these applications, initially successful classifiers will degrade easily. In this paper we model the interaction between the adversary and the classifier as a two person sequential non cooperative Stackelberg game and analyze the payoff when there is a leader and a follower. We then proceed to model the interaction as an optimization problem and solve it with evolutionary strategy. Our experimental results are promising; since they show that our approach improves accuracy spam detection on several real world data sets.

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