A game theoretic framework for adversarial learning
Murat Kantarcıoğlu, Bowei Xi, Chris Clifton · Annual Information Security Symposium · 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 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. We develop a game theoretic framework where the equilibrium behavior of adversarial learning applications can be analyzed, and provide a solution for finding the 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 classifier.