caa a22 4500 445871504 CHVBK 20180317145513.0 cr unu---uuuuu 170323e20110101xx s 000 0 eng 10.1007/s10618-010-0197-3 doi (NATIONALLICENCE)springer-10.1007/s10618-010-0197-3 Classifier evaluation and attribute selection against active adversaries [Elektronische Daten] [Murat Kantarcıoğlu, Bowei Xi, Chris Clifton] Many data mining applications, such as spam filtering and intrusion 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 classification techniques no longer holds and initially successful classifiers 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 are not detected. We develop a 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 classifier. A case study on online lending data demonstrates how to apply the proposed game theoretic framework to a real application. The Author(s), 2010 Adversarial classification nationallicence Game theory nationallicence Attribute selection nationallicence Simulated annealing nationallicence Kantarcıoğlu Murat Computer Science Department, University of Texas at Dallas, Richardson, TX, USA aut Xi Bowei Department of Statistics, Purdue University, West Lafayette, IN, USA aut Clifton Chris Department of Computer Science, Purdue University, West Lafayette, IN, USA aut Data Mining and Knowledge Discovery Springer US; http://www.springer-ny.com 22/1-2(2011-01-01), 291-335 1384-5810 22:1-2<291 2011 22 10618 https://doi.org/10.1007/s10618-010-0197-3 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s10618-010-0197-3 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Kantarcıoğlu Murat Computer Science Department, University of Texas at Dallas, Richardson, TX, USA aut NATIONALLICENCE 700 1- Xi Bowei Department of Statistics, Purdue University, West Lafayette, IN, USA aut NATIONALLICENCE 700 1- Clifton Chris Department of Computer Science, Purdue University, West Lafayette, IN, USA aut NATIONALLICENCE 773 0- Data Mining and Knowledge Discovery Springer US; http://www.springer-ny.com 22/1-2(2011-01-01), 291-335 1384-5810 22:1-2<291 2011 22 10618 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer