Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Off-Line Computation of Stackelberg Solutions with the Genetic Algorithm
Computational Economics
Mining time-changing data streams
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Data Mining and Knowledge Discovery
Learning nonstationary models of normal network traffic for detecting novel attacks
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
A robust minimax approach to classification
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Prediction, Learning, and Games
Prediction, Learning, and Games
Nightmare at test time: robust learning by feature deletion
ICML '06 Proceedings of the 23rd international conference on Machine learning
Towards systematic evaluation of the evadability of bot/botnet detection methods
WOOT'08 Proceedings of the 2nd conference on USENIX Workshop on offensive technologies
Stackelberg games for adversarial prediction problems
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Adversarial support vector machine learning
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Randomizing smartphone malware profiles against statistical mining techniques
DBSec'12 Proceedings of the 26th Annual IFIP WG 11.3 conference on Data and Applications Security and Privacy
An efficient adversarial learning strategy for constructing robust classification boundaries
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
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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.