A Game Theoretic Framework for Adversarial Classification
Technical Report,27 Sep 2012,26 Sep 2015
University of Texas at Dallas Richardson United States
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Many real world applications, ranging from spam filtering to intrusion detection, are facing malicious adversaries who actively transform the objects under their control to avoid detection. Unfortunately, traditional machine learning techniques are insufficient to handle such adversarial problems directly. Adversaries change the dynamics in standard settings where machine learning techniques are designed to excel. They adopt their attacks to deceive the machine learning models built using the past data.
- Operations Research