Spam filtering

Abstract

Extending pattern classification theory and design methods to adversarial settings is thus a novel and very relevant research direction, which has not yet been pursued in a systematic way. The system evaluates at design phase the security of pattern classifiers, namely, the performance degradation under potential attacks they may incur during operation. A framework is used for evaluation of classifier security that formalizes and generalizes the training and testing datasets. Pattern classification systems are commonly used in adversarial applications, like biometric authentication, network intrusion detection, and spam filtering, in which data can be purposely manipulated by humans to undermine their operation.
Keywords-Pattern classification; adversarial classification; performance evaluation security evaluation; robustness evaluation.