Multiple classifier systems for adversarial classification tasks

Pattern classification systems are currently used in security applications like intrusion detection in computer networks, spam filtering and biometric identity recognition. These are adversarial classification problems, since the classifier faces an intelligent adversary who adaptively modifies patterns (e.g., spam e-mails) to evade it. In these tasks the goal of a classifier is to attain both a high classification accuracy and a high hardness of evasion, but this issue has not been deeply investigated yet in the literature. We address it under the viewpoint of the choice of the architecture of a multiple classifier system. We propose a measure of the hardness of evasion of a classifier architecture, and give an analytical evaluation and comparison of an individual classifier and a classifier ensemble architecture. We finally report an experimental evaluation on a spam filtering task.