Multiple classifier systems for robust classifier design in adversarial environments

Pattern recognition systems are increasingly being used in adversarial environments like network intrusion detection, spam filtering and biometric authentication and verification systems, in which an adversary may adaptively manipulate data to make a classifier ineffective. Current theory and design methods of pattern recognition systems do not take into account the adversarial nature of such kind of applications. Their extension to adversarial settings is thus mandatory, to safeguard the security and reliability of pattern recognition systems in adversarial environments. In this paper we focus on a strategy recently proposed in the literature to improve the robustness of linear classifiers to adversarial data manipulation, and experimentally investigate whether it can be implemented using two well known techniques for the construction of multiple classifier systems, namely, bagging and the random subspace method. Our results provide some hints on the potential usefulness of classifier ensembles in adversarial classification tasks, which is different from the motivations suggested so far in the literature.