NDLI: Multiple classifier systems for robust classifier design in adversarial environments

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Multiple classifier systems for robust classifier design in adversarial environments

Content Provider	Springer Nature Link
Author	Biggio, Battista Fumera, Giorgio Roli, Fabio
Copyright Year	2010
Abstract	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.
Starting Page	27
Ending Page	41
Page Count	15
File Format	PDF
ISSN	18688071
Journal	International Journal of Machine Learning and Cybernetics
Volume Number	1
Issue Number	1-4
e-ISSN	1868808X
Language	English
Publisher	Springer-Verlag
Publisher Date	2010-10-12
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Adversarial classification Multiple classifier systems Robust classifiers Linear classifiers Pattern Recognition Systems Biology Statistical Physics, Dynamical Systems and Complexity Control Robotics Mechatronics Artificial Intelligence (incl. Robotics) Computational Intelligence
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Vision and Pattern Recognition Software

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