Machine Learning
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Algorithmic Implementation of Stochastic Discrimination
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Over-Fitting in ensembles of neural network classifiers within ECOC frameworks
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
A modular architecture for the analysis of HTTP payloads based on multiple classifiers
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
Multiple classifier systems under attack
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
SOCIAL: self-organizing classifier ensemble for adversarial learning
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
Adversarial attacks against intrusion detection systems: Taxonomy, solutions and open issues
Information Sciences: an International Journal
On the hardness of evading combinations of linear classifiers
Proceedings of the 2013 ACM workshop on Artificial intelligence and security
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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.