Classification problems in security settings are usually contemplated as confrontations in which one or more adversaries try to fool a classifier to obtain a benefit. Most approaches to such adversarial classification problems have focused on game theoretical ideas with strong underlying common knowledge assumptions, which are actually not realistic in security domains. We provide an alternative framework to such problem based on adversarial risk analysis, which we illustrate with several examples. Computational and implementation issues are discussed.
@article{arxiv.1802.07513,
title = {Adversarial classification: An adversarial risk analysis approach},
author = {Roi Naveiro and Alberto Redondo and David Ríos Insua and Fabrizio Ruggeri},
journal= {arXiv preprint arXiv:1802.07513},
year = {2019}
}
Comments
Published in the International Journal for Approximate Reasoning