  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.
