R. Naveiro, A. Redondo, D. Ríos Insua, F. Ruggeri
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.
Palabras clave / Keywords: adversarial machine learning, classification, Bayesian methods
Programado
Sesión 2 Premio Ramiro Melendreras
29 de mayo de 2018 17:00
Sala Cristal
