Adversarial Machine Learning: Perspectives from Adversarial Risk Analysis

Adversarial Machine Learning (AML) is emerging as a major eld aimed at

the protection of automated ML systems against security threats. The majority of work in

this area has built upon a game-theoretic framework by modelling a conict between an

attacker and a defender. After reviewing game-theoretic approaches to AML, we discuss

the benets that a Bayesian Adversarial Risk Analysis perspective brings when defending

ML based systems. A research agenda is included.