SCRAP

Adversarial attacks have gained popularity recently due to their simplicity and impact. Their applicability to diverse security scenarios is however less understood. In particular, in some scenarios, attackers may come up naturally with ad-hoc black-box attack techniques inspired directly on characteristics of the problem space rather than using generic adversarial techniques. In this paper we explore an intuitive attack technique for Mouse-based Behavioral Biometrics and compare its effectiveness against adversarial machine learning attacks. We show that attacks leveraging on domain knowledge have higher transferability when applied to various machine-learning techniques and are also more difficult to defend against. We also propose countermeasures against such attacks and discuss their effectiveness.