Targeted Mimicry Attacks on Touch Input Based Implicit Authentication Schemes

Touch input implicit authentication (``touch IA'') employs behavioural biometrics like touch location and pressure to continuously and transparently authenticate smartphone users. We provide the first ever evaluation of targeted mimicry attacks on touch IA and show that it fails against shoulder surfing and offline training attacks. Based on experiments with three diverse touch IA schemes and 256 unique attacker-victim pairs, we show that shoulder surfing attacks have a bypass success rate of 84% with the majority of successful attackers observing the victim's behaviour for less than two minutes. Therefore, the accepted assumption that shoulder surfing attacks on touch IA are infeasible due to the hidden nature of some features is incorrect. For offline training attacks, we created an open-source training app for attackers to train on their victims' touch data. With this training, attackers achieved bypass success rates of 86%, even with only partial knowledge of the underlying features used by the IA scheme. Previous work failed to find these severe vulnerabilities due to its focus on random, non-targeted attacks. Our work demonstrates the importance of considering targeted mimicry attacks to evaluate the security of an implicit authentication scheme. Based on our results, we conclude that touch IA is unsuitable from a security standpoint.