A study on continuous authentication using a combination of keystroke and mouse biometrics

In this paper we focus on a context independent continuous authentication system that reacts on every separate action performed by a user. We contribute with a robust dynamic trust model algorithm that can be applied to any continuous authentication system, irrespective of the biometric modality. We also contribute a novel performance reporting technique for continuous authentication. Our proposed approach was validated with extensive experiments with a unique behavioural biometric dataset. This dataset was collected under complete uncontrolled condition from 53 users by Using our data collection software. We considered both keystroke and mouse usage behaviour patterns to prevent a situation where an attacker avoids detection by restricting to one input device because the system only checks the other input device. During our research, we developed a feature selection technique that could be applied to other pattern recognition problems. The best result obtained in this research is that 50 out of 53 genuine users are never inadvertently locked out by the system, while the remaining 3 genuine users (i.e. 5.7%) are sometimes locked out, on average after 2265 actions. Furthermore, there are only 3 out of 2756 impostors not been detected, i.e. only 0.1% of the impostors go undetected. Impostors are detected on average after 252 actions.