We introduce a novel biometric based on distinctive eye movement patterns. The biometric consists of 20 features that allow us to reliably distinguish users based on differences in these patterns. We leverage this distinguishing power along with the ability to gauge the users’ task familiarity, that is, level of knowledge, to address insider threats. In a controlled experiment, we test how both time and task familiarity influence eye movements and feature stability, and how different subsets of features affect the classifier performance. These feature subsets can be used to tailor the eye movement biometric to different authentication methods and threat models. Our results show that eye movement biometrics support reliable and stable continuous authentication of users. We investigate different approaches in which an attacker could attempt to use inside knowledge to mimic the legitimate user. Our results show that while this advance knowledge is measurable, it does not increase the likelihood of successful impersonation. In order to determine the time stability of our features, we repeat the experiment twice within 2 weeks. The results indicate that we can reliably authenticate users over the entire period. We show that lower sampling rates provided by low-cost hardware pose a challenge, but that reliable authentication is possible even at the rate of 50Hz commonly available with consumer-level devices. In a second set of experiments, we evaluate how our authentication system performs across a variety of real-world tasks, including reading, writing, and web browsing. We discuss the advantages and limitations of our approach in detail and give practical insights on the use of this biometric in a real-world environment.
['Simon Eberz', 'Kasper Bonne Rasmussen', 'Vincent Lenders', 'Ivan Martinovic']
Looks Like Eve: Exposing Insider Threats Using Eye Movement Biometrics