Continuous User Authentication Using Machine Learning and Multi-Finger Mobile Touch Dynamics with a Novel Dataset
As technology grows and evolves rapidly, it is increasingly clear that mobile
devices are more commonly used for sensitive matters than ever before. A need
to authenticate users continuously is sought after as a single-factor or multi
factor authentication may only initially validate a user, which does not help
if an impostor can bypass this initial validation. The field of touch dynamics
emerges as a clear way to non intrusively collect data about a user and their
behaviors in order to develop and make imperative security related decisions in
real time. In this paper we present a novel dataset consisting of tracking 25
users playing two mobile games Snake.io and Minecraft each for 10 minutes,
along with their relevant gesture data. From this data, we ran machine learning
binary classifiers namely Random Forest and K Nearest Neighbor to attempt to
authenticate whether a sample of a particular users actions were genuine. Our
strongest model returned an average accuracy of roughly 93% for both games,
showing touch dynamics can differentiate users effectively and is a feasible
consideration for authentication schemes. Our dataset can be observed at
https://github.com/zderidder/MC-Snake-Results