  In this paper, we propose a novel continuous authentication system for
smartphone users. The proposed system entirely relies on unlabeled phone
movement patterns collected through smartphone accelerometer. The data was
collected in a completely unconstrained environment over five to twelve days.
The contexts of phone usage were identified using k-means clustering. Multiple
profiles, one for each context, were created for every user. Five machine
learning algorithms were employed for classification of genuine and impostors.
The performance of the system was evaluated over a diverse population of 57
users. The mean equal error rates achieved by Logistic Regression, Neural
Network, kNN, SVM, and Random Forest were 13.7%, 13.5%, 12.1%, 10.7%, and 5.6%
respectively. A series of statistical tests were conducted to compare the
performance of the classifiers. The suitability of the proposed system for
different types of users was also investigated using the failure to enroll
policy.
