Continuous User Authentication via Unlabeled Phone Movement Patterns
This addresses security for smartphone users by enabling passive authentication, but it is incremental as it applies existing machine learning methods to a new application with unconstrained data.
The paper tackles continuous smartphone user authentication by using unlabeled accelerometer data to create context-specific profiles, achieving a mean equal error rate as low as 5.6% with Random Forest on a dataset of 57 users.
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.