Continuous User Authentication via Unlabeled Phone Movement Patterns

TL;DR

This paper introduces a continuous user authentication system leveraging unlabeled phone movement data, employing clustering and machine learning to distinguish genuine users from impostors in real-world settings.

Contribution

It presents a novel approach that uses unlabeled accelerometer data and clustering to create user profiles for continuous authentication.

Findings

Random Forest achieved the lowest EER at 5.6%.

System tested on 57 users in unconstrained environments.

Multiple classifiers evaluated for effectiveness.

Abstract

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.