Active Authentication on Mobile Devices via Stylometry, Application Usage, Web Browsing, and GPS Location

TL;DR

This paper presents a comprehensive study on active authentication for mobile devices using behavioral biometrics across four modalities, demonstrating effective continuous user verification in organizational environments.

Contribution

It introduces a large, multi-modal dataset collected over 30 days from 200 users and evaluates classifiers for each modality within a fusion architecture for active authentication.

Findings

High accuracy in intruder detection within short timeframes

GPS and WiFi location modalities significantly improve detection performance

Combining modalities enhances overall authentication reliability

Abstract

Active authentication is the problem of continuously verifying the identity of a person based on behavioral aspects of their interaction with a computing device. In this study, we collect and analyze behavioral biometrics data from 200subjects, each using their personal Android mobile device for a period of at least 30 days. This dataset is novel in the context of active authentication due to its size, duration, number of modalities, and absence of restrictions on tracked activity. The geographical colocation of the subjects in the study is representative of a large closed-world environment such as an organization where the unauthorized user of a device is likely to be an insider threat: coming from within the organization. We consider four biometric modalities: (1) text entered via soft keyboard, (2) applications used, (3) websites visited, and (4) physical location of the device as determined from GPS (when outdoors) or WiFi (when indoors). We implement and test a classifier for each modality and organize the classifiers as a parallel binary decision fusion architecture. We are able to characterize the performance of the system with respect to intruder detection time and to quantify the contribution of each modality to the overall performance.