Visible to the public Active Authentication on Mobile Devices via Stylometry, Application Usage, Web Browsing, and GPS Location

TitleActive Authentication on Mobile Devices via Stylometry, Application Usage, Web Browsing, and GPS Location
Publication TypeJournal Article
Year of Publication2017
AuthorsFridman, L., Weber, S., Greenstadt, R., Kam, M.
JournalIEEE Systems Journal
KeywordsActive Authentication, Android (operating system), Android mobile device, Androids, application usage patterns, authentication, Behavioral biometrics, biometrics (access control), decision fusion, geographical colocation, Global Positioning System, GPS location, Human Behavior, Humanoid robots, insider threat, Intrusion detection, Keyboards, message authentication, Metrics, Mobile handsets, multimodal biometric systems, online front-ends, parallel binary decision fusion architecture, pubcrawl, software architecture, stylometry, Web browsing, web browsing behavior, Web sites, Wi-Fi, wireless LAN

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 paper, we collect and analyze behavioral biometrics data from 200 subjects, each using their personal Android mobile device for a period of at least 30 days. This data set 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.

Citation Keyfridman_active_2017