Mobile Active Authentication via Linguistic Modalities
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 200 subjects, 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. We further characterize the contribution of two additional modalities developed in addition to the main four modalities: eye tracking and an alternate stylometry method.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jul 01, 2015
- Accession Number
- ADA621879
Entities
People
- Lex Fridman
- Moshe Kam
- Patrick Brenna
- Rachel Greenstadt
Organizations
- Drexel University