SenGuard: Passive user identification on smartphones using multiple sensors

Abstract

Abstract-User identification and access control have become a high demand feature on mobile devices because those devices are wildly used by employees in corporations and government agencies for business and store increasing amount of sensitive data. This paper describes SenGuard, a user identification framework that enables continuous and implicit user identification service for smartphone. Different from traditional active user authentication and access control, SenGuard leverages availability of multiple sensors on today's smartphones and passively use sensor inputs as sources of user authentication. It extracts sensor modality dependent user identification features from captured sensor data and performs user identification at background. SenGuard invokes active user authentication when there is a mounting evidence that the phone user has changed. In addition, SenGuard uses a novel virtualization based system architecture as a safeguard to prevent subversion of the background user identification mechanism by moving it into a privileged virtual domain. An initial prototype of SenGuard was created using four sensor modalities including, voice, location, multitouch, and locomotion. Preliminary empirical studies with a set of users indicate that those four modalities are suited as data sources for implicit mobile user identification.

Adaptive Threshold Scheme for Touchscreen Gesture Continuous Authentication Using Sensor Trust

“…The scores are combined periodically taking into account their contribution to the final score. In SenGaurd [15] the authors propose using interactive and passive biometrics including touchscreen gestures, microphone, GPS, cellular tower, and motion sensors.…”

A feature based comparison of pen and swipe based signature characteristics

“…Research is currently investigating the possibility of combining pattern-PIN with other biometric modalities such as voice or face provided by other device peripherals, and keystroke dynamics on virtual screen-based keyboard (Shi, Yang, Jiang, Yang, & Xiong, 2011) Implementation of biometric systems using these devices leverage two parallel properties, both relating to the mobile nature of the systems; the portability of a device demands greater security of the device itself in terms of access to the data contained in it and the communication attributes of the system means across to the devices will open to user to potentially confidential web-based data sources.…”

Driver classification and driving style recognition using inertial sensors

“…In [11] and later in [16], they attempted to identify a person using inertial sensors. They detect and classify behavior and events and showed it's possible to separate the data into classes.…”