Evaluation of mobile device sensor data for a transparent authentication
computer science
Description
Evaluation
of mobile device sensor data for a transparent authentication
Abstract
Mobile phone has become a
method used by most people daily to store and process private and sensitive
information. To secure information stored on mobile phones, different forms of
authentication methods (knowledge-based and tokens) are currently in use. Most
of these forms of mobile phone authentications in use pose certain drawbacks that
are either easy to circumvent or cumbersome to use (Feng, 2015). As a result, new forms of mobile phone authentication
are being proposed to mitigate some of these drawbacks. Mobile phone-based
biometric is one of the new forms of authentication. Mobile phone sensors can
be harnessed to offers a wide range of solutions for authentication. This work
focused on analysing and evaluating mobile phone sensors for an explicit and
transparent user authentication process. In this project, LG mobile phones were
used to extract data from thirty (30) participants. The mobile phone sensors
that were used included gyroscope, accelerometer, linear accelerometer,
proximity sensor, gravity sensor, GPS sensor, magnetometer and the rotation
sensor. A supervised machine learning algorithm was applied after feature
extraction with Feedforward Neural network for the data classification. An EER
within the range of 31%-43% is achieved using 30 participants.
