Evaluation of mobile device sensor data for a transparent authentication

computer science


Evaluation of mobile device sensor data for a transparent authentication






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.

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