Mobile Match on Card Active Authentication Using Touchscreen Biometric

Abstract

With the wide use of personal consumer electronics devices such as smartphones, people store sensitive and confidential information more on their devices. Active authentication (AA) systems continuously authenticate users to reduce possible attacks after a successful login on the device. In this article, we propose match-on-card (MOC) approach for a secure active authentication scheme using touchscreen for smartphones to enhance the security and privacy and decrease the performance overhead on the consumer device. We train a Deep Neural Network (DNN) model, and store the model on the smart card available on the device for user authentication. To implement the user verification on smart cards, we quantize inputs to the model and the model’s parameters. A speed-up technique is added to the verification phase to improve the execution time. Evaluation results show that with a well configured DNN model, our on-card authentication reaches an Equal Error Rate (EER) of 2.6% for 15 strokes and verification time of 0.65 second for each stroke. Considering the average user’s stroke frequency of 1 stroke/s, our proposed scheme shows the potential for mobile MOC active authentication using touchscreen gestures on consumer devices.