Demo: A Multimodal Behavioral Biometric Scheme for Smartphone User Authentication (MBBS)

In this demo paper, we introduce MBBS - a tetra-model behavioral biometric authentication scheme for smartphones. MBBS leverages four modalities: the way a smartphone user (i) swipes on the touchscreen, (ii) taps any combination of ''text-independent" 8-digit numbers, (iii) writes his name on the touchscreen, and (iv) the hand's micro-movements he makes during this entry process, to authenticate users. Additionally, MBBS includes a Generative Adversarial Network (GAN) powered data augmentation architecture to enhance overall accuracy and security. To this end, we aim to demonstrate the effectiveness of MBBS firstly on ''real'' users' samples and later on the augmented samples comprising of ''real'' and ''GAN-generated'' samples, on a real Android device. MBBS is likely to enjoy maximum usability since it does not require users to remember any secret. Further, it exploits the users' familiarity with the processes and it increases the accuracy (by employing GAN in real time) without requiring a large sample size from users. Preliminary results in terms of performance, security, and usability analysis also show a positive opinion about our developed mechanism.