PACIS 2020 Proceedings


Mobile user authentication (MUA) has become a gatekeeper for securing a wealth of personal and sensitive information residing on mobile devices. Keystrokes and touch gestures are two types of touch behaviors. It is not uncommon for a mobile user to make multiple MUA attempts. Nevertheless, there is a lack of an empirical comparison of different types of touch dynamics based MUA methods across different attempts. In view of the richness of touch dynamics, a large number of features have been extracted from it to build MUA models. However, there is little understanding of what features are important for the performance of such MUA models. Further, the training sample size of template generation is critical for real-world application of MUA models, but there is a lack of such information about touch gesture based methods. This study is aimed to address the above research limitations by conducting experiments using two MUA prototypes. Their empirical results can not only serve as a guide for the design of touch dynamics based MUA methods but also offer suggestions for improving the performance of MUA models.



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