Ranking most informative apps for effective identification of legitimate smartphone owners

Abstract

Improving the accuracy, reducing the time to authenticate users and preserving privacy are some of the pivotal issues in smartphone security. A majority of published owner identification methods have concentrated on improving accuracy, emphasizing less on response time. Usage pattern of smartphone apps by the owner may be used as an important signature to differentiate between the legitimate user and others. In this work, we rank the most informative apps specific to an owner to identify the owner using an information theoretic approach. Interestingly, the reduced set of data based on highly ranked apps gives a higher detection accuracy with reduced learning time consumed by the classifiers.