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Creators/Authors contains: "Guirguis, M."

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  1. ; (, 2021 Association of Computer Science Departments at Minority Institutions (ADMI) Symposium)
    null (Ed.)
    With the growing popularity of smartphones, continuous and implicit authentication of such devices via behavioral biometrics such as touch dynamics becomes an attractive option. Specially, when the physical biometrics are challenging to utilize, and their frequent and continuous usage annoys the user. This paper presents a touchstroke authentication model based on several classification algorithms and compare their performances in authenticating legitimate smartphone users. The evaluation results suggest that it is possible to achieve comparable authentication accuracies with an average accuracy of 91% considering the best performing model. This research is supervised by Dr. Debzani Deb (debd@wssu.edu), Department of Computer Science at Winston-Salem State University, NC. 
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    Accepted Manuscript Full Text Available