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- W4309773753 abstract "Several authentication techniques are required to preserve smartphone users' privacy. Part of these authentication mechanisms are based on Keystroke Dynamics (KD) or Swipe Dynamics (SD); however, these mechanisms have performance challenges due to the following; limited capability in handling user behavioral variations, inefficient feature extraction, and alternate usages that are not restricted to a specific method typing or swiping. This work presents an improved smartphone continuous authentication model by integrating free text-based KD and SD. The proposed model adopts feature-level fusion by concatenating free-text KD and SD features. This Feature level fusion was evaluated based on a comprehensive and benchmark dataset and Random Forest (RF) classifier. Results have confirmed the proposed model's performance, in which accuracy was 99.98%, with the lowest Equal Error Rate (EER) rate of 0.02% based on multi-class classification." @default.
- W4309773753 created "2022-11-29" @default.
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- W4309773753 date "2023-02-01" @default.
- W4309773753 modified "2023-09-25" @default.
- W4309773753 title "Keystroke and swipe biometrics fusion to enhance smartphones authentication" @default.
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- W4309773753 doi "https://doi.org/10.1016/j.cose.2022.103022" @default.
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