Keystroke Dynamics Advances for Mobile Devices Using Deep Neural Network

Recent popularity in mobile devices has raised concerns on mobile technology security, as not only sensitive and private data are being stored on mobile devices, but also allowing remote access to other high value assets. This drives research efforts to new mobile technology security methods. Fortunately, new mobile devices are equipped with advanced sensor suite, enabling a multi-modal biometrics authentication solution, to include voice, face, gait, signature, and keystroke authentication, among others. Compared with other modalities, keystroke authentication offer some very attractive features: 1) non-intrusive, either password or free-text typing keystroke authentication can be applied without affecting users’ daily user of the device; 2) it can work on continuous authentication mode for free typing; 3) it can leverage a unique set of advanced build in sensors, including accelerometer and gyroscope to capture rich typing information than raw timing pattern. We present a deep learning approach

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