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Summary Different authentication techniques that we use today, are prone to shoulder surfing attacks and mimicry attacks. Thus, keystroke dynamics combined with time and motion‐based typing patterns have been studied for years. In this paper, we introduce and evaluate a touch gesture‐based application to authenticate a user based on their typing behavior in distinct contexts such as lying, sitting, standing, walking, stationary, climbing up and down the stairs, by leveraging different features extracted from multiple built‐in smartphone sensors. We use various attributes including time‐based features such as dwell time and flight time and motion‐based features such as accelerometer, gyroscope, and magnetometer readings. The proposed authentication model distinguishes the legitimate smartphone owner from impostors using hand gestures, touch and keystroke dynamics. We experimented with different design alternatives such as a combination of motion sensor features, time‐based features extracted from multiple devices. In addition, we evaluated the performance using various supervised machine learning algorithms to show how to achieve high authentication accuracy and least equal error rate. A thorough evaluation shows that the system achieves authentication with 99.8% accuracy with a high AUC of 0.99 and EER of 0.11%.

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