BeCAPTCHA-Mouse: Synthetic Mouse Trajectories and Improved Bot Detection
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We first study the suitability of behavioral biometrics to distinguish between computers and humans, commonly named as bot detection. We then present BeCAPTCHA-Mouse, a bot detector based on neuromotor modeling of mouse dynamics that enhances traditional CAPTCHA methods. Our proposed bot detector is trained using both human and bot data generated by two new methods developed for generating realistic synthetic mouse trajectories: i) a knowledge-based method based on heuristic functions, and ii) a data-driven method based on Generative Adversarial Networks (GANs) in which a Generator synthesizes human-like trajectories from a Gaussian noise input. Experiments are conducted on a new testbed also introduced here and available in GitHub: BeCAPTCHA-Mouse Benchmark; useful for research in bot detection and other mouse-based HCI applications. Our benchmark data consists of 10,000 mouse trajectories including real data from 58 users and bot data with various levels of realism. Our experiments show that BeCAPTCHA-Mouse is able to detect bot trajectories of high realism with 93 When our approach is fused with state-of-the-art mouse dynamic features, the bot detection accuracy increases relatively by more than 36 mouse-based bot detection is a fast, easy, and reliable tool to complement traditional CAPTCHA systems.
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