BeCAPTCHA-Mouse: Synthetic mouse trajectories and improved bot detection
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: i) a neuromotor model of mouse dynamics to obtain a novel feature set for the classification of human and bo...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | Universidad Autónoma de Madrid |
| Repositorio: | Biblos-e Archivo. Repositorio Institucional de la UAM |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.uam.es:10486/711222 |
| Acceso en línea: | http://hdl.handle.net/10486/711222 https://dx.doi.org/10.1016/j.patcog.2022.108643 |
| Access Level: | acceso abierto |
| Palabra clave: | CAPTCHA Bot detection Behavior Biometrics Mouse Neuromotor Telecomunicaciones |
| Sumario: | 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: i) a neuromotor model of mouse dynamics to obtain a novel feature set for the classification of human and bot samples; and ii) a learning framework involving real and synthetically generated mouse trajectories. We propose two new mouse trajectory synthesis methods for generating realistic data: a) a function-based method based on heuristic functions, and b) 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 15,000 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 of accuracy in average using only one mouse trajectory. When our approach is fused with state-of-the-art mouse dynamic features, the bot detection accuracy increases relatively by more than , proving that mouse-based bot detection is a fast, easy, and reliable tool to complement traditional CAPTCHA systems. |
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