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% 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 36%, proving that mouse-based bot detection is a fast, easy, and reliable tool to complement traditional CAPTCHA systems.

本研究探讨利用行为生物统计学来区分人类和计算机（常称为机器人检测）的适用性。我们基于神经运动模型提出了BeCAPTCHA-Mouse，这是一种新颖的基于老鼠动态的机器人检测器。我们提出了两种新的老鼠轨迹综合方法。在BeCAPTCHA-Mouse基准测试中，我们的实验表明，使用仅一个老鼠轨迹，BeCAPTCHA-Mouse能够以93％的平均准确率检测出具有高逼真度的机器人轨迹，而我们的方法与最先进的老鼠动态特征融合后，相对提高了超过36％的机器人检测精度，证明了老鼠轨迹的机器人检测是一种快速，易于使用和可靠的补充传统CAPTCHA系统的工具。

BeCAPTCHA-Mouse:合成鼠标轨迹及改进的机器人检测