  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 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.
