BeCAPTCHA-Mouse: Synthetic Mouse Trajectories and Improved Bot Detection

TL;DR

This paper introduces BeCAPTCHA-Mouse, a novel bot detection method using a neuromotor model and synthetic mouse trajectories, achieving high accuracy and demonstrating the effectiveness of mouse dynamics in distinguishing humans from bots.

Contribution

The paper presents new synthetic mouse trajectory generation methods and a comprehensive benchmark dataset, enhancing bot detection accuracy with a neuromotor feature set.

Findings

Achieves 93% accuracy in bot detection using a single mouse trajectory.

Synthetic data improves detection when fused with real mouse dynamic features.

Provides a new benchmark dataset with 15,000 trajectories for research.

Abstract

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.