BeCAPTCHA: Behavioral Bot Detection using Touchscreen and Mobile Sensors benchmarked on HuMIdb

TL;DR

This paper introduces BeCAPTCHA, a new CAPTCHA approach leveraging touchscreen and accelerometer data to distinguish humans from bots during a drag-and-drop task, evaluated on the HuMIdb dataset.

Contribution

Proposes BeCAPTCHA, a novel mobile sensor-based CAPTCHA method combining touchscreen and accelerometer data for improved bot detection.

Findings

Mobile sensors effectively characterize human behavior.

BeCAPTCHA outperforms traditional CAPTCHA methods.

HuMIdb provides a valuable resource for mobile interaction research.

Abstract

In this paper we study the suitability of a new generation of CAPTCHA methods based on smartphone interactions. The heterogeneous flow of data generated during the interaction with the smartphones can be used to model human behavior when interacting with the technology and improve bot detection algorithms. For this, we propose BeCAPTCHA, a CAPTCHA method based on the analysis of the touchscreen information obtained during a single drag and drop task in combination with the accelerometer data. The goal of BeCAPTCHA is to determine whether the drag and drop task was realized by a human or a bot. We evaluate the method by generating fake samples synthesized with Generative Adversarial Neural Networks and handcrafted methods. Our results suggest the potential of mobile sensors to characterize the human behavior and develop a new generation of CAPTCHAs. The experiments are evaluated with HuMIdb (Human Mobile Interaction database), a novel multimodal mobile database that comprises 14 mobile sensors acquired from 600 users. HuMIdb is freely available to the research community.