BeCAPTCHA: Behavioral bot detection using touchscreen and mobile sensors benchmarked on HuMIdb

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 algo...

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Detalhes bibliográficos
Autores: Acién Ayala, Alejandro, Morales Moreno, Aythami, Fiérrez Aguilar, Julián, Vera Rodríguez, Rubén, Delgado Ben Mohatar, Óscar
Formato: artículo
Fecha de publicación:2020
País:España
Recursos:Universidad Autónoma de Madrid
Repositorio:Biblos-e Archivo. Repositorio Institucional de la UAM
Idioma:inglés
OAI Identifier:oai:repositorio.uam.es:10486/705495
Acesso em linha:http://hdl.handle.net/10486/705495
https://dx.doi.org/10.1016/j.engappai.2020.104058
Access Level:acceso abierto
Palavra-chave:Biometrics
Database
HCI
Mobile behavior
Multimodal
Smartphone
Telecomunicaciones
Descrição
Resumo: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 HuMIdb1 (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