Тип публикации: статья из журнала
Год издания: 2024
Идентификатор DOI: 10.1109/access.2024.3391377
Аннотация: This paper introduces a novel approach for the detection of automated entities in online environments through the analysis of mouse dynamics. Leveraging fractional derivatives and vector cross products, our methodology scrutinizes the intricate patterns embedded in mouse movements. Fractional derivatives capture the non-integer ordПоказать полностьюer dynamics, while vector cross products reveal deviations from expected trajectories. The combination of these advanced mathematical tools offers a unique perspective on distinguishing between human and bot behaviors. We present experimental results showcasing the efficacy of our approach in various scenarios, demonstrating its potential in the realm of cybersecurity and online integrity. Our findings contribute to the evolving landscape of bot detection methodologies, emphasizing the importance of incorporating mathematical rigor in the analysis of digital behavior.
Журнал: IEEE Access
Выпуск журнала: Т. 12
Номера страниц: 56707-56718
ISSN журнала: 21693536
Издатель: Institute of Electrical and Electronics Engineers Inc.