Performance evaluation of lightweight network-based bot detection using mouse movements

Abstract

Confronted with the challenge of malicious bot abuse disrupting daily life, bot detection has become a crucial field of research. Among the various methods proposed, a behavior-driven framework analyzing mouse movements stands out for its widespread applicability, adaptability, and non-intrusive nature. Traditionally, this area of research has relied on manually extracted features from mouse dynamics and classical machine learning techniques, which often struggle to identify complex behavior patterns. To address these limitations, our research turns to Convolutional Neural Networks (CNNs), which have proven effective in image processing, to explore their application in bot detection using mouse movement data. We employ various visual representation techniques to convert mouse movements into trajectory images suitable for CNN analysis. Our empirical findings show that among all the lightweight networks tested, EfficientNet_b3 not only achieves the highest accuracy, with an impressive precision of 99.82%, but also surpasses more complex models like ResNet50 in terms of detection accuracy and modeling speed. These results not only enhance the practical application of deep learning in bot detection but also offer valuable insights into more effective strategies for advancing bot detection technologies.