Assessment of Bot Detection Approaches Using Behavioral Biometrics and Mouse Dynamics

Abstract

The proliferation of automated software agents, or ”bots,” presents a significant and evolving threat to web security, data integrity, and user trust. Traditional defense mechanisms, most notably CAPTCHAS, have been systematically defeated by advancements in artificial intelligence, rendering them increasingly ineffective and detrimental to user experience. In response, the field of cybersecurity has shifted its focus towards behavioral biometrics, a paradigm that seeks to distinguish humans from bots based on their intrinsic interaction patterns. This survey provides a comprehensive review of the literature on bot detection with a specific focus on mouse dynamics—the analysis of a user’s cursor movement patterns. We trace the evolution of this field from foundational concepts and statistical feature engineering to the adoption of sophisticated deep learning models like Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs). Furthermore, we examine the critical role of public datasets in advancing research, explore the challenges posed by advanced threats such as session-replay bots and adversarial attacks, and identify key research gaps. This review synthesizes the current state-of-the-art and establishes a clear justification for the development of next-generation, robust, and frictionless bot detection systems.