Year: 2025
Type: article
Abstract: Automatic bots increasingly compromise the security and reliability of online platforms, requiring effective detection mechanisms. This paper proposes a behavioral-based framework to distinguish human users from the bots through the analysis of keystroke dynamics and mouse movement patterns. A dedicated web-based interface was developed to capture real-time interaction data, including writing speeds and cursor trajectories. These features were collected in a structured dataset and used to train machine learning for accurate prediction. Experimental results indicate a high classification accuracy, which shows the framework's capability to identify non-human interactions. The integration of behavioral metrics with machine learning contributes to robust, adaptive bot detection, and enhancing the integrity and security of the digital systems.
Authors: M Amshavalli, S Chandiran, R Dharshini, A M Edwin Arul Solomon
Institution: Sengunthar Engineering College (Autonomous)
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4410729436