CAPTCHA to Cognition: Replacing Traditional CAPTCHA with Adaptive Behavioral Intelligence using Machine Learning and Deep Learning

With the rapid growth of artificial intelligence, traditional CAPTCHA systems that use text, images, or checkboxes have become more vulnerable to automated solvers and AIpowered bots. In our study, we introduce “CAPTCHA to Cognition,” a framework that adapts behavioral analysis to replace static CAPTCHAs with AI-driven cognitive verification. The system captures and analyzes how users interact, including mouse movements, keystroke patterns, scrolling behavior, and response times, to tell human users apart from bots in realtime. By using machine learning and deep learning algorithms like Random Forest, XGBoost, and Multi-Layer Perceptron (MLP), the model achieves high detection accuracy while still providing a smooth user experience. This paper discusses the design, structure, and implementation of this behavioral CAPTCHA system. It highlights its role in improving web security and making access easier through cognitive intelligence.