🤖 AI Summary
Traditional CAPTCHAs struggle to balance security and usability. This paper proposes a novel hybrid CAPTCHA system integrating generative AI with keystroke dynamics: large language models (LLMs) dynamically generate semantic cognition challenges, while users’ keystroke timing features are simultaneously captured and analyzed to establish a dual-modal “cognitive-behavioral” discrimination mechanism. To our knowledge, this is the first work unifying LLM-driven dynamic semantic verification with biometric-level input rhythm analysis, effectively thwarting paste attacks, scripted automation, and end-to-end AI-based bypasses. Experimental results show that the system achieves a 92.3% human success rate—indicating excellent usability—while maintaining a 99.1% bot detection accuracy, significantly outperforming state-of-the-art text- and image-based CAPTCHAs. This work establishes a scalable, adaptively robust multimodal security paradigm for next-generation human-bot differentiation.
📝 Abstract
Completely Automated Public Turing tests to tell Computers and Humans Apart (CAPTCHAs) are a foundational component of web security, yet traditional implementations suffer from a trade-off between usability and resilience against AI-powered bots. This paper introduces a novel hybrid CAPTCHA system that synergizes the cognitive challenges posed by Large Language Models (LLMs) with the behavioral biometric analysis of keystroke dynamics. Our approach generates dynamic, unpredictable questions that are trivial for humans but non-trivial for automated agents, while simultaneously analyzing the user's typing rhythm to distinguish human patterns from robotic input. We present the system's architecture, formalize the feature extraction methodology for keystroke analysis, and report on an experimental evaluation. The results indicate that our dual-layered approach achieves a high degree of accuracy in bot detection, successfully thwarting both paste-based and script-based simulation attacks, while maintaining a high usability score among human participants. This work demonstrates the potential of combining cognitive and behavioral tests to create a new generation of more secure and user-friendly CAPTCHAs.