Keystroke based authentication methods: A comprehensive review of evolution, challenges, and future directions

Keystroke authentication is a sophisticated behavior biometric method that verifies users in accordance with individuals 'typing timing and rhythm patterns while interactingwiththe keyboard. The majör advantages of this technology include that there are no additional user actions needed, no specialized hardware demands,and it is inexpensive. With increasing demand for second-factor authentication and ongoing user monitoring in sophisticated computing systems and high-security solutions,keystroke based systems are introduced as a natural and inexpensive option to fulfill these requirements.This study performs a systematic survey of the literature on keystroke dynamic authentication methods. First, different sets and their features are reviewed, then machine learning (ML), deep learning (DL), and hybrid models are compared relative to their performance, security, and usability. Further, the present methodologies are placed in the context of the guidance offered through the OWASP Authentication Cheat Sheet, presenting an analysis of vulnerabilities and possible attacks. It turns out that the hybrid models outperform autonomous ML or DL methods in returning improved accuracy and superior resilience. As far as future directions are concerned, federated learning (FL), explainable artificial intelligence (XAI), and multimodal biometrics fusion are promising to produce more robust solutions relative to privacy, explainability, and generalizability across platforms. To this end, we propose a hybrid authentication framework that fuses the classification capabilities of deep neural networks with anomaly detection methods, through context-aware feature extraction and adaptive thresholding mechanisms. Our system can learn and adjust to changing user habits and react with high sensitivity to spoofing behavior. In addition, when evaluated over a range of application scenarios—from continuous session verification to second-factor authentication this framework demonstrates high scalability and feasibility in practice. As a conclusion, the results show that keystroke authentication systems, and particularly when combined with other biometric techniques or augmented with contextual information, provide a secure, versatile, and user-acceptable solution for security-sensitive applications. These study not only attempts a comprehensive comparison of methods available in literature but also draws a guideline for methodological selections in future studies. Keywords: Keystroke Authentication, Behavioral Biometrics, Machine Learning, Deep Learning