Behavior Aware Adaptive Multi-Factor Authentication enhances traditional MFA by incorporating user behavioral patterns into authentication decisions. This research proposes a framework that analyzes behavioral and contextual attributes such as login patterns, device characteristics, location, and access behavior to dynamically adjust authentication requirements. The system will generate real-time risk scores and apply appropriate authentication factors accordingly. Its effectiveness will be evaluated using security, accuracy, false acceptance, false rejection, latency, and usability metrics. The research aims to develop a secure, intelligent, and user-friendly authentication mechanism for modern digital environments.
Increasing cyber threats make static MFA insufficient. Behavior aware authentication enables dynamic, risk-based security, reducing unauthorized access while maintaining usability across banking, healthcare, cloud, and digital services.
Future research can explore AI-driven continuous authentication, privacy-preserving behavioral analysis, adversarial attacks, and large-scale real-world deployment.
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