Three-factor authentication (3FA) provides stronger identity protection by combining knowledge, possession, and biometric factors; however, conventional 3FA systems often apply the same authentication requirements regardless of contextual risk. This research proposes a Risk-Adaptive Three-Factor Authentication framework that dynamically adjusts authentication requirements based on user behavior, device trust, network conditions, location, and historical authentication patterns. A risk-scoring model will determine appropriate authentication levels while maintaining security and usability. The proposed framework will be experimentally evaluated using metrics including authentication accuracy, false acceptance rate, false rejection rate, latency, usability, and privacy. The study aims to develop a secure, adaptive, and practical authentication framework for modern digital environments.
With increasing cyber threats, identity theft, and unauthorized access, traditional authentication methods are becoming insufficient. Risk-Adaptive Three-Factor Authentication can provide stronger security while improving usability by dynamically adjusting authentication requirements according to contextual risk. This research is highly relevant for banking, healthcare, e-commerce, cloud services, and other security-critical digital platforms.
Future research can integrate AI-based behavioral analysis to improve real-time risk assessment and adaptive authentication. The framework can be extended to IoT, mobile, banking, healthcare, and cloud environments. Further studies may explore continuous authentication, privacy-preserving biometrics, adversarial attacks, usability, and large-scale real-world deployment.
Cybersecurity, Multi-Factor Authentication, Risk Assessment, AI-Based Behavioral Analysis, Biometric Security, Python, Data Analysis, and Security Research.
Link 1: https://ieeexplore.ieee.org/abstract/document/9850264
Link 2: https://link.springer.com/chapter/10.1007/978-3-032-23335-6_1
Link 3: https://ieeexplore.ieee.org/abstract/document/10060915
