Abstract

In the evolving landscape of decentralized networks, traditional perimeter-based security and static credentialing have proven insufficient against sophisticated identity-based attacks. This paper proposes a Blockchain-Enabled Zero-Trust (BEZT) cybersecurity framework designed to provide continuous, context-aware security. The BEZT architecture uses Multi-Factor Adaptive Authentication (MFAA) by synthesizing three critical security layers: AI-driven Behavioral Biometrics, Geo-fencing, and Intelligent Access Control driven by Smart Contracts. The AI-driven Behavioral Biometrics component utilizes machine learning to analyze unique user behavioral patterns, such as typing rhythms and device interactions, enabling continuous user authentication. The Geo-fencing layer utilizes geolocation data to define secure access zones and dynamically assess risk based on user location, enabling adaptive access control. The Intelligent Access Control mechanism seamlessly combines authentication factors and adjusts the required strength based on real-time risk assessment. Unlike conventional systems, the proposed BEZT framework utilizes an AI engine to generate a real-time Dynamic Trust Score. This score is orchestrated via a private blockchain, ensuring tamper-proof access permissions that are automatically revoked if the user’s behavioral or spatial context deviates from baselines. The study addresses the following research questions: (1) how Blockchain-based Smart Contracts can be used to store and execute MFAA logic without compromising the privacy of a user’s biometric and location data; (2) the extent to which the inclusion of Geo-fencing reduces the success rate of remote credential-stuffing attacks in decentralized environments; and (3) whether User Behavioral Biometrics can provide a high enough “Confidence Interval” to allow for “passive authentication,” reducing digital fatigue while maintaining high security.