Dynamic biometric MFA proposed for zero-trust smart organizations
Original title: Developing a Dynamic Multi-factor Authentication System for Securing Smart Organizations
A Markov chain picks fingerprint, iris, or face factors, with AES-128 used to protect stored data.
The paper proposes a dynamic multi-factor authentication design inside a zero-trust model that continuously verifies users. A Markov chain randomly selects among fingerprint, iris, and face recognition to reduce predictability. The authors evaluate the design with CASIA biometric datasets and AES-128 encryption and say the selection process balances security and efficiency for smart organizations, especially e-government. No deployment, incident, or independent evaluation is described.
Unconfirmed: Security and efficiency claims come only from the authors' analysis; no independent results are given.
- Factors named are fingerprint, iris, and face recognition.
- User data and authentication are managed with AES-128 encryption.
- Evaluation uses CASIA biometric datasets.
- The intended setting is smart organizations and e-government.
From the original article
1 min read at arxiv.orgAs smart organizations increasingly rely on technologies like AI, big data, and IoT, securing these systems is crucial. This research introduces a dynamic multi-factor authentication (MFA) system within the Zero Trust Architecture (ZTA) framework, which enforces continuous verification.
By Mohsen Saffar · arxiv.org
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