BehFayda: A Comprehensive Review and Framework Proposal for Adaptive Authentication in National Identity Systems Using Multi-Modal Biometric Fusion

Abstract

The proliferation of digital services necessitates robust identity verification mechanisms. The Ethiopian digital national ID, Fayda, built on the Modular Open-Source Identity Platform (MOSIP), aims to offer a secure and scalable solution for national identity management. However, MOSIP lacks explicit support for adaptive continuous authentication—a crucial aspect of ensuring security and usability. This paper introduces BehFayda, a comprehensive architecture for a privacy-enhanced multi-modal biometric fusion system for adaptive continuous authentication tailored to digital identity systems. The framework integrates behavioral biometrics, such as keystroke dynamics in two languages, swipe gestures, motion data, and contextual data as a candidate for the proposed fusion strategy. We propose the Multi-Modal Deep Residual Fusion (MM-DRF) algorithm, which incorporates feature-level fusion with adaptive attention mechanisms to dynamically adjust the contribution of different biometric modalities based on their relevance. Our approach provides a new insight to enhance authentication accuracy which mainly aims to guide future research in advancing adaptive authentication in national digital identity systems, with a focus on privacy-preserving techniques and real-time behavioral analysis.