Enhancing Network Access Control using Multi-Modal Biometric Authentication Framework
Received: 9 November 2024 | Revised: 10 December 2024 | Accepted: 29 December 2024 | Online: 5 January 2025
Corresponding author: Abdelnasser Mohammed
Abstract
This study presents an innovative multi-modal biometric authentication framework that integrates Deep Learning (DL) techniques with zero-trust architecture principles for enhanced network access control. The framework employs a three-tier fusion strategy (feature-level, score-level, and decision-level) incorporating facial, fingerprint, and iris recognition modalities. The system architecture implements a sophisticated multi-layered approach utilizing the ResNet-50 based Convolutional Neural Network (CNN) architecture for facial recognition, CNN-based minutiae extraction for fingerprint processing, and 2D Gabor wavelets with DL-based feature extraction for iris analysis. The experimental validation using established datasets, namely Labeled Faces in the Wild (LFW), CelebA, FVC2004, NIST SD14, CASIA Iris V4, and UBIRIS v2, demonstrates exceptional performance with 99.47% authentication accuracy, 0.02% False Acceptance Rate (FAR), and 0.15% False Rejection Rate (FRR). The framework resulted in a 68% reduction in fraudulent access attempts. It achieved a mean authentication time of 235 ms (SD=28 ms), representing a 45% improvement over traditional systems. The resource efficiency analysis showed significant reductions in system overhead: 32% in CPU utilization, 28% in memory consumption, and 45% in network bandwidth requirements. The scalability testing confirmed a linear performance scaling up to 100,000 concurrent authentication requests. The statistical test of significance through t-test confirmed the framework's significant improvements over existing solutions (p-value<0.001). This study establishes an effective framework to address network access control challenges across various sectors, particularly in high-security environments requiring robust authentication mechanisms.
Keywords:
biometric authentication, deep learning, zero-trust architecture, multi-modal fusion, network securityDownloads
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Copyright (c) 2025 Abdelnasser Mohamed, Ahmed Salama, Nasser Shebka, Amr Hassan
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