Enhancing Ad Hoc Network Security using Palm Vein Biometric Features
Résumé
This study proposes an innovative approach to securing ad hoc networks through palm vein biometric authentication, addressing critical security vulnerabilities in decentralized wireless communications. The research introduces an Adaptive Fusion Biometric Key Generation (AFBKG) framework that seamlessly integrates palm vein biometric features with state-of-the-art cryptographic protocols. The methodology implements a comprehensive six-stage process, incorporating Near-Infrared (NIR) imaging at 850 nm wavelength, advanced image preprocessing techniques, and deep learning-based feature extraction using a fine-tuned Convolutional Neural Network (CNN), culminating in a robust 512-dimensional feature vector. A rigorous performance evaluation was conducted, which demonstrated exceptional results, achieving 98% authentication accuracy with a 0.1% False Acceptance Rate (FAR) and 95% spoofing resistance. The AFBKG algorithm significantly outperforms traditional security methods, demonstrating 95% authentication strength and 92% resistance to Man-in-the-Middle (MITM) attacks while maintaining minimal key management complexity (15%). The system's superior scalability (90%) and computational efficiency (10% overhead) compared to conventional biometric approaches are noteworthy. These findings establish palm vein biometric authentication as a cutting-edge solution for enhancing ad hoc network security, offering substantial improvements over traditional password-based systems and alternative biometric methods.
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