Optimizing Biometric Security in Industry 4.0 A Critical Analysis of Authentication Parameters and Their Impacts Using Multi-Criteria Methods - Javier Andres Esquivias Varela (Nova Scientia, 2025)
With increasing cybersecurity threats and identity fraud, secure and efficient authentication methods are essential. Traditional authentication systems, such as passwords and keycards, have vulnerabilities, making biometric authentication a promising alternative. However, biometric methods vary significantly in security, accuracy, and energy consumption, and no standardized framework exists for their evaluation in Industry 4.0 applications. This study compares seven biometric authentication systems using a multi-criteria approach based on security, accuracy, energy efficiency, and cost. TOPSIS and MORA decision-making frameworks were applied to determine the optimal biometric system. The results indicate that facial detection offers the highest accuracy (98%) and the lowest energy consumption (64% less than Retina recognition), although its performance is affected by lighting conditions. These findings provide a structured methodology for selecting biometric authentication systems in industrial settings, emphasizing security and sustainability. Future research should focus on improving facial detection reliability under varying environmental conditions and integrating AI-driven enhancements.