Study on Anomaly Detection in Biometric Data: Methods, Challenges, and Applications in Information Security

Anomaly detection in biometric data is essential for ensuring the security and integrity of biometric authentication systems. This study investigates various anomaly detection methods, including statistical techniques, machine learning algorithms, and modeling-based methods, to evaluate their efficiency and limitations. Statistical methods are quick and easy to implement but may be limited in the case of complex data. Machine learning algorithms offer superior performance but require significant computational resources and high-quality data. Modeling-based methods are useful for anticipating normal values and identifying subtle deviations, although they can be complex to implement. The study highlights the importance of anomaly detection for preventing unauthorized access, protecting sensitive data, and ensuring compliance with security regulations. Recommendations for future research include developing hybrid solutions, improving biometric data quality, automating and adapting systems, conducting additional case studies, and promoting interdisciplinary collaboration. These directions can contribute to advancing biometric technologies, providing safer and more efficient solutions for anomaly detection.