Biometric Authentication for Digital Twin Identity Security

Authors

  • Maria Fernandez, Assistant Professor of Machine Learning, Universidad Politécnica de Madrid, Madrid, Spain Author

Keywords:

AI-powered biometric authentication, digital twin, identity security, machine learning

Abstract

Virtual replicas of real items can be created by the help of digital twin technology, this heightened the need of secure digital identity management. This technology needs user and entity identity security in healthcare, manufacturing, and urban planning. Sometimes traditional authentication techniques are complicated and insecure. Face recognition, fingerprints, and voice patterns looks promising for AI powered biometric authentication. This paper discusses the use and advantages of AI powered biometric authentication for identity security in digital twin application and also discusses merits, demerits and the future application of this technology.

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References

Jain, A. K., & Ross, A. (2017). Biometric systems and performance evaluation. Proceedings of the IEEE, 105(5), 1064–1077.

European Union. (2018). General Data Protection Regulation (GDPR). Official Journal of the European Union, L119, 1–88.

Miller, M. (2020). Enhancing security with AI-powered biometrics. Journal of Cybersecurity, 8(2), 234–248.

Singh, R., & Kumar, V. (2021). Challenges in biometric authentication for digital identity management. International Journal of Information Security, 9(4), 450–467.

Chen, H., & Zhang, Y. (2019). Machine learning for biometric authentication: Challenges and prospects. Biometric Technology Today, 15(3), 58–62.

Patel, D., & Gupta, A. (2022). Quantum computing and biometric security: A new frontier. Journal of Quantum Information Science, 5(1), 28–40.

Zhao, Y., & Liu, W. (2018). Deep learning in facial recognition systems. Journal of Machine Learning Research, 19(1), 2031–2043.

S. Kumari, “AI-Driven Cloud Transformation for Product Management: Optimizing Resource Allocation, Cost Management, and Market Adaptation in Digital Products ”, IoT and Edge Comp. J, vol. 2, no. 1, pp. 29–54, Jun. 2022

Pillai, Vinayak. “Implementing Efficient Data Operations: An Innovative Approach”. Asian Journal of Multidisciplinary Research & Review, vol. 3, no. 6, Dec. 2022, pp. 231-67, https://ajmrr.org/journal/article/view/241.

S. Kumari, “Agile Cloud Transformation in Enterprise Systems: Integrating AI for Continuous Improvement, Risk Management, and Scalability”, Australian Journal of Machine Learning Research & Applications, vol. 2, no. 1, pp. 416–440, Mar. 2022

Zhu, Yue, and Johnathan Crowell. "Systematic Review of Advancing Machine Learning Through Cross-Domain Analysis of Unlabeled Data." Journal of Science & Technology 4.1 (2023): 136-155.

Sangaraju, Varun Varma, and Senthilkumar Rajagopal. "Applications of Computational Models in OCD." In Nutrition and Obsessive-Compulsive Disorder, pp. 26-35. CRC Press.

Sivaraman, Hariprasad. "Self-Healing Test Automation Frameworks Using Reinforcement Learning for Full-Stack Test Automation." Journal of Artificial Intelligence & Cloud Computing. SRC/JAICC-E210. DOI: doi. org/10.47363/JAICC/2022 (1) E210 J Arti Inte & Cloud Comp 1.4 (2022): 2-4.

Singu, Santosh Kumar. "Impact of Data Warehousing on Business Intelligence and Analytics." ESP Journal of Engineering & Technology Advancements 2.2 (2022): 101-113.

S. Kumari, “AI-Enhanced Agile Development for Digital Product Management: Leveraging Data-Driven Insights for Iterative Improvement and Market Adaptation”, Adv. in Deep Learning Techniques, vol. 2, no. 1, pp. 49–68, Mar. 2022

S. Kumari, “AI-Driven Cybersecurity in Agile Cloud Transformation: Leveraging Machine Learning to Automate Threat Detection, Vulnerability Management, and Incident Response”, J. of Art. Int. Research, vol. 2, no. 1, pp. 286–305, Apr. 2022

Sangaraju, Varun Varma, and Kathleen Hargiss. "Zero trust security and multifactor authentication in fog computing environment." Available at SSRN 4472055.

Sivaraman, Hariprasad. (2022). Adaptive Thresholding in ML-Driven Alerting Systems for Reducing False Positives in Production Environments. INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT. 6. 10.55041/IJSREM11938.

Pillai, V. “Data Analytics and Engineering in Automobile Data Systems”. Journal of Science & Technology, vol. 4, no. 6, Dec. 2023, pp. 140-79, https://thesciencebrigade.com/jst/article/view/520

S. Kumari, “Cybersecurity in Digital Transformation: Using AI to Automate Threat Detection and Response in Multi-Cloud Infrastructures ”, J. Computational Intel. & Robotics, vol. 2, no. 2, pp. 9–27, Aug. 2022

Sivaraman, Hariprasad. "Behavior-Based DDoS Detection for Multi-Vector Attacks in Hybrid Cloud Environments." Sivaraman H. Behavior-Based DDoS Detection for Multi-Vector Attacks in Hybrid Cloud Environments.

Arshad, F., & Ahmed, M. (2020). Integrating AI in biometric authentication for IoT systems. IEEE Internet of Things Journal, 7(4), 1185–1198.

Brown, S., & Harris, A. (2020). Biometric data protection in the age of AI. Cybersecurity Law Review, 10(2), 104–118.

Smith, J. P., & Rogers, S. (2021). Exploring AI-based authentication systems in the digital age. International Journal of AI Research, 8(1), 92–105.

Lee, J., & Kim, S. (2022). Enhancing IoT security with biometric authentication. IoT Security Journal, 6(3), 67–80.

Taylor, M., & Anderson, B. (2019). AI-powered voice biometrics: A review of emerging technologies. Journal of Voice Technology, 12(4), 150–163.

Wang, T., & Zhang, P. (2020). AI-based fingerprint recognition for mobile security. Journal of Digital Security, 11(5), 120–134.

Yang, X., & Liu, F. (2020). Advanced facial recognition techniques in AI-powered security systems. Journal of Computer Vision, 34(2), 97–108.

Kumar, P., & Sharma, A. (2021). Privacy concerns in biometric authentication. Cybersecurity Ethics, 7(1), 72–84.

Zhang, J., & Lee, C. (2020). Biometric systems for digital identity management. Journal of Digital Authentication, 10(4), 55–70.

Moore, C., & Price, A. (2021). AI in digital twin technology: Security and privacy implications. IEEE Transactions on Industrial Applications, 14(3), 445–459.

Davis, K., & Brown, F. (2019). The evolution of biometric authentication systems. Journal of Security Technologies, 12(3), 185–198.

White, M., & Clark, G. (2022). Biometric authentication in healthcare: Ensuring privacy and security. Healthcare Security Review, 6(2), 33–45.

Harris, T., & Kahn, M. (2021). Artificial intelligence and digital twin security: A practical overview. Journal of Digital Systems, 19(4), 174–189.

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Published

02-02-2023

How to Cite

[1]
Maria Fernandez, “Biometric Authentication for Digital Twin Identity Security”, American J Auton Syst Robot Eng, vol. 3, pp. 1–7, Feb. 2023, Accessed: Dec. 12, 2025. [Online]. Available: https://ajasre.org/index.php/publication/article/view/6