PERANCANGAN AUTENTIKASI MULTI FAKTOR DENGAN PENGENALAN WAJAH DAN FIDO (FAST IDENTITY ONLINE)

  • Rizky Atmawijaya Universitas Nusa Mandiri
  • Ummu Radiyah Universitas Nusa Mandiri
Keywords: FaceNet, face recognition, multi-factor authentication, privacy preserving

Abstract

Digital services based online are assets that need to be safeguarded, especially if the application still uses single-factor authentication vulnerable to cyberattacks and potential data leaks and identity theft. The proposed solution is to implement multi-factor authentication (MFA) utilizing facial recognition, particularly through FaceNet technology. Although facial recognition can provide an additional layer of security, the main challenge is to maintain user privacy even if biometric information might leak. This research aims to create a secure, reliable MFA model that protects the privacy of employees at PT Traspac Makmur Sejahtera. The proposed method involves an MFA system with four factors: knowledge factor (password), biometric factor (facial measurements), ownership factor (OTP) and location factor (optional if facial accuracy is insufficient). The implementation of this MFA model enhances security, reliability, and protects employee privacy. Considering the specific needs of the company, this research can assist the company in monitoring the locations of employees working from home (WFH).

Downloads

Download data is not yet available.

References

Ali, G. (2023). Development of a secure multi-factor authentication algorithm for mobile money applications. https://dspace.nm-aist.ac.tz/handle/20.500.12479/2210

Anthony, P., Ay, B., & Aydin, G. (2021). A review of face anti-spoofing methods for face recognition systems. 2021 International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2021 - Proceedings. https://doi.org/10.1109/INISTA52262.2021.9548404

Arman, S. M., Yang, T., Shahed, S., Mazroa, A. Al, Attiah, A., & Mohaisen, L. (2024). A Comprehensive survey for privacy-preserving biometrics: Recent approaches, challenges, and future directions. Computers, Materials and Continua, 78(2), 2087–2110. https://doi.org/10.32604/cmc.2024.047870

Baig, A. F., & Eskeland, S. (2021). Security, privacy, and usability in continuous authentication: A survey. In Sensors (Vol. 21, Issue 17). MDPI. https://doi.org/10.3390/s21175967

Cahyono, F. (2020). Pengenalan Wajah Menggunakan Model Facenet Untuk Presensi Pegawai (Doctoral dissertation, Institut Teknologi Sepuluh Nopember).

Feng, H., Li, H., Pan, X., & Zhao, Z. (2021). A Formal Analysis of the FIDO UAF Protocol. 28th Annual Network and Distributed System Security Symposium, NDSS 2021. https://doi.org/10.14722/ndss.2021.24363

Jin, R., Li, H., Pan, J., Ma, W., & Lin, J. (2021). Face Recognition Based on MTCNN and FaceNet. www.aaai.org

Kim, H., Lee, D., & Ryou, J. (2020). User Authentication Method using FIDO based Password Management for Smart Energy Environment. 2020 International Conference on Data Mining Workshops (ICDMW), 707–710. https://doi.org/10.1109/ICDMW51313.2020.00100

Klieme, E., Wilke, J., Dornick, N. van, & Meinel, C. (2020). FIDOnuous: A FIDO2/WebAuthn Extension to Support Continuous Web Authentication. 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), 1857–1867. https://doi.org/10.1109/TrustCom50675.2020.00254

Maheswari, V., Sari, C. A., Setiadi, D. R. I. M., & Rachmawanto, E. H. (2020). Face Recognition using FaceNet (Survey, Performance Test, and Comparison). Proceedings - 2020 International Seminar on Application for Technology of Information and Communication: IT Challenges for Sustainability, Scalability, and Security in the Age of Digital Disruption, ISemantic 2020, 55–60. https://doi.org/10.1109/iSemantic50169.2020.9234250

Meden, B., Rot, P., Terhorst, P., Damer, N., Kuijper, A., Scheirer, W. J., Ross, A., Peer, P., & Struc, V. (2021). Privacy-Enhancing Face Biometrics: A Comprehensive Survey. IEEE Transactions on Information Forensics and Security, 16, 4147–4183. https://doi.org/10.1109/TIFS.2021.3096024

Ming, Z., Visani, M., Luqman, M. M., & Burie, J. C. (2020). A survey on anti-spoofing methods for facial recognition with rgb cameras of generic consumer devices. In Journal of Imaging (Vol. 6, Issue 12). MDPI. https://doi.org/10.3390/jimaging6120139

Otta, S. P., Panda, S., Gupta, M., & Hota, C. (2023). A Systematic Survey of Multi-Factor Authentication for Cloud Infrastructure. Future Internet, 15(4). https://doi.org/10.3390/fi15040146

Taskiran, M., Kahraman, N., & Erdem, C. E. (2020). Face recognition: Past, present and future (a review). In Digital Signal Processing: A Review Journal (Vol. 106). Elsevier Inc. https://doi.org/10.1016/j.dsp.2020.102809

Wang, Q., & Wang, D. (2023). Understanding Failures in Security Proofs of Multi-Factor Authentication for Mobile Devices. IEEE Transactions on Information Forensics and Security, 18, 597–612. https://doi.org/10.1109/TIFS.2022.3227753

Xin, T. Y., Katuk, N., & Arif, A. S. C. M. (2021). Smart Home Multi-Factor Authentication Using Face Recognition and One-Time Password on Smartphone. International Journal of Interactive Mobile Technologies, 15(24), 32–48. https://doi.org/10.3991/IJIM.V15I24.25393

Published
2024-07-23
How to Cite
Atmawijaya, R., & Radiyah, U. (2024). PERANCANGAN AUTENTIKASI MULTI FAKTOR DENGAN PENGENALAN WAJAH DAN FIDO (FAST IDENTITY ONLINE). INTI Nusa Mandiri, 19(1), 46-53. https://doi.org/10.33480/inti.v19i1.5263