Authors : Paul Ndudiri Ohia; Friday Eleonu Onuodu; Dr. Ugochi Adaku Okengwu

Volume/Issue : Volume 11 - 2026, Issue 9 - September

Google Scholar : https://tinyurl.com/y3amuu2b

DOI : https://doi.org/10.38124/ijisrt/26sep945

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : his paper presents a comparative evaluation of contextual-only and machine learning-enhanced biometric access-control systems in an edge computing environment. The contextual only baseline used device trust, known location, login time, access history, resource sensitivity, contextual risk prediction, and Chinese Wall policy enforcement to produce grant, step-up, or deny decisions. However, contextual trust alone cannot directly confirm that the requester is the genuine enrolled user. The enhanced system addressed this limitation by integrating MobileNetV2 face recognition, EfficientNetB0 fingerprint recognition, 1D CNN/Conv1D contextual analysis, biometric confidence scoring, and policy-aware authorization at the edge. Evaluation was carried out using training and validation curves, confusion matrices, FAR/FRR analysis, contextual-only and biometric-enhanced decision-outputs, decision transition heatmaps, and proportional decision composition graphs. The results show that biometric enhancement improves identity assurance, strengthens decision quality, and reduces overdependence on contextual signals in distributed edge access-control environments.

Keywords : Edge Computing, Bimodal Biometrics, Contextual Access Control, Machine Learning, MobileNetV2, EfficientNetB0, 1D CNN, Chinese Wall Policy, Access Decision.

References :

1. Hameed, S., et al., "Securing distributed systems: A survey on access control techniques for cloud, blockchain, IoT and SDN," Cyber Security and Applications, vol. 1, p. 100015, 2023.
2. Satyanarayanan, M., "The emergence of edge computing," Computer, vol. 50, no. 1, pp. 30-39, 2017.
3. Shakarami, A., et al., "Contemporary advances in multi-access edge computing: A survey of fundamentals, architecture, technologies, deployment cases, security, challenges, and directions," Journal of Network and Computer Applications, vol. 219, p. 103726, 2023.
4. Kaur, K., Garg, S., Aujla, G. S., Kumar, N., Rodrigues, J. J. P. C., and Guizani, M., "Edge computing in the industrial Internet of Things environment: Software-defined-networks-based edge-cloud interplay," IEEE Communications Magazine, vol. 56, no. 2, pp. 44-51, 2018.
5. Muhammad, A., et al., "Dynamic access control at the network edge using adaptive risk-based access control systems," 2024.
6. Brewer, D. F. C. and Nash, M. J., "The Chinese Wall security policy," Proceedings of the IEEE Symposium on Security and Privacy, pp. 206-214, 1989.
7. Chandramouli, R. and Butcher, Z., "A Zero Trust Architecture Model for Access Control in Cloud-Native Applications in Multi-Cloud Environments," National Institute of Standards and Technology, 2023.
8. Hu, V. C., Ferraiolo, D., Kuhn, R., Schnitzer, A., Sandlin, K., Miller, R., and Scarfone, K., "Guide to Attribute Based Access Control (ABAC) Definition and Considerations," NIST Special Publication 800-162, 2014.
9. Rahman, A., et al., "Risk-adaptive access control using machine learning for cyber-physical systems," 2022.
10. Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., and Gulin, A., "CatBoost: Unbiased boosting with categorical features," Advances in Neural Information Processing Systems, 2018.
11. Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L. C., "MobileNetV2: Inverted residuals and linear bottlenecks," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510-4520, 2018.
12. Tan, M. and Le, Q. V., "EfficientNet: Rethinking model scaling for convolutional neural networks," Proceedings of the International Conference on Machine Learning, pp. 6105-6114, 2019.
13. Jain, A. K., Ross, A., and Prabhakar, S., "An introduction to biometric recognition," IEEE Transactions on Circuits and Systems for Video Technology, vol. 14, no. 1, pp. 4-20, 2004.
14. Ross, A. and Jain, A. K., "Multimodal biometrics: An overview," Proceedings of the European Signal Processing Conference, pp. 1221-1224, 2004.
15. Cao, Q., Shen, L., Xie, W., Parkhi, O. M., and Zisserman, A., "VGGFace2: A dataset for recognising faces across pose and age," Proceedings of the IEEE International Conference on Automatic Face and Gesture Recognition, pp. 67-74, 2018.
16. Ruiz Garcia, S., "SOCOFing: Sokoto Coventry Fingerprint Dataset," Kaggle, 2018.