IJBTT

A Multi-Factor Artificial Intelligence Enabled User Authentication System for Leveraging Integrity and Robustness in Online Examination System

A Multi-Factor Artificial Intelligence Enabled User Authentication System for Leveraging Integrity and Robustness in Online Examination System
	© 2024 by IJETT Journal
	Volume-72 Issue-11
	Year of Publication : 2024
	Author : Vallem Ranadheer Reddy, Gourishetty Shankar Lingam, Pulyala Mahipal Reddy, Nalla Rajender Reddy
	DOI : 10.14445/22315381/IJETT-V72I11P115

How to Cite?
Vallem Ranadheer Reddy, Gourishetty Shankar Lingam, Pulyala Mahipal Reddy, Nalla Rajender Reddy, "A Multi-Factor Artificial Intelligence Enabled User Authentication System for Leveraging Integrity and Robustness in Online Examination System," International Journal of Engineering Trends and Technology, vol. 72, no. 11, pp. 117-130, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I11P115

Abstract
The modern age has increased online education and examination requirements. In the process, several universities are offering courses besides conducting examinations online. There is a necessity for ensuring integrity and robustness in the online examination system due to the proliferation of technologies that may enable participants in online examinations to perform unauthorized activities and violate system rules. There are several ways in which students could violate the rules of integrity of examinations. In this context, ensuring that only authorized and genuine candidates will participate in the examinations is indispensable. The literature shows that there has been a certain effort to conduct online examinations with integrity. However, further leveraging the integrity and robustness of Artificial Intelligence (AI) enabled multi-factor authentication system is needed. We presented a paradigm in this research with mechanisms and algorithms towards a multi factor Artificial Intelligence (AI) enabled user authentication system for leveraging integrity and robustness in online examination systems. The system has multiple layers of authentication mechanisms with an approach that throws challenges like user ID and password, one-time passwords through handheld devices, and deep learning to recognize face models. I proposed an algorithm known as AI enabled Multi-factor Authentication (AIMA), which has the desired mechanisms to realize the proposed framework. Our investigational study demonstrated that the suggested framework can ensure the integrity and robustness of the online examination system regarding user authentication. The AI enabled space recognition system as part of multi factor authentication is superior to many existing methods, with the highest accuracy, 98.74%.

Keywords
Online examination, Multi-factor authentication, Security, Integrity and robustness, Deep learning.

References
[1] Mireya Lucia Hernandez-Jaimes et al., “Artificial Intelligence for IOMT Security: A Review of Intrusion Detection Systems, Attacks, Datasets and Cloud-Fog-Edge Architectures,” Internet of Things, vol. 23, pp. 1-33, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Shunliang Zhang, and Dali Zhu, “Towards Artificial Intelligence-Enabled 6G: State of The Art, Challenges, and Opportunities,” Computer Networks, vol. 183, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Zhi Zhou et al., “Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing,” Proceedings of the IEEE, vol. 107, no. 8, pp. 1738-1762, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Chafika Benzaid, Tarik Taleb, and Muhammad Zubair Farooqi, “Trust in 5G and Beyond Networks,” IEEE Network, vol. 35, no. 3, pp. 212-222, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Chafika Benzaid, and Tarik Taleb, “ZSM Security: Threat Surface and Best Practices,” IEEE Network, vol. 34, no. 3, pp. 124-133, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Yusra Abdulrahman et al., “AI and Blockchain Synergy in Aerospace Engineering: An Impact Survey on Operational Efficiency and Technological Challenges,” IEEE Access, vol. 11, pp. 87790-87804, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Huangxun Chen et al., “EchoFace: Acoustic Sensor-Based Media Attack Detection for Face Authentication,” IEEE Internet of Things Journal, vol. 7, no. 3, pp. 2152-2159, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Maheen Zulfiqar et al., “Deep Face Recognition for Biometric Authentication,” 2019 International Conference on Electrical, Communication, and Computer Engineering, Swat, Pakistan, pp. 1-6, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Hind Baqeel, and Saqib Saeed, “Face Detection Authentication on Smartphones: End Users Usability Assessment Experiences,” 2019 International Conference on Computer and Information Sciences, Sakaka, Saudi Arabia, pp. 1-6, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Navya Saxena, and Devina Varshney, “Smart Home Security Solutions using Facial Authentication and Speaker Recognition through Artificial Neural Networks,” International Journal of Cognitive Computing in Engineering, vol. 2, pp. 154-164, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Xinman Zhang et al., “An Efficient Android-Based Multimodal Biometric Authentication System with Face and Voice,” IEEE Access, vol. 8, pp. 102757-102772, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Young Kyun Jang, and Nam Ik Cho, “Deep Face Image Retrieval for Cancelable Biometric Authentication,” 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance, Taipei, Taiwan, pp. 1-8, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Vera Wati et al., “Security of Facial Biometric Authentication for Attendance System,” Multimedia Tools and Applications, vol. 80, pp. 23625-23646, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Emna Fourati, Wael Elloumi, and Aladine Chetouani, “Anti-Spoofing in Face Recognition-Based Biometric Authentication Using Image Quality Assessment,” Multimedia Tools and Applications, vol. 79, pp. 865-889, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Muhammad Irwan Padli Nasution et al., “Face Recognition Login Authentication for Digital Payment Solution at COVID-19 Pandemic,” 2020 3rd International Conference on Computer and Informatics Engineering, Yogyakarta, Indonesia, pp. 48-51, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Xiang Wang et al., “A Privacy-Preserving Edge Computation-Based Face Verification System for User Authentication,” IEEE Access, vol. 7, pp. 14186-14197, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Chen Wang et al., “User Authentication on Mobile Devices: Approaches, Threats and Trends,” Computer Networks, vol. 107, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Dana Weitzner, David Mendlovic, and Raja Giryes, “Face Authentication from Grayscale Coded Light Field,” 2020 IEEE International Conference on Image Processing, Abu Dhabi, United Arab Emirates, pp. 2611-2615, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Mikel Labayen et al., “Online Student Authentication and Proctoring System Based on Multimodal Biometrics Technology,” IEEE Access, vol. 9, pp. 72398-72411, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Jinsu Kim, and Namje Park, “Lightweight Knowledge-Based Authentication Model for Intelligent Closed-Circuit Television in Mobile Personal Computing,” Personal and Ubiquitous Computing, vol. 26, pp. 345-353, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Abdeljebar Mansour et al., “A Lightweight Seamless Unimodal Biometric Authentication System,” Procedia Computer Science, vol. 231, pp. 190-197, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Vipul Vekariya et al, “Multi-Biometric Fusion for Enhanced Human Authentication in Information Security,” Measurement: Sensors, vol. 31, pp. 1-8, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Wang Yao et al., “A Study on the Effect of Ageing in Facial Authentication and the Utility of Data Augmentation to Reduce Performance Bias Across Age Groups,” IEEE Access, vol. 11, pp. 97118-97134, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Bahareh Nakisa et al., “Using an Extended Technology Acceptance Model to Investigate Facial Authentication,” Telematics and Informatics Reports, vol. 12, pp. 1-11, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Lin Li et al., “A Survey of PPG's Application in Authentication,” Computers & Security, vol. 135, pp. 1-13, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Pietro Ruiu et al., “Enhancing eID Card Mobile-Based Authentication Through 3D Facial Reconstruction,” Journal of Information Security and Applications, vol. 77, pp. 1-14, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Tingcong Jiang et al., “FaceGroup: Continual Face Authentication via Partially Homomorphic Encryption & Group Testing,” 2023 IEEE 20th International Conference on Mobile Ad Hoc and Smart Systems, Toronto, ON, Canada, pp. 443-451, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Wang Yao et al., “Toward Robust Facial Authentication for Low-Power Edge-AI Consumer Devices,” IEEE Access, vol. 10, pp. 123661-123678, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[29] Alakananda Mitra et al., “iFace 1.1: A Proof-of-Concept of a Facial Authentication Based Digital ID for Smart Cities,” IEEE Access, vol. 10, pp. 71791-71804, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[30] M. Vasanthi, and K. Seetharaman, “Facial Image Recognition for Biometric Authentication Systems Using a Combination of Geometrical Feature Points and Low Visual Features,” Journal of King Saud University - Computer and Information Sciences, vol. 24, no. 7, pp. 4109-4121, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[31] John Duchi, Elad Hazan, and Yoram Singer, “Adaptive Subgradient Methods for Online Learning and Stochastic Optimization,” Journal of Machine Learning Research, vol. 12, pp. 2121-2159, 2011.
[Google Scholar] [Publisher Link]
[32] Gary B. Huang et al., “Labeled Faces in The Wild: A Database for Studying Face Recognition in Unconstrained Environments,” Workshop on Faces in'Real-Life'Images: Detection, Alignment, and Recognition, Marseille, France, pp. 1-15, 2008.
[Google Scholar] [Publisher Link]
[33] Yi Sun et al., “Deep Learning Face Representation by Joint Identification-Verification,” Advances in Neural Information Processing Systems, vol. 27, 2014.
[Google Scholar] [Publisher Link]
[34] Yi Sun, Xiaogang Wang, and Xiaoou Tang, “Deeply Learned Face Representations Are Sparse, Selective, And Robust,” 2015 IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA, pp. 2892-2900, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[35] Christian Szegedy et al., “Going Deeper with Convolutions,” 2015 IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA, pp. 1-9, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[36] Yaniv Taigman et al., “Deepface: Closing the Gap to Human-Level Performance in Face Verification,” 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, pp. 1701-1708, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[37] Kilian Q. Weinberger, and Lawrence K. Saul, “Distance Metric Learning for Large Margin Nearest Neighbor Classification,” Journal of Machine Learning Research, pp. 207-244, 2009.
[Google Scholar] [Publisher Link]
[38] D. Randall Wilson, and Tony R. Martinez, “The General Inefficiency of Batch Training for Gradient Descent Learning,” Neural Networks, vol. 16, no. 10, pp. 1429-1451, 2003.
[CrossRef] [Google Scholar] [Publisher Link]
[39] Lior Wolf, Tal Hassner, and Itay Maoz, “Face Recognition in Unconstrained Videos with Matched Background Similarity,” CVPR, Colorado Springs, CO, USA, pp. 1-6, 2011.
[CrossRef] [Google Scholar] [Publisher Link]