Multi-dimensional Identity Construction and Continuous Authentication Methods: A Survey

Zhiwei Zhang

¹School of Computer Science and Technology, Xidian University, Xi’an, Shaanxi, 710071, China

²Shaanxi key Laboratory of Network and System Security, Xidian University, Xi’an, Shaanxi, 710071, China

*Corresponding author

In the rapidly evolving digital era, where interconnected devices like smartphones proliferate, sensitive data exchange has surged, amplifying cybersecurity risks. Therefore, a need for robust authentication to safeguard privacy and prevent unauthorized access, driving innovative solutions that enhance digital security. This survey paper focuses on the critical need for advanced authentication methods to address escalating cyber threats and the shortcomings of traditional single-factor authentication. Conventional methods, reliant on passwords or tokens, are highly susceptible to breaches and user negligence, leaving significant security vulnerabilities. This study bridges these gaps through an in-depth exploration of Continuous Authentication (CA) techniques, which utilize real-time monitoring of user behavior and biometric data to enhance security and usability. A comprehensive analysis of multi-dimensional identity factors (physiological, behavioral, and context-aware) highlights how their integration can improve reliability while respecting privacy. The study provides insights into balancing security, usability, and privacy, guiding the development of modern, user-centric authentication frameworks.

Continuous authentication, Biometric data, Single-factor authentication, Traditional methods, Privacy

Talha Hussain Hashmi, Guiyuan Tang, Xiaowei Liu, and Zhiwei Zhang (2025). Multi-dimensional Identity Construction and Continuous Authentication Methods: A Survey. Journal of Networking and Network Applications, Volume 5, Issue 4, pp. 176–197. https://doi.org/10.33969/J-NaNA.2025.050404.

[1] D. Miller, L. Abed Rabho, P. Awondo, M. de Vries, M. Duque, P. Garvey et al., The global smartphone: Beyond a youth technology. UCL Press, 2021.

[2] A. Zielonka, M. Wo´zniak, S. Garg, G. Kaddoum, M. J. Piran, and G. Muhammad, “Smart homes: How much will they support us? A research on recent trends and advances,” IEEE Access, vol. 9, pp. 26388–26419, 2021.

[3] C. P. Andriotis and K. G. Papakonstantinou, “Managing engineering systems with large state and action spaces through deep reinforcement learning,” Reliability Engineering & System Safety, vol. 191, p. 106483, 2019.

[4] X. Wang, Z. Yan, R. Zhang, and P. Zhang, “Attacks and defenses in user authentication systems: A survey,” Journal of Network and Computer Applications, vol. 188, p. 103080, 2021.

[5] C. Wang, Y. Wang, Y. Chen, H. Liu, and J. Liu, “User authentication on mobile devices: Approaches, threats and trends,” Computer Networks, vol. 170, p. 107118, 2020.

[6] D. K¨ohler, E. Klieme, M. Kreuseler, F. Cheng, and C. Meinel, “Assess-ment of remote biometric authentication systems: another take on the quest to replace passwords,” in 2021 IEEE 5th International Conference on Cryptography, Security and Privacy (CSP), 2021, pp. 22–31.

[7] V. Parmar, H. A. Sanghvi, R. H. Patel, and A. S. Pandya, “A compre-hensive study on passwordless authentication,” in 2022 International Conference on Sustainable Computing and Data Communication Sys-tems (ICSCDS), 2022, pp. 1266–1275.

[8] M. A. Al Kabir and W. Elmedany, “An overview of the present and future of user authentication,” in 2022 4th IEEE Middle East and North Africa COMMunications Conference (MENACOMM), 2022, pp. 10–17.

[9] A. Suokas, “Privileged Accounts Protection with Multi-factor Authen-tication,” 2023.

[10] P. Bansal and A. Ouda, “Continuous Authentication in the Digital Age: An Analysis of Reinforcement Learning and Behavioral Biometrics,” Computers, vol. 13, p. 103, 2024.

[11] D. Temoshok, Y.-Y. Choong, R. Galluzzo, M. LaSalle, A. Regenscheid, D. Proud-Madruga, et al., “NIST SP 800-63-4: Digital Identity Guide-lines,” National Institute of Standards and Technology, Gaithersburg, MD, 2025.

[12] I. Stylios, S. Kokolakis, O. Thanou, and S. Chatzis, “Behavioral biometrics & continuous user authentication on mobile devices: A survey,” Information Fusion, vol. 66, pp. 76–99, 2021.

[13] W. Yang, S. Wang, N. M. Sahri, N. M. Karie, M. Ahmed, and C. Valli, “Biometrics for internet-of-things security: A review,” Sensors, vol. 21,

p. 6163, 2021.

[14] A. M. Mostafa, M. Ezz, M. K. Elbashir, M. Alruily, E. Hamouda, M. Alsarhani et al., “Strengthening cloud security: an innovative multi-factor multi-layer authentication framework for cloud user authentica-tion,” Applied Sciences, vol. 13, p. 10871, 2023.

[15] M. J. Rooney, “An Empirical Assessment of the Use of Password Workarounds and the Cybersecurity Risk of Data Breaches,” Nova Southeastern University, 2023.

[16] M. Wang, S. Wang, and J. Hu, “Cancellable template design for privacy-preserving EEG biometric authentication systems,” IEEE Transactions on Information Forensics and Security, vol. 17, pp. 3350–3364, 2022.

[17] G. Singh, G. Bhardwaj, S. V. Singh, and V. Garg, “Biometric identifi-cation system: security and privacy concern,” Artificial intelligence for a sustainable industry 4.0, pp. 245–264, 2021.

[18] N. H. C. Kamaruddin and M. F. Zolkipli, “The Role of Multi-Factor Authentication in Mitigating Cyber Threats,” Borneo International Journal, vol. 7, pp. 35–42, 2024.

[19] S. P. Otta, S. Panda, M. Gupta, and C. Hota, “A systematic survey of multi-factor authentication for cloud infrastructure,” Future Internet, vol. 15, p. 146, 2023.

[20] S. L. Burton, “Advancing Cybersecurity: Strategic Insights Into Multi-factor Authentication,” Organizational Readiness and Research: Secu-rity, Management, and Decision Making, pp. 247–282, 2025.

[21] I. Ahmed and A. Asghar, “Evaluating the Efficacy of Biometric Authentication Techniques in Healthcare,” International Journal of Responsible Artificial Intelligence, vol. 13, pp. 1–12, 2023.

[22] R. Alrawili, A. A. S. AlQahtani, and M. K. Khan, “Comprehensive survey: Biometric user authentication application, evaluation, and dis-cussion,” Computers and Electrical Engineering, vol. 119, p. 109485, 2024.

[23] T. Bisztray, “Investigating Privacy Aspects of Identity Management: From Data Protection Impact Assessment for Biometric Applications to Privacy-Centric Password Testing,” 2023.

[24] K. Coombs, “Perspectives of Cybersecurity Risks in DNA Data Storage Environment,” Capella University, 2024.

[25] T. Kumar, S. Bhushan, P. Sharma, and V. Garg, “Examining the Vul-nerabilities of Biometric Systems: Privacy and Security Perspectives,” in Leveraging Computer Vision to Biometric Applications, Chapman and Hall/CRC, 2024, pp. 34–67.

[26] A. M. Abdulkareem and A. Gordon, “Evaluating the Usability and User Acceptance of Biometric Authentication in Different Applications,” Quarterly Journal of Emerging Technologies and Innovations, vol. 8, pp. 1–10, 2023.

[27] G. Abdelkader, K. Elgazzar, and A. Khamis, “Connected vehicles: Technology review, state of the art, challenges and opportunities,” Sensors, vol. 21, p. 7712, 2021.

[28] S. Oduri, “Continuous Authentication and Behavioral Biometrics: Enhancing Cybersecurity in the Digital Era,” International Journal of Innovative Research in Science Engineering and Technology, vol. 13, pp. 13632–13640, 2024.

[29] L. D. Chhibbar, S. Patni, S. Todi, A. Bhatia, and K. Tiwari, “Enhancing security through continuous biometric authentication using wearable sensors,” Internet of Things, vol. 28, p. 101374, 2024.

[30] J. Solano, L. Camacho, A. Correa, C. Deiro, J. Vargas, and M. Ochoa, “Combining behavioral biometrics and session context analytics to enhance risk-based static authentication in web applications,” Inter-national Journal of Information Security, vol. 20, pp. 181–197, 2021.

[31] Y. Cahyaningrum, “Evaluation of System Access Security in The Implementation of MultiFactor Authentication (MFA) in Educational Institutions,” Journal of Practical Computer Science, vol. 4, pp. 11–19, 2024.

[32] M. L. Rathod and A. Meera, “Modeling of a Smart Antenna System with Adaptive Beam Forming Technology,” in 2023 10th International Conference on Computing for Sustainable Global Development (INDI-ACom), 2023, pp. 767–772.

[33] O. A. Farayola, O. L. Olorunfemi, and P. O. Shoetan, “Data privacy and security in it: a review of techniques and challenges,” Computer Science & IT Research Journal, vol. 5, pp. 606–615, 2024.

[34] P. M. S. S´anchez, J. M. J. Valero, A. H. Celdr´an, G. Bovet, M. G. P´erez, and G. M. P´erez, “A survey on device behavior fingerprinting: Data sources, techniques, application scenarios, and datasets,” IEEE Communications Surveys & Tutorials, vol. 23, pp. 1048–1077, 2021.

[35] A. B. Tatar, “Biometric identification system using EEG signals,” Neural Computing and Applications, vol. 35, pp. 1009–1023, 2023.

[36] S. Rostamzadeh, A. Abouhossein, S. Vosoughi, S. B. Gendeshmin, and R. Yarahmadi, “Stress influence on real-world driving identified by monitoring heart rate variability and morphologic variability of electrocardiogram signals: the case of intercity roads,” International journal of occupational safety and ergonomics, vol. 30, pp. 252–263, 2024.

[37] M. Kokila and S. Reddy, “Authentication, Access Control and Scala-bility models in Internet of Things Security-A Review,” Cyber Security and Applications, p. 100057, 2024.

[38] J. Abrera, “Data Privacy and Security in Cloud Computing: A Com-prehensive Review,” Journal of Computer Science and Information Technology, vol. 1, pp. 01–09, 2024.

[39] P. Voigt and A. Von dem Bussche, “The eu general data protection regulation (gdpr),” A Practical Guide, 1st Ed., Cham: Springer Inter-national Publishing, vol. 10, pp. 10–5555, 2017.

[40] S. Bamashmos, N. Chilamkurti, and A. S. Shahraki, “Two-Layered Multi-Factor Authentication Using Decentralized Blockchain in an IoT Environment,” Sensors, vol. 24, p. 3575, 2024.

[41] N. Zeeshan, M. Bakyt, N. Moradpoor, and L. La Spada, “Continuous Authentication in Resource-Constrained Devices via Biometric and Environmental Fusion,” Sensors, vol. 25, p. 5711, 2025.

[42] P. T. Tran-Truong, M. Q. Pham, H. X. Son, D. L. T. Nguyen, M. B. Nguyen, K. L. Tran, et al., “A systematic review of multi-factor authentication in digital payment systems: NIST standards alignment and industry implementation analysis,” Journal of Systems Architecture, vol. 162, p. 103402, 2025.

[43] R. M. Saqib, A. S. Khan, Y. Javed, S. Ahmad, K. Nisar, I. A. Abbasi et al., “Analysis and Intellectual Structure of the Multi-Factor Authentication in Information Security,” Intelligent Automation & Soft Computing, vol. 32, 2022.

[44] S. Ayeswarya and K. J. Singh, “A comprehensive review on secure biometric-based continuous authentication and user profiling,” IEEE Access, vol. 12, pp. 82996–83021, 2024.

[45] O. L. Finnegan, J. W. White Iii, B. Armstrong, E. L. Adams, S. Burkart, M. W. Beets, et al., “The utility of behavioral biometrics in user authentication and demographic characteristic detection: a scoping review,” Systematic Reviews, vol. 13, p. 61, 2024.

[46] D. Boshoff and G. P. Hancke, “A classifications framework for con-tinuous biometric authentication (2018–2024),” Computers & Security, vol. 150, p. 104285, 2025.

[47] T. A. Busey, N. Heise, R. A. Hicklin, B. T. Ulery, and J. Buscaglia, “Characterizing missed identifications and errors in latent fingerprint comparisons using eye-tracking data,” PloS one, vol. 16, p. e0251674, 2021.

[48] M. Abuhamad, A. Abusnaina, D. Nyang, and D. Mohaisen, “Sensor-based continuous authentication of smartphones’ users using behavioral biometrics: A contemporary survey,” IEEE Internet of Things Journal, vol. 8, pp. 65–84, 2020.

[49] S. Ayeswarya and K. J. Singh, “A comprehensive review on secure biometric-based continuous authentication and user profiling,” IEEE Access, 2024.

[50] W. Safder, “PASSWORD SECURITY, AN ANALYSIS OF AUTHEN-TICATION METHODS,” ed, 2024.

[51] F. Binbeshr, K. C. Siong, L. Yee, M. Imam, A. A. Al-Saggaf, and A. A. Abudaqa, “A systematic review of graphical password methods resistant to shoulder-surfing attacks,” International Journal of Information Security, vol. 24, pp. 1–22, 2025.

[52] V. Zimmermann, “From the quest to replace passwords towards sup-porting secure and usable password creation,” 2021.

[53] M. Jubur, P. Shrestha, and N. Saxena, “An In-Depth Analysis of Password Managers and Two-Factor Authentication Tools,” ACM Com-puting Surveys, 2025.

[54] M. Lodder, “Token Based Authentication and Authorization with Zero-Knowledge Proofs for Enhancing Web API Security and Privacy,” 2023.

[55] H. R. M. H. Hamid, N. W. Nordin, N. Y. Abdullah, W. H. W. Ismail, and D. Abdullah, “Two Factor Authentication: Voice Biometric and Token-Based Authentication,” in Applied Problems Solved by Information Technology and Software, Springer, 2024, pp. 27–35.

[56] M. Schink, A. Wagner, F. Unterstein, and J. Heyszl, “Security and trust in open source security tokens,” IACR Transactions on Cryptographic Hardware and Embedded Systems, pp. 176–201, 2021.

[57] T. Sun, N. He, J. Xiao, Y. Yue, X. Luo, and H. Wang, “All Your Tokens are Belong to Us: Demystifying Address Verification Vulnerabilities in Solidity Smart Contracts,” arXiv preprint arXiv:2405.20561, 2024.

[58] S. Singh, S. Sharma, M. Awasthi, S. Rawat, and Y. Chanti, “Advance-ments of Emerging Technologies in Biometrics Authentication,” in 2024 IEEE 1st Karachi Section Humanitarian Technology Conference (KHI-HTC), 2024, pp. 1–7.

[59] P. Aithal, “Implementation of Voice Biometric System in the Banking Sector,” International Journal of Applied Engineering and Management Letters (IJAEML), vol. 8, pp. 120–127, 2024.

[60] H. Gururaj, B. Soundarya, S. Priya, J. Shreyas, and F. Flammini, “A Comprehensive Review of Face Recognition Techniques, Trends and Challenges,” IEEE Access, 2024.

[61] V. Upadhyaya, “Advancements in Computer Vision for Biometrics Enhancing Security and Identification,” in Leveraging Computer Vision to Biometric Applications, Chapman and Hall/CRC, 2025, pp. 260–292.

[62] J. Rose, A. Zabin, and T. Bourlai, “On Assessing the Impact of Ocular Pathologies on the Performance of Deep Learning Ocular Based Recognition Systems in the Visible and NIR Bands,” in Face Recognition Across the Imaging Spectrum, Springer, 2024, pp. 233–252.

[63] A. K. Jain, A. A. Ross, K. Nandakumar, and T. Swearingen, “Iris Recognition,” in Introduction to Biometrics, Springer, 2024, pp. 175–214.

[64] G. K. Kyeremeh, M. Abdul-Al, R. Qahwaji, and R. Abd-Alhameed, “Verification technology for finger vein biometric,” arXiv preprint arXiv:2405.11540, 2024.

[65] H. M. Ahmed, D. Elsheweikh, and S. Shaban, “Security system based on hand geometry and palmprint for user authentication in E-correction system,” International Journal of Information Technology, vol. 16, pp. 1783–1799, 2024.

[66] S. Li, L. Fei, B. Zhang, X. Ning, and L. Wu, “Hand-based multimodal biometric fusion: A review,” Information Fusion, p. 102418, 2024.

[67] P. Rad, G. Dorai, and M. Jozani, “From Seaweed to Security: The Emergence of Alginate in Compromising IoT Fingerprint Sensors,” arXiv preprint arXiv:2404.02150, 2024.

[68] M. H. Memon, J.-P. Li, I. Memon, and Q. A. Arain, “GEO matching re-gions: multiple regions of interests using content based image retrieval based on relative locations,” Multimedia Tools and Applications, vol. 76, pp. 15377–15411, 2017.

[69] M. Saideh, J.-P. Jamont, and L. Vercouter, “Opportunistic Sensor-Based Authentication Factors in and for the Internet of Things,” Sensors, vol. 24, p. 4621, 2024.

[70] Y. Zheng, Z. Li, X. Xu, and Q. Zhao, “Dynamic defenses in cyber se-curity: Techniques, methods and challenges,” Digital Communications and Networks, vol. 8, pp. 422–435, 2022.

[71] N. R. Mayeke, A. T. Arigbabu, O. O. Olaniyi, O. J. Okunleye, and C. S. Adigwe, “Evolving Access Control Paradigms: A Comprehensive Multi-Dimensional Analysis of Security Risks and System Assurance in Cyber Engineering,” Available at SSRN, 2024.

[72] I. Traore, Continuous Authentication Using Biometrics: Data, Models, and Metrics: Data, Models, and Metrics. Igi Global, 2011.

[73] L. Ibanez-Lissen, J. M. De Fuentes, L. Gonzalez-Manzano, and N. Anciaux, “Continuous Authentication Leveraging Matrix Profile,” in Proceedings of the 19th International Conference on Availability, Reliability and Security, 2024, pp. 1–13.

[74] I. Stylios and S. Aegean, “Behavioral biometrics for continuous au-thentication: security and privacy issues,” University of the Aegean, Greece, 2023.

[75] M. F. Hasan, F. Ashfaq, A. A. Chowdhury, S. I. Hamim, and M. Rahmani, “Dynamic authentication protocols for advanced security in federated metaverse systems,” Brac University, 2024.

[76] M. O. Abolarinwa, “Enhanced Local Binary Pattern with Chinese Remainder Theorem and Swarm Intelligent Technique for Face Recog-nition System,” Kwara State University (Nigeria), 2022.

[77] L. Lu, J. Yu, Y. Chen, and Y. Wang, “Vocallock: Sensing vocal tract for passphrase-independent user authentication leveraging acoustic signals on smartphones,” Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 4, pp. 1–24, 2020.

[78] B. Alharbi and H. S. Alshanbari, “Face-voice based multimodal bio-metric authentication system via FaceNet and GMM,” PeerJ Computer Science, vol. 9, p. e1468, 2023.

[79] F. Abbaas and G. Serpen, “Evaluation of biometric user authentication using an ensemble classifier with face and voice recognition,” arXiv preprint arXiv:2006.00548, 2020.

[80] M. Stokkenes, R. Ramachandra, A. Mohammadi, S. Venkatesh, K. Raja, P. Wasnik et al., “Smartphone Multi-modal Biometric Presen-tation Attack Detection,” in Handbook of Biometric Anti-Spoofing: Presentation Attack Detection and Vulnerability Assessment, Springer, 2023, pp. 521–544.

[81] H. Fereidooni, J. K¨onig, P. Rieger, M. Chilese, B. G¨okbakan, M. Finke et al., “AuthentiSense: A Scalable Behavioral Biometrics Authentica-tion Scheme using Few-Shot Learning for Mobile Platforms,” arXiv preprint arXiv:2302.02740, 2023.

[82] A. Alshardan, A. Kumar, M. Alghamdi, M. Maashi, S. Alahmari, A.A. Alharbi et al., “Multimodal biometric identification: leveraging con-volutional neural network (CNN) architectures and fusion techniques with fingerprint and finger vein data,” PeerJ Computer Science, vol. 10, p. e2440, 2024.

[83] C. Zhang, Y. Zhao, Y. Huang, M. Zeng, S. Ni, M. Budagavi et al., “Facial: Synthesizing dynamic talking face with implicit attribute learning,” in Proceedings of the IEEE/CVF international conference on computer vision, 2021, pp. 3867–3876.

[84] A. K. Yadav, R. Pateriya, N. K. Gupta, P. Gupta, D. K. Saini, and M. Alahmadi, “Hybrid Machine Learning Model for Face Recognition Using SVM,” Computers, Materials & Continua, vol. 72, 2022.

[85] P. A. Thomas and K. Preetha Mathew, “A broad review on non-intrusive active user authentication in biometrics,” Journal of Ambient Intelligence and Humanized Computing, vol. 14, pp. 339–360, 2023.

[86] J. Han and B. Bhanu, “Individual recognition using gait energy image,” IEEE transactions on pattern analysis and machine intelligence, vol. 28, pp. 316–322, 2005.

[87] K. Shiraga, Y. Makihara, D. Muramatsu, T. Echigo, and Y. Yagi, “Geinet: View-invariant gait recognition using a convolutional neural network,” in 2016 international conference on biometrics (ICB), 2016, pp. 1–8.

[88] Z. Wu, Y. Huang, L. Wang, X. Wang, and T. Tan, “A comprehensive study on cross-view gait based human identification with deep cnns,” IEEE transactions on pattern analysis and machine intelligence, vol. 39, pp. 209–226, 2016.

[89] R. Zhang, D. Yin, Z. Zhou, Z. Cao, F. Meng, and B. Hu, “Improving cross-view gait recognition with generative adversarial networks,” Electrical Engineering and Computer Science (EECS), vol. 3, pp. 43–47, 2019.

[90] W. Zeng, C. Wang, and F. Yang, “Silhouette-based gait recognition via deterministic learning,” Pattern recognition, vol. 47, pp. 3568–3584, 2014.

[91] Z. Qin, Y. Liu, P. Ji, D. Kim, L. Wang, R. I. McKay et al., “Fusing higher-order features in graph neural networks for skeleton-based ac-tion recognition,” IEEE Transactions on Neural Networks and Learning Systems, vol. 35, pp. 4783–4797, 2022.

[92] H. Chao, K. Wang, Y. He, J. Zhang, and J. Feng, “GaitSet: Cross-view gait recognition through utilizing gait as a deep set,” IEEE transactions on pattern analysis and machine intelligence, vol. 44, pp. 3467–3478, 2021.

[93] T. Huang, X. Ben, C. Gong, W. Xu, Q. Wu, and H. Zhou, “GaitDAN: Cross-view Gait Recognition via Adversarial Domain Adaptation,” IEEE Transactions on Circuits and Systems for Video Technology, 2024.

[94] W. Cheng, X. Yan, and Y. Luo, “Gaitstc: A Pure Spatial-Temporal Convolution Network For Skeleton-Based Gait Recognition,” in 2024 4th International Conference on Neural Networks, Information and Communication (NNICE), 2024, pp. 759–765.

[95] M. Gadaleta and M. Rossi, “Idnet: Smartphone-based gait recognition with convolutional neural networks,” Pattern Recognition, vol. 74, pp. 25–37, 2018.

[96] R. N. Yousef, A. T. Khalil, A. S. Samra, and M. M. Ata, “Proposed methodology for gait recognition using generative adversarial network with different feature selectors,” Neural Computing and Applications, vol. 36, pp. 1641–1663, 2024.

[97] R. Ghosh, “A Faster R-CNN and recurrent neural network based approach of gait recognition with and without carried objects,” Expert Systems with Applications, vol. 205, p. 117730, 2022.

[98] C. Wang, Y. Gao, Y. Li, and M. Zhang, “GaitParse: Gait Parsing Algorithm with Self-Supervised Fine-Tuning for Gait Recognition,” in Proceedings of the 2023 9th International Conference on Communica-tion and Information Processing, 2023, pp. 85–92.

[99] C. Shen, S. Yu, J. Wang, G. Q. Huang, and L. Wang, “A Compre-hensive Survey on Deep Gait Recognition: Algorithms, Datasets, and Challenges,” IEEE Transactions on Biometrics, Behavior, and Identity Science, 2024.

[100] A. Ray-Dowling, Evaluating Multi-Modality Mobile Behavioral Bio-metric Fusion Using Public Datasets. Clarkson University, 2023.

[101] D. Gafurov, “A survey of biometric gait recognition: Approaches, security and challenges,” in Annual Norwegian computer science conference, 2007, pp. 19–21.

[102] E. Ellavarason, R. Guest, F. Deravi, R. Sanchez-Riello, and B. Corsetti, “Touch-dynamics based behavioural biometrics on mobile devices–a review from a usability and performance perspective,” ACM Computing Surveys (CSUR), vol. 53, pp. 1–36, 2020.

[103] W. Liu, C. Zhang, H. Ma, and S. Li, “Learning efficient spatial-temporal gait features with deep learning for human identification,” Neuroinformatics, vol. 16, pp. 457–471, 2018.

[104] D. Kumar and R. Verma, “A CNN-RF Hybrid Model for Cross View Gait Recognition Method using CASIA-B Cross,” in 2024 2nd International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS), 2024, pp. 156–162.

[105] J. Daugman, “New methods in iris recognition,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 37, pp. 1167–1175, 2007.

[106] R. P. Wildes, “Iris recognition: an emerging biometric technology,” Proceedings of the IEEE, vol. 85, pp. 1348–1363, 1997.

[107] H. Proenc¸a and L. A. Alexandre, “Toward noncooperative iris recog-nition: A classification approach using multiple signatures,” IEEE transactions on pattern analysis and machine intelligence, vol. 29, pp. 607–612, 2007.

[108] Z. G. A. Hasan, “Iris Recognition Method for Non-cooperative Im-ages,” in International Conference on Micro-Electronics and Telecom-munication Engineering, 2023, pp. 275–288.

[109] Z. Yan, L. He, Y. Wang, Z. Sun, and T. Tan, “Flexible iris matching based on spatial feature reconstruction,” IEEE Transactions on Biomet-rics, Behavior, and Identity Science, 2021.

[110] M. Omran and E. N. AlShemmary, “An iris recognition system using deep convolutional neural network,” in Journal of Physics: Conference Series, 2020, p. 012159.

[111] D. Kerrigan, M. Trokielewicz, A. Czajka, and K. W. Bowyer, “Iris recognition with image segmentation employing retrained off-the-shelf deep neural networks,” in 2019 International Conference on Biometrics (ICB), 2019, pp. 1–7.

[112] S. Ahmad and B. Fuller, “Unconstrained iris segmentation using convo-lutional neural networks,” in Computer Vision–ACCV 2018 Workshops: 14th Asian Conference on Computer Vision, Perth, Australia, December 2–6, 2018, Revised Selected Papers 14, 2019, pp. 450–466.

[113] Y. Yin, S. He, R. Zhang, H. Chang, X. Han, and J. Zhang, “Deep learn-ing for iris recognition: a review,” arXiv preprint arXiv:2303.08514, 2023.

[114] K. Nguyen, H. Proenc¸a, and F. Alonso-Fernandez, “Deep learning for iris recognition: A survey,” ACM Computing Surveys, vol. 56, pp. 1–35, 2024.

[115] K. Liang, J. Chen, T. He, W. Wang, A. K. Singh, D. B. Rawat et al., “Review of the Open Datasets for Contactless Sensing,” IEEE Internet of Things Journal, 2024.

[116] S. Marcel and J. d. R. Mill´an, “Person authentication using brainwaves (EEG) and maximum a posteriori model adaptation,” IEEE transactions on pattern analysis and machine intelligence, vol. 29, pp. 743–752, 2007.

[117] M. Hosseinzadeh, B. Vo, M. Y. Ghafour, and S. Naghipour, “Elec-trocardiogram signals-based user authentication systems using soft computing techniques,” Artificial Intelligence Review, vol. 54, pp. 667–709, 2021.

[118] J. J. Riera, T. Ogawa, T. Goto, A. Sumiyoshi, H. Nonaka, A. Evans et al., “Pitfalls in the dipolar model for the neocortical EEG sources,” Journal of neurophysiology, vol. 108, pp. 956–975, 2012.

[119] B. Kaliappan, B. V. Sudalaiyadumperumal, and S. Thalavaipillai, “Affective analysis in machine learning using AMIGOS with Gaussian expectation-maximization model,” Int J Reconfigurable & Embedded Syst, vol. 13, pp. 201–209, 2024.

[120] Y. Zhang, J. Chen, J. H. Tan, Y. Chen, Y. Chen, D. Li et al., “An investigation of deep learning models for EEG-based emotion recognition,” Frontiers in neuroscience, vol. 14, p. 622759, 2020.

[121] P. Arnau-Gonz´alez, S. Katsigiannis, M. Arevalillo-Herr´aez, and N. Ramzan, “BED: A new data set for EEG-based biometrics,” IEEE Internet of Things Journal, vol. 8, pp. 12219–12230, 2021.

[122] K. S. Priya, S. Vasanthi, R. Nithyanandhan, G. Jeyasheeli, M. Karthiga, and C. Pandi, “Blink talk: A machine learning-based method for women safety using EEG and eye blink signals,” Measurement: Sen-sors, vol. 28, p. 100810, 2023.

[123] X. Zhang, L. Yao, C. Huang, T. Gu, Z. Yang, and Y. Liu, “DeepKey: A multimodal biometric authentication system via deep decoding gaits and brainwaves,” ACM Transactions on Intelligent Systems and Technology (TIST), vol. 11, pp. 1–24, 2020.

[124] N. Sae-Bae, K. Ahmed, K. Isbister, and N. Memon, “Biometric-rich gestures: a novel approach to authentication on multi-touch devices,” in proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 2012, pp. 977–986.

[125] Z. I. Rauen, F. Anjomshoa, and B. Kantarci, “Gesture and sociability-based continuous authentication on smart mobile devices,” in Proceed-ings of the 16th ACM International Symposium on Mobility Manage-ment and Wireless Access, 2018, pp. 51–58.

[126] M. Frank, R. Biedert, E. Ma, I. Martinovic, and D. Song, “Touchalytics: On the applicability of touchscreen input as a behavioral biometric for continuous authentication,” IEEE transactions on information forensics and security, vol. 8, pp. 136–148, 2012.

[127] W. Meng, W. Li, and D. S. Wong, “Enhancing touch behavioral authentication via cost-based intelligent mechanism on smartphones,” Multimedia Tools and Applications, vol. 77, pp. 30167–30185, 2018.

[128] L. Liu, Z. Cao, and T. Li, “FaceTouch: Practical Face Touch Detection with a Multimodal Wearable System for Epidemiological Surveillance,” in Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation, 2023, pp. 13–26.

[129] A. Mahfouz, H. Mostafa, T. M. Mahmoud, and A. Sharaf Eldin, “M2auth: A multimodal behavioral biometric authentication using feature-level fusion,” Neural Computing and Applications, vol. 36, pp. 21781–21799, 2024.

[130] A. Bajaber, M. A. Fadel, and L. A. Elrefaei, “Evaluation of Deep Learning Models for Person Authentication Based on Touch Gesture,” Comput. Syst. Sci. Eng., vol. 42, pp. 465–481, 2022.

[131] M. L. Brocardo, I. Traore, S. Saad, and I. Woungang, “Authorship verification for short messages using stylometry,” in 2013 International Conference on Computer, Information and Telecommunication Systems (CITS), 2013, pp. 1–6.

[132] M. Bhargava, P. Mehndiratta, and K. Asawa, “Stylometric analysis for authorship attribution on twitter,” in Big Data Analytics: Second International Conference, BDA 2013, Mysore, India, December 16–18, 2013, Proceedings 2, 2013, pp. 37–47.

[133] M. Toshevska and S. Gievska, “A review of text style transfer using deep learning,” IEEE Transactions on Artificial Intelligence, vol. 3, pp. 669–684, 2021.

[134] S. Almlawi, J. Fang, and J. Li, “Enhancing Sentiment Analysis Using MCNN-BRNN Model with BERT,” in 2023 3rd International Conference on Electronic Information Engineering and Computer Communication (EIECC), 2023, pp. 574–579.

[135] G. O. Adebayo, “Multimodal stylometry: A novel approach for author-ship identification,” 2024.

[136] G. Bonomi, “Deep Learning Techniques for Authorship Attribution of Literary Texts: Adapting a BERT-Based Model for Analyzing Il Fiore and Detto d’Amore,” ed, 2024.

[137] N. Schaetti and J. Savoy, “Comparison of Visualisable Evidence-based Authorship Attribution Methods using Recurrent Neural Networks,” 2020.

[138] R. Joyce and G. Gupta, “Identity authentication based on keystroke latencies,” Communications of the ACM, vol. 33, pp. 168–176, 1990.

[139] H. Gascon, S. Uellenbeck, C. Wolf, and K. Rieck, “Continuous au-thentication on mobile devices by analysis of typing motion behavior,” 2014.

[140] K. S. Killourhy and R. A. Maxion, “Comparing anomaly-detection algorithms for keystroke dynamics,” in 2009 IEEE/IFIP international conference on dependable systems & networks, 2009, pp. 125–134.

[141] O. Alpar, “Biometric keystroke barcoding: A next-gen authentication framework,” Expert Systems with Applications, vol. 177, p. 114980, 2021.

[142] S. S. Almohamade, “Continuous authentication of users to robotic technologies using behavioural biometrics,” University of Sheffield, 2022.

[143] S. P. Banerjee and D. L. Woodard, “Biometric authentication and identification using keystroke dynamics: A survey,” Journal of Pattern recognition research, vol. 7, pp. 116–139, 2012.

[144] Y. Zhang, W. Hu, W. Xu, C. T. Chou, and J. Hu, “Continuous authentication using eye movement response of implicit visual stimuli,” proceedings of the acm on interactive, mobile, wearable and ubiquitous technologies, vol. 1, pp. 1–22, 2018.

[145] I. Sluganovic, M. Roeschlin, K. B. Rasmussen, and I. Martinovic, “Analysis of reflexive eye movements for fast replay-resistant biometric authentication,” ACM Transactions on Privacy and Security (TOPS), vol. 22, pp. 1–30, 2018.

[146] M. Javed, Z. Zhang, F. H. Dahri, and A. A. Laghari, “Real-time deepfake video detection using eye movement analysis with a hybrid deep learning approach,” Electronics, vol. 13, p. 2947, 2024.

[147] R. Zemblys, D. C. Niehorster, O. Komogortsev, and K. Holmqvist, “Us-ing machine learning to detect events in eye-tracking data,” Behavior research methods, vol. 50, pp. 160–181, 2018.

[148] M. Yang, Y. Gao, L. Tang, J. Hou, and B. Hu, “Wearable eye-tracking system for synchronized multimodal data acquisition,” IEEE Transactions on Circuits and Systems for Video Technology, 2023.

[149] H. Sbeyti, “Mobile user authentication based on user behavioral pattern (MOUBE),” International Journal of Computer Science and Security (IJCSS), vol. 10, p. 1, 2016.

[150] H. Gomi, S. Yamaguchi, K. Tsubouchi, and N. Sasaya, “Continuous authentication system using online activities,” in 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE), 2018, pp. 522–532.

[151] U. Mahbub, J. Komulainen, D. Ferreira, and R. Chellappa, “Continuous authentication of smartphones based on application usage,” IEEE Transactions on Biometrics, Behavior, and Identity Science, vol. 1, pp. 165–180, 2019.

[152] D. Cui, Z. Wang, P. Liu, S. Wang, Z. Zhang, D. G. Dorrell et al., “Battery electric vehicle usage pattern analysis driven by massive real-world data,” Energy, vol. 250, p. 123837, 2022.

[153] K. Gupta, “Machine learning-based device type classification for IoT device re-and continuous authentication,” 2022.

[154] M. A. Hassan, Z. Shukur, and M. K. Hasan, “An improved time-based one time password authentication framework for electronic payments,” Int. J. Adv. Comput. Sci. Appl, vol. 11, pp. 359–366, 2020.

[155] Y.-C. Tian and J. Gao, “Network Security and Privacy Architecture,” in Network Analysis and Architecture, Springer, 2023, pp. 361–402.

[156] D. Dasgupta, A. Roy, and A. Nag, Advances in user authentication. Springer, 2017.

[157] L. Fridman, S. Weber, R. Greenstadt, and M. Kam, “Active authentica-tion on mobile devices via stylometry, application usage, web browsing, and GPS location,” IEEE Systems Journal, vol. 11, pp. 513–521, 2016.

[158] Y. Baseri, A. Senhaji Hafid, D. Makrakis, and H. Fereidouni, “Privacy-Preserving Federated Learning Framework for Risk-Based Adaptive Authentication,” arXiv, 2025.

[159] G. Kang, J. Park, and Y.-G. Kim, “Continuous behavioral biometric au-thentication for secure metaverse workspaces in digital environments,” Systems, vol. 13, p. 588, 2025.

[160] N. Al-Naffakh, N. Clarke, F. Li, and P. Haskell-Dowland, “Real-world continuous smartwatch-based user authentication,” The Computer Jour-nal, vol. 68, pp. 717–733, 2025.

[161] M. K. Jangir, S. K. Dayama, and P. Sundar, “HybridTouch: A Robust Framework for Continuous User Authentication by GAN-Augmented Behavioral Biometrics on Mobile Devices,” Journal of Information Technology Management, vol. 17, pp. 108–129, 2025.

[162] R. Dave, M. Handoko, A. Rashid, and C. Schoenbauer, “From Clicks to Security: Investigating Continuous Authentication via Mouse Dy-namics,” arXiv, 2024.

[163] L. Wan, K. Liu, H. A. Mengash, N. Alruwais, M. Al Duhayyim, and K. Venkatachalam, “Deep learning-based photoplethysmography biometric authentication for continuous user verification,” Applied Soft Computing, vol. 156, p. 111461, 2024.

[164] W. Shao, Z. Liang, R. Zhang, R. Fang, N. Miao, E. Kourkchi, et al., “Know Me by My Pulse: Toward Practical Continuous Authentication on Wearable Devices via Wrist-Worn PPG,” arXiv, 2025.

[165] D. Aguilar, A. Mart´ınez-Cruz, K. A. Ram´ırez-Guti´errez, and M. Morales-Sandoval, “PPG-based biometric authentication: A review on architectures, datasets, attacks and security challenges,” Computational and Structural Biotechnology Journal, vol. 28, pp. 511–528, 2025.

[166] S. M. Arman, T. Yang, S. Shahed, A. Al Mazroa, A. Attiah, and L. Mohaisen, “A comprehensive survey for privacy-preserving biometrics: Recent approaches, challenges, and future directions,” Computers, Materials & Continua, vol. 78, pp. 2087–2110, 2024.

[167] S. Jacob, P. Vinod, and V. G. Menon, “Context-aware privacy-preserving continuous authentication on mobile environments,” Secu-rity and Privacy, vol. 8, 2025.

[168] X. X. Zheng, M. M. Ur Rahma, B. Taha, M. Masood, D. Hatzinakos, and T. Y. Al-Naffouri, “Multimodal Biometric Authentication Using Camera-Based PPG and Fingerprint Fusion,” arXiv, 2024.

[169] G. H. Silva de Carvalho, D. Kaur, I. Woungang, and A. Anpalagan, “Novel Explainable CNN-LightGBM Model for Smartphone Continu-ous Authentication,” SSRN, 2025.

[170] N. Zeeshan, M. Bakyt, N. Moradpoor, and L. La Spada, “Continuous Authentication in Resource-Constrained Devices via Biometric and Environmental Fusion,” Sensors, vol. 25, p. 5711, 2025.

[171] W. Sheng and X. Li, “Multi-task learning for gait-based identity recognition and emotion recognition using attention enhanced temporal graph convolutional network,” Pattern Recognition, vol. 114, p. 107868, 2021.

[172] E.-S. M. El-Kenawy, S. Mirjalili, A. A. Abdelhamid, A. Ibrahim, N. Khodadadi, and M. M. Eid, “Meta-heuristic optimization and keystroke dynamics for authentication of smartphone users,” Mathematics, vol. 10, p. 2912, 2022.

[173] B. A. Alahmadi, L. Axon, and I. Martinovic, “99% false positives: A qualitative study of SOC analysts’ perspectives on security alarms,” in 31st USENIX Security Symposium (USENIX Security 22), 2022, pp. 2783–2800.

[174] N. Kausar, I. U. Din, M. A. Khan, A. Almogren, and B.-S. Kim, “GRA-PIN: A graphical and PIN-based hybrid authentication approach for smart devices,” Sensors, vol. 22, p. 1349, 2022.

[175] A. Qazi, N. Hasan, O. Abayomi-Alli, G. Hardaker, R. Scherer, Y. Sarker et al., “Gender differences in information and communication technology use & skills: a systematic review and meta-analysis,” Education and Information Technologies, pp. 1–34, 2022.

[176] P. De Andr´es, R. Gimeno, and R. M. de Cabo, “The gender gap in bank credit access,” Journal of Corporate Finance, vol. 71, p. 101782, 2021.

[177] J. Sadik and S. Ruoti, “A Large-Scale Survey of Password Entry Practices on Non-Desktop Devices,” arXiv preprint arXiv:2409.03044, 2024.

[178] T. C. Adeniran, R. G. Jimoh, E. U. Abah, N. Faruk, E. Alozie, and A.L. Imoize, “Vulnerability Assessment Studies of Existing Knowledge-Based Authentication Systems: A Systematic Review,” Sule Lamido University Journal of Science & Technology, vol. 8, pp. 34–61, 2024.

[179] M. I. M. Yusop, N. H. Kamarudin, N. H. S. Suhaimi, and M. K. Hasan, “Advancing Passwordless Authentication: A Systematic Review of Methods, Challenges, and Future Directions for Secure User Identity,” IEEE Access, 2025.

[180] S. Baseer and K. Charumathi, “Multi-Factor Authentication: A User Experience Study,” Available at SSRN 4840295, 2024.

[181] J. Kim, G. Yang, J. Kim, S. Lee, K. K. Kim, and C. Park, “Efficiently updating ECG-based biometric authentication based on incremental learning,” Sensors, vol. 21, p. 1568, 2021.

[182] E. Garea-Llano and A. Morales-Gonzalez, “Framework for biometric iris recognition in video, by deep learning and quality assessment of the iris-pupil region,” Journal of Ambient Intelligence and Humanized Computing, vol. 14, pp. 6517–6529, 2023.

[183] S. A. Lone and A. Mir, “Smartphone-based biometric authentication scheme for access control management in client-server environment,” Int J Inf Technol Comput Sci, vol. 14, pp. 34–47, 2022.

[184] I. L. Furuberg and M. Øseth, “From Password to Passwordless: Exploring User Experience Obstacles to the Adoption of FIDO2 Authentication,” NTNU, 2023.

[185] S. An, C. F. Cheung, and K. W. Willoughby, “A gamification ap-proach for enhancing older adults’ technology adoption and knowledge transfer: A case study in mobile payments technology,” Technological Forecasting and Social Change, vol. 205, p. 123456, 2024.

[186] A. P. Umejiaku, P. Dhakal, and V. S. Sheng, “Balancing password security and user convenience: Exploring the potential of prompt models for password generation,” Electronics, vol. 12, p. 2159, 2023.

[187] P. P. Surve, O. Brodt, M. Yampolskiy, Y. Elovici, and A. Shabtai, “SoK: Security Below the OS–A Security Analysis of UEFI,” arXiv preprint arXiv:2311.03809, 2023.

[188] B. T. Alabdulwahab, “The Meaning, Development and Evaluation of Natural User Interface in Education: An Empirical Study with Pen-Based Interaction,” 2024.

[189] O. Inverso, E. Tomasco, B. Fischer, S. La Torre, and G. Parlato, “Bounded verification of multi-threaded programs via lazy sequential-ization,” ACM Transactions on Programming Languages and Systems (TOPLAS), vol. 44, pp. 1–50, 2021.

[190] N. Karim, H. Kanaker, W. Abdulraheem, M. Ghaith, E. Alhroob, and A. Alali, “Choosing the right MFA method for online systems: A comparative analysis,” International Journal of Data and Network Science, vol. 8, pp. 201–212, 2024.

[191] D. Nguyen and B. Beijnon, “The data subject and the myth of the ‘black box’ data communication and critical data literacy as a resistant practice to platform exploitation,” Information, Communication & Society, vol. 27, pp. 333–349, 2024.

[192] D. Harauzek, “Cloud Computing: Challenges of cloud computing from business users perspective-vendor lock-in,” ed, 2022.

[193] Z. Sun, Q. Li, Y. Liu, and Y. Zhu, “Opportunities and challenges for biometrics,” China’s e-Science Blue Book 2020, pp. 101–125, 2021.

[194] M. Fourn´e, “Human Factors in Open Source Security,” Univer-sit¨atsbibliothek, 2024.

[195] P. Jain, H. P¨otter, A. J. Lee, and D. M´osse, “Mafia: Multi-layered architecture for iot-based authentication,” in 2020 Second IEEE In-ternational Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA), 2020, pp. 199–208.

[196] N. A. Karim, O. A. Khashan, H. Kanaker, W. K. Abdulraheem, M. Alshinwan, and A.-K. Al-Banna, “Online banking user authentication methods: a systematic literature review,” Ieee Access, vol. 12, pp. 741–757, 2023.

[197] C. Dong, F. Jiang, S. Chen, and X. Liu, “Continuous authentication for uav delivery systems under zero-trust security framework,” in 2022 IEEE International Conference on Edge Computing and Communica-tions (EDGE), 2022, pp. 123–132.

[198] R. Vasudev, N. Harini, and M. Neethu, “Multi-Factor Authentication System With ID Card Credentials For Secure Transactions,” in 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT), 2023, pp. 1–8.

[199] M. Fanti, Implementing Multifactor Authentication: Protect your ap-plications from cyberattacks with the help of MFA. 2023.

[200] A. Muir, K. Brown, and A. Girma, “Reviewing the Effectiveness of Multi-factor Authentication (MFA) Methods in Preventing Phishing Attacks,” in Proceedings of the Future Technologies Conference, 2024, pp. 597–607.

[201] R. Kovaˇc, “Threat Report T22021,” 2021.

[202] V. Business, “2024 Data Breach Investigations Report,” 2024.

[203] A. Vempati, “Analyzing the Nexus between Cyberaggression and Cybersecurity Insider Threat Dynamics,” Purdue University, 2024.

[204] M. N. Al-Mhiqani, R. Ahmad, Z. Zainal Abidin, W. Yassin, A. Hassan, K. H. Abdulkareem et al., “A review of insider threat detection: Classification, machine learning techniques, datasets, open challenges, and recommendations,” Applied Sciences, vol. 10, p. 5208, 2020.

[205] J. Jeong, J. Mihelcic, G. Oliver, and C. Rudolph, “Towards an improved understanding of human factors in cybersecurity,” in 2019 IEEE 5th International Conference on Collaboration and Internet Computing (CIC), 2019, pp. 338–345.

[206] U. Rauf, F. Mohsen, and Z. Wei, “A taxonomic classification of insider threats: Existing techniques, future directions & recommendations,” Journal of Cyber Security and Mobility, vol. 12, pp. 221–252, 2023.

[207] H. ˙I. Aslan and C. Choi, “VisGIN: Visibility graph neural network on one-dimensional data for biometric authentication,” Expert Systems with Applications, vol. 237, p. 121323, 2024.

[208] A. I. Awad, A. Babu, E. Barka, and K. Shuaib, “AI-powered biometrics for Internet of Things security: A review and future vision,” Journal of Information Security and Applications, vol. 82, p. 103748, 2024.

[209] E. C. P. Neto, H. Taslimasa, S. Dadkhah, S. Iqbal, P. Xiong, T. Rahman et al., “CICIoV2024: Advancing realistic IDS approaches against DoS and spoofing attack in IoV CAN bus,” Internet of Things, vol. 26, p. 101209, 2024.

[210] E. Ritter, “Your Voice Gave You Away: The Privacy Risks of Voice-Inferred Information,” Duke LJ, vol. 71, p. 735, 2021.

[211] W. Lee, J. J. Seong, B. Ozlu, B. S. Shim, A. Marakhimov, and S. Lee, “Biosignal sensors and deep learning-based speech recognition: A review,” Sensors, vol. 21, p. 1399, 2021.

[212] M. H. Uddin, M. H. Ali, and M. K. Hassan, “Cybersecurity hazards and financial system vulnerability: a synthesis of literature,” Risk Management, vol. 22, pp. 239–309, 2020.

[213] J. L. Kr¨oger, L. Gellrich, S. Pape, S. R. Brause, and S. Ullrich, “Per-sonal information inference from voice recordings: User awareness and privacy concerns,” Proceedings on Privacy Enhancing Technologies, 2022.

[214] I. B. A. Ouahab, L. Elaachak, M. Bouhorma, Y. A. Alluhaidan, and B. Zafar, “Voice Biometric Technology: Enhancing Public Safety and Security in Smart Cities,” in 2024 Mediterranean Smart Cities Conference (MSCC), 2024, pp. 1–6.

[215] A. B. Rubin, “A Facial Challenge: Facial Recognition Technology and the Carpenter Doctrine,” Rich. JL & Tech., vol. 27, p. 1, 2020.

[216] A. Gallardo, C. Choy, J. Juneja, E. Bozkir, C. Cobb, L. Bauer et al., “Speculative privacy concerns about AR glasses data collection,” Proceedings on Privacy Enhancing Technologies, 2023.

[217] R. V. Petrescu, “Face recognition as a biometric application,” Journal of Mechatronics and Robotics, vol. 3, p. 237.257, 2019.

[218] T.-K. Ghazali and N.-H. Zakaria, “Security, comfort, healthcare, and energy saving: A review on biometric factors for smart home environ-ment,” Journal of Computers (Taiwan), 2018.

[219] P. Piccolotto and P. Maller, “BIOMETRICS FROM THE USER POINT OF VIEW: DERIVING DESIGN PRINCIPLES FROM USER PER-CEPTIONS AND CONCERNS ABOUT BIOMETRIC SYSTEMS,” Intel Technology Journal, vol. 18, 2014.

[220] D. Sethi, S. Bharti, and C. Prakash, “A comprehensive survey on gait analysis: History, parameters, approaches, pose estimation, and future work,” Artificial Intelligence in Medicine, vol. 129, p. 102314, 2022.

[221] E. J. Harris, I.-H. Khoo, and E. Demircan, “A survey of human gait-based artificial intelligence applications,” Frontiers in Robotics and AI, vol. 8, p. 749274, 2022.

[222] M. L. Shuwandy, A. Jouda, M. Ahmed, M. M. Salih, Z. Al-qaysi, A. Alamoodi et al., “Sensor-Based Authentication in Smartphone; a Systematic Review,” Journal of Engineering Research, 2024.

[223] S. Park and G. Ryu, “Motion-Based User Authentication for Enhanced Metaverse Security,” Journal of the Korea Institute of Information Security & Cryptology, vol. 34, pp. 493–503, 2024.

[224] A. Z. Zaidi, C. Y. Chong, Z. Jin, R. Parthiban, and A. S. Sadiq, “Touch-based continuous mobile device authentication: State-of-the-art, challenges and opportunities,” Journal of Network and Computer Applications, vol. 191, p. 103162, 2021.

[225] G. Cho, S. Kwag, J. H. Huh, B. Kim, C.-H. Lee, and H. Kim, “Towards usable and secure location-based smartphone authentication,” in Seventeenth Symposium on Usable Privacy and Security (SOUPS 2021), 2021, pp. 1–16.

[226] A. Alabdulatif, R. Samarasinghe, and N. N. Thilakarathne, “A Novel Robust Geolocation-Based Multi-Factor Authentication Method for Securing ATM Payment Transactions,” Applied Sciences, vol. 13, p. 10743, 2023.

[227] I. Ullah, D. Adhikari, H. Khan, M. S. Anwar, S. Ahmad, and X. Bai, “Mobile robot localization: Current challenges and future prospective,” Computer Science Review, vol. 53, p. 100651, 2024.

[228] A. G. Mart´ın, M. Beltr´an, A. Fern´andez-Isabel, and I. M. de Diego, “An approach to detect user behaviour anomalies within identity federations,” computers & security, vol. 108, p. 102356, 2021.

[229] O. Olukoya, “Assessing frameworks for eliciting privacy & security requirements from laws and regulations,” Computers & Security, vol. 117, p. 102697, 2022.

[230] G. Reshma, B. Prasanna, H. N. Murthy, T. Murthy, S. Parthiban, and M. Sangeetha, “Privacy-aware access control (PAAC)-based biometric authentication protocol (Bap) for mobile edge computing environment,” Soft Computing, pp. 1–20, 2023.

[231] P. Khanpara, K. Lavingia, R. Trivedi, S. Tanwar, A. Verma, and R. Sharma, “A context-aware internet of things-driven security scheme for smart homes,” Security and Privacy, vol. 6, p. e269, 2023.

[232] A. Al Abdulwahid, N. Clarke, I. Stengel, S. Furnell, and C. Reich, “Continuous and transparent multimodal authentication: reviewing the state of the art,” Cluster Computing, vol. 19, pp. 455–474, 2016.

[233] S. W. Shah, N. F. Syed, A. Shaghaghi, A. Anwar, Z. Baig, and R. Doss, “LCDA: lightweight continuous device-to-device authentication for a zero trust architecture (ZTA),” Computers & Security, vol. 108, p. 102351, 2021.

[234] S. G. Persiani, B. Kobas, S. C. Koth, and T. Auer, “Biometric data as real-time measure of physiological reactions to environmental stimuli in the built environment,” Energies, vol. 14, p. 232, 2021.

[235] M. K. Hasan, A. A. Habib, Z. Shukur, F. Ibrahim, S. Islam, and M. A. Razzaque, “Review on cyber-physical and cyber-security system in smart grid: Standards, protocols, constraints, and recommendations,” Journal of network and computer applications, vol. 209, p. 103540, 2023.

[236] S. M. Amft, “On the usability of authentication security communica-tion,” 2024.

[237] S. Gupta, C. Maple, B. Crispo, K. Raja, A. Yautsiukhin, and F. Martinelli, “A survey of human-computer interaction (HCI) & natural habits-based behavioural biometric modalities for user recognition schemes,” Pattern Recognition, vol. 139, p. 109453, 2023.

[238] G. Zhao, Y. Jiao, J. Zhuo, Y. Chen, C. Ju, and Y. Wang, “Continuous Authentication of Smartphones Based on Screen-Touch Trajectories,” Journal of Networking and Network Applications, vol. 4, pp. 102–108, 2024.

[239] B. Ying, N. R. Mohsen, and A. Nayak, “Efficient authentication protocol for continuous monitoring in medical sensor networks,” IEEE Open Journal of the Computer Society, vol. 2, pp. 130–138, 2021.

[240] P. Agbaje, A. Anjum, A. Mitra, E. Oseghale, G. Bloom, and H. Olufowobi, “Survey of interoperability challenges in the internet of vehicles,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, pp. 22838–22861, 2022.

[241] A. Ali, “Securing IoT connectivity: The role of Multi-Factor Authen-tication (MFA) in strengthening Cyber defense,” ed, 2022.

[242] I. Gharbi, F. Taia-Alaoui, H. Fourati, N. Vuillerme, and Z. Zhou, “Transportation Mode Detection Using Learning Methods and Self-Contained Sensors,” Sensors, vol. 24, p. 7369, 2024.

[243] A. A. Bangash, “Cost-effective Strategies to Develop Energy-Efficient Mobile Applications,” 2023.

[244] S. F. Ahmed, M. S. B. Alam, S. Afrin, S. J. Rafa, N. Rafa, and A. H. Gandomi, “Insights into Internet of Medical Things (IoMT): Data fusion, security issues and potential solutions,” Information Fusion, vol. 102, p. 102060, 2024.

[245] R. Zahid, A. Altaf, T. Ahmad, F. Iqbal, Y. A. M. Vera, M. A. L. Flores et al., “Secure data management life cycle for government big-data ecosystem: Design and development perspective,” Systems, vol. 11, p. 380, 2023.

[246] S. Aftabjahani, R. Kastner, M. Tehranipoor, F. Farahmandi, J. Oberg, A. Nordstrom et al., “Special session: Cad for hardware security-automation is key to adoption of solutions,” in 2021 IEEE 39th VLSI Test Symposium (VTS), 2021, pp. 1–10.

[247] P. M. A. B. Estrela, R. d. O. Albuquerque, D. M. Amaral, W. F. Giozza, and R. T. d. S. J´unior, “A framework for continuous authentication based on touch dynamics biometrics for mobile banking applications,” Sensors, vol. 21, p. 4212, 2021.

[248] P. Melzi, C. Rathgeb, R. Tolosana, R. Vera-Rodriguez, and C. Busch, “An overview of privacy-enhancing technologies in biometric recogni-tion,” ACM Computing Surveys, vol. 56, pp. 1–28, 2024.

[249] S. Fleury and N. Chaniaud, “Multi-user centered design: acceptance, user experience, user research and user testing,” Theoretical Issues in Ergonomics Science, vol. 25, pp. 209–224, 2024.