Behavioral Biometrics for Healthcare Cybersecurity

Maurice L. McBride, Kenna L. Young

Abstract


With patient records going online, healthcare organizations are facing new-age threats and old-world security solutions. Typing, mouse, and voice dynamics are continuous and non-intrusive user authentication, enhancing security in clinical systems. This paper discusses the application of behavioral biometrics in mitigating insider threats, compromised credentials, and unauthorized access in the healthcare sector. The paper utilizes peer-reviewed articles published between 2020 and 2025 and applies thematic analysis to identify patterns, advantages, and disadvantages. Studies reveal that behavioral biometrics enhances identification, reduces system vulnerabilities, and can be easily integrated into existing systems. However, issues remain regarding algorithm bias, user privacy, and the system's sensitivity. The article concludes that while behavioral biometrics cannot be an effective substitute for conventional security measures, they are a valuable addition to IT security in the rapidly growing field of digital healthcare and can help healthcare facilities meet regulatory requirements.


Keywords


Behavioral Biometrics; Healthcare Cybersecurity; User Authentication; Insider Threats; Data Privacy

Full Text:

PDF

References


Alder, S. (2024). Security Breaches in Healthcare in 2023. The HIPAA Journal, Michigan, United States. Available online: https://www. hipaajournal. com/security-breaches-in-healthcare/(accessed on December 2024).

Alsowail, R. A., & Al-Shehari, T. (2022). Techniques and Countermeasures for Preventing Insider Threats. PeerJ Computer Science, 8. https://doi.org/10.7717/peerj-cs.938

Bajwa, J., Munir, U., Nori, A., & Williams, B. (2021). Artificial Intelligence in Healthcare: Transforming the Practice of Medicine. Future Healthcare Journal, 8(2), 188–194. NCBI. https://doi.org/10.7861/fhj.2021-0095

Balamurugan, M. (2024). Biometric Authentication: A Double-Edged Sword for Security? International Journal of Science and Research (IJSR), 13(9), 170–173. https://doi.org/10.21275/sr24901230354

Basil, N., Ambe, S., Ekhator, C., & Fonkem, E. (2024). Health Records Database and Inherent Security Concerns: A Review of the Literature. Nih.gov. https://pmc.ncbi.nlm.nih.gov/articles/PMC9647912/

Clarke, M., & Martin, K. (2023). Managing cybersecurity risk in healthcare settings. Healthcare Management Forum, Vol. 37, No. 1. https://doi.org/10.1177/08404704231195804

Edemekong, P. F., Haydel, M. J., & Annamaraju, P. (2024). Health Insurance Portability and Accountability Act (HIPAA). National Library of Medicine. https://www.ncbi.nlm.nih.gov/books/NBK500019/

Ghilom, M., & Latifi, S. (2024). The Role of Machine Learning in Advanced Biometric Systems. Electronics, 13(13), 2667.. https://doi.org/10.3390/electronics13132667

Finnegan, O. L., White, J. W., Armstrong, B., Adams, E. L., Burkart, S., Beets, M. W., S. Nelakuditi, Willis, E. A., L. von Klinggraeff, Parker, H., Bastyr, M., Zhu, X., Zhong, Z., & Weaver, R. G. (2024). The utility of behavioral biometrics in user authentication and demographic characteristic detection: a scoping review. Systematic Reviews, vol. 13, no. 1. https://doi.org/10.1186/s13643-024-02451-1

Inayat, U., Farzan, M., Mahmood, S., Zia, M. F., Hussain, S., & Pallonetto, F. (2024). Insider threat mitigation: Systematic literature review. Ain Shams Engineering Journal, 103068. https://doi.org/10.1016/j.asej.2024.103068

Kumarapeli, D., Jung, S., & Lindeman, R. W. (2024). Privacy threats of behaviour identity detection in VR. Frontiers in Virtual Reality, 5, 1197547. https://doi.org/10.3389/frvir.2024.1197547

Ko, H. Y. K., Tripathi, N. K., Mozumder, C., Muengtaweepongsa, S., & Pal, I. (2023). Real-Time Remote Patient Monitoring and Alarming System for Noncommunicable Lifestyle Diseases. International Journal of Telemedicine and Applications, 2023, e9965226. https://doi.org/10.1155/2023/9965226

Liang, W., & Hamzah, F. (2025). Behavioral Biometrics and AI for Cloud User Authentication. https://www.researchgate.net/publication/389717394_Behavioral_Biometrics_and_AI_for_Cloud_User_Authentication/download

Muthuppalaniappan, M., & Stevenson, K. (2020). Healthcare Cyber-Attacks and the COVID-19 Pandemic: An Urgent Threat to Global Health. International Journal for Quality in Health Care, vol. 33, no. 1. https://doi.org/10.1093/intqhc/mzaa117

Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., & McGuinness, L. A. (2021). The PRISMA 2020 statement: An updated Guideline for Reporting Systematic Reviews. British Medical Journal, 372(71). https://doi.org/10.1136/bmj.n71

Progonov, D., Cherniakova, V., Kolesnichenko, P., & Oliynyk, A. (2022). Behavior-based user authentication on mobile devices in various usage contexts. EURASIP Journal on Information Security, 2022, 1. https://doi.org/10.1186/s13635-022-00132-x

Rukhiran, M., Wong-In, S., & Netinant, P. (2023). User Acceptance Factors Related to Biometric Recognition Technologies of Examination Attendance in Higher Education: TAM Model. Sustainability, 15(4), 3092. https://doi.org/10.3390/su15043092

Sarkar, G., & Shukla, S. K. (2023). Behavioral Analysis of Cybercrime: Paving the Way for Effective Policing Strategies. Journal of Economic Criminology, 2(1), 100034. https://doi.org/10.1016/j.jeconc.2023.100034

Shojaei, P., Gjorgievska, E. V., & Chow, Y.-W. (2024). Security and Privacy of Technologies in Health Information Systems: A Systematic Review of the Literature. Computers, 13(2). https://doi.org/10.3390/computers13020041

Subash, A., & Song, I. (2021, November 1). Real-Time Behavioral Biometric Information Security System for Assessment Fraud Detection. IEEE Xplore. https://doi.org/10.1109/ICOCO53166.2021.9673568

Suleski, T., Ahmed, M., Yang, W., & Wang, E. (2023). A Review of Multi-Factor Authentication in the Internet of Healthcare Things. Digital Health, 9(1), 1–20. https://doi.org/10.1177/20552076231177144

Van Giffen, B., Herhausen, D., & Fahse, T. (2022). Overcoming the pitfalls and perils of algorithms: A classification of machine learning biases and mitigation methods. Journal of Business Research, 144(6), 93-106. https://doi.org/10.1016/j.jbusres.2022.01.076

Zhang, J., Liu, Z., & Luo, X. (Robert). (2024). Unraveling the Juxtaposed Effects of Biometric Characteristics on User Security Behaviors: A Controversial Information Technology Perspective. Decision Support Systems, 183, 114267. https://doi.org/10.1016/j.dss.2024.114267

Zhang, Z., Yin, H., Rao, S. X., Yan, X., Wang, Z., Liang, W., Zhao, Y., Shan, Y., Zhang, R., Lin, Y., & Jiang, J. (2025). Identifying E-Commerce Fraud Through User Behavior Data: Observations and Insights. Data Science and Engineering. https://doi.org/10.1007/s41019-024-00275-6




DOI: https://doi.org/10.53889/citj.v3i1.617

Article Metrics

Abstract view : 78 times
PDF - 37 times

Refbacks

  • There are currently no refbacks.


Copyright (c) 2025 Cybersecurity and Innovative Technology Journal

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.