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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM66898

ROLE-AWARE ANOMALY DETECTION FOR INSIDER THREAT IDENTIFICATION IN HEALTHCARE EHR SYSTEMS

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM66898

Volume:

10 |

8 |

Aug 2026

Pages:

1-8

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM66898

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

TR

THASLEEM R

COMPUTER SCIENCE

QUAID-E-MILLATH GOVERNMENT COLLEGE FOR WOMEN

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DSK

DR SUMATHY KINGSLIN

COMPUTER SCIENCE

QUAID-E-MILLATH GOVERNMENT COLLEGE FOR WOMEN

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Abstract

Insider threat detection in healthcare EHR systems is challenging due to diverse user roles and complex access workflows. Conventional anomaly detection approaches often treat all users uniformly, resulting in high false alarm rates when legitimate role-specific behavior is misclassified as suspicious. This paper presents a role-aware anomaly detection approach for insider threat identification in healthcare environments. User activity logs are enriched with role-based contextual features that model expected access behavior for different healthcare roles, such as physicians, nurses, and administrative staff. Anomaly detection models are applied to identify deviations from role-consistent behavior patterns. Experimental evaluation using benchmark and simulated healthcare log data demonstrates that incorporating role awareness improves detection accuracy and significantly reduces false positives compared to role-agnostic approaches. The results highlight the importance of role context in developing reliable and practical insider threat detection systems for healthcare EHR environments.

KEYWORDS

Insider Threat Detection Healthcare Cybersecurity Role-Aware Analysis Anomaly Detection Electronic Health Records

Cite This Article

THASLEEM R, DR SUMATHY KINGSLIN, "ROLE-AWARE ANOMALY DETECTION FOR INSIDER THREAT IDENTIFICATION IN HEALTHCARE EHR SYSTEMS," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 8, pp. 1-8, Aug 2026. https://doi.org/10.55041/IJSREM66898

Full Article/Conclusion

Overall, the results confirm that role-aware anomaly detection provides a practical and effective improvement over role-agnostic approaches for insider threat detection in healthcare EHR systems. The proposed method enhances detection reliability while maintaining a lightweight and scalable implementation, making it suitable for real-world healthcare deployment.

References

[1] A. Cappelli, A. Moore, and R. Trzeciak, The CERT Guide to Insider Threats: How to Prevent, Detect, and Respond to Information Technology Crimes, Addison-Wesley, 2012. [2] M. Bishop, C. Gates, S. Engle, and S. Whalen, “Insider threat detection: A framework,” Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, vol. 1, no. 1, pp. 1–14, 2010. [3] C. Liu, J. Wang, and J. Zhao, “Insider threat detection using machine learning: A survey,” IEEE Access, vol. 8, pp. 152914–152939, 2020. [4] Y. Yuan, Y. Li, and Z. Tian, “Deep learning for insider threat detection: A review,” IEEE Transactions on Information Forensics and Security, vol. 16, pp. 2102–2118, 2021. [5] A. Tuor, S. Kaplan, B. Hutchinson, N. Nichols, and S. Robinson, “Deep learning for unsupervised insider threat detection in structured cybersecurity data streams,” in Proc. AI for Cybersecurity Workshop, 2017. [6] U.S. Department of Health and Human Services, “Security and privacy of electronic health records,” Health IT Security Guidelines, 2022. [7] S. Alneyadi, E. Sithirasenan, and V. Muthukkumarasamy, “A survey on insider threat detection using machine learning,” IEEE Communications Surveys & Tutorials, vol. 20, no. 1, pp. 317–353, 2018. [8] M. E. Johnson, “Data hemorrhages in the healthcare sector,” in Financial Cryptography and Data Security, Springer, pp. 71–89, 2009. [9] B. Scholkopf et al., “Estimating the support of a high-dimensional distribution,” Neural Computation, vol. 13, no. 7, pp. 1443–1471, 2001. (One-Class SVM reference) [10] F. T. Liu, K. M. Ting, and Z.-H. Zhou, “Isolation Forest,” in Proc. IEEE ICDM, pp. 413–422, 2008.

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Publication Date

8/22/2026

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Review Rounds: 2

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