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A Perceptron Mixture Model of Intrusion Detection for Safeguarding Electronic Health Record System

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Advances in Networked-Based Information Systems (NBiS 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 313))

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Abstract

Electronic Health Record System, EHRS, has recently become a common healthcare information technology that has been widely adopted by many physicians. Due to compromised internal structures and systems, safeguarding the privacy and security of EHRS becomes a very challenging issue based on the most recent studies on trends and characteristics of protected health information breaches in the United States. Traditionally intrusion detection systems were proposed to address security of EHRS infrastructure by detecting unauthorized accesses; they, however, tend to generate a large number of false alerts mainly due to lack of proper features to model normal behaviors and overfitting when using signature-based detection algorithms. In this paper we address this limitation and propose a mixture model combining both misuse detection and anomaly detection approaches to minimize the number of false alerts over a specific time period in real time through its self-learning-fixing-and-improving capability built upon the perceptron algorithm.

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Acknowledgments

This research was supported in part by funding from a Keene State College Faculty Development Grant.

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Authors and Affiliations

  1. Department of Computer Science, Keene State College, USNH, Keene, NH, USA

    Wei Lu

  2. College of Health and Human Services, University of New Hampshire, Durham, NH, USA

    Ling Xue

Authors
  1. Wei Lu
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  2. Ling Xue
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Corresponding author

Correspondence to Wei Lu .

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Editors and Affiliations

  1. Department of Information and Communication Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan

    Hsing-Chung Chen

  3. Faculty of Business Administration, Rissho University, Tokyo, Japan

    Tomoya Enokido

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Lu, W., Xue, L. (2022). A Perceptron Mixture Model of Intrusion Detection for Safeguarding Electronic Health Record System. In: Barolli, L., Chen, HC., Enokido, T. (eds) Advances in Networked-Based Information Systems. NBiS 2021. Lecture Notes in Networks and Systems, vol 313. Springer, Cham. https://doi.org/10.1007/978-3-030-84913-9_18

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