ArticleInternational Conference on Network-Based Information Systems : [proceedings]. NBiS (Conference)2022
A Perceptron Mixture Model of Intrusion Detection for Safeguarding Electronic Health Record System.
Article in International Conference on Network-Based Information Systems : [proceedings]. NBiS (Conference), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
0 citing papers in PubMed, 4 citations in OpenAlex.
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Corrections and comments
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
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.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.