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Electronic Health Record Security Based on Ensemble Classification of Keystroke Dynamics.

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Title: Electronic Health Record Security Based on Ensemble Classification of Keystroke Dynamics.
Authors: Wesołowski, Tomasz Emanuel1 (AUTHOR) rafal.doroz@us.edu.pl, Porwik, Piotr1 (AUTHOR), Doroz, Rafal1 (AUTHOR)
Source: Applied Artificial Intelligence. 2016, Vol. 30 Issue 6, p521-540. 20p.
Subjects: Electronic health records, Keystroke timing authentication, Computer systems, Real-time computing, Machine learning, Security systems
Abstract: One the most important security issues is unauthorized access to Electronic Health Records (EHR). The number of leaked EHRs is drastically growing year by year. Medical records are processed and stored in systems with keyboard-based interfaces. Using these interfaces, intruders can break into a system and gain unauthorized access to protected data. We propose a novel solution based on a computer user profiling that prevents such an intrusion. In this approach, a computer user's activity is constantly analyzed by performing real-time keyboard monitoring. This allows secure control of access to the computer systems dealing with EHRs. Introduced security systems are especially needed in medical environments where sensitive data are processed. To achieve the high performance of an intrusion detection system, we have constructed an ensemble of classifiers supported by machine learning methods. The obtained results show that the proposed method can be used in intrusion detection and monitoring systems. [ABSTRACT FROM AUTHOR]
Copyright of Applied Artificial Intelligence is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Electronic Health Record Security Based on Ensemble Classification of Keystroke Dynamics.
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  Data: <searchLink fieldCode="JN" term="%22Applied+Artificial+Intelligence%22">Applied Artificial Intelligence</searchLink>. 2016, Vol. 30 Issue 6, p521-540. 20p.
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  Data: <searchLink fieldCode="DE" term="%22Electronic+health+records%22">Electronic health records</searchLink><br /><searchLink fieldCode="DE" term="%22Keystroke+timing+authentication%22">Keystroke timing authentication</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+systems%22">Computer systems</searchLink><br /><searchLink fieldCode="DE" term="%22Real-time+computing%22">Real-time computing</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Security+systems%22">Security systems</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: One the most important security issues is unauthorized access to Electronic Health Records (EHR). The number of leaked EHRs is drastically growing year by year. Medical records are processed and stored in systems with keyboard-based interfaces. Using these interfaces, intruders can break into a system and gain unauthorized access to protected data. We propose a novel solution based on a computer user profiling that prevents such an intrusion. In this approach, a computer user's activity is constantly analyzed by performing real-time keyboard monitoring. This allows secure control of access to the computer systems dealing with EHRs. Introduced security systems are especially needed in medical environments where sensitive data are processed. To achieve the high performance of an intrusion detection system, we have constructed an ensemble of classifiers supported by machine learning methods. The obtained results show that the proposed method can be used in intrusion detection and monitoring systems. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Applied Artificial Intelligence is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1080/08839514.2016.1193715
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      – Code: eng
        Text: English
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        PageCount: 20
        StartPage: 521
    Subjects:
      – SubjectFull: Electronic health records
        Type: general
      – SubjectFull: Keystroke timing authentication
        Type: general
      – SubjectFull: Computer systems
        Type: general
      – SubjectFull: Real-time computing
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Security systems
        Type: general
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      – TitleFull: Electronic Health Record Security Based on Ensemble Classification of Keystroke Dynamics.
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            NameFull: Porwik, Piotr
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            NameFull: Doroz, Rafal
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            – D: 01
              M: 07
              Text: 2016
              Type: published
              Y: 2016
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              Value: 30
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            – TitleFull: Applied Artificial Intelligence
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