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. |
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| 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] |
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| Database: | Engineering Source |
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| 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] |
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| ISSN: | 08839514 |
| DOI: | 10.1080/08839514.2016.1193715 |
