Electronic Health Record Security Based on Ensemble Classification of Keystroke Dynamics. Issue 6 (2nd July 2016)

• Record Type: Journal Article
• Authors: Wesołowski, Tomasz Emanuel; Porwik, Piotr; Doroz, Rafal
• Is Part Of: Applied artificial intelligence. Volume 30:Issue 6(2016)
• Page Start: 521
• Page End: 540
• Publication Date: 2016-07-02
• Subjects: Artificial intelligence -- Periodicals; 006.3
• DOI: 10.1080/08839514.2016.1193715
• Languages: English
• ISSNs: 0883-9514

Abstract

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.