Threats Identification in Healthcare Information Systems Using Genetic Algorithm and Cox Regression

R. Ahmad, Ganthan Narayana Samy, Nuzulha Khilwani Ibrahim, P. Bath, Z. Ismail
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引用次数: 9

Abstract

Threats to information security for healthcare information system increased tremendously. There are various factors contribute to information security threats, many researchers focused only to certain factors which interest them (e.g., virus attack). Certain factors which may be important remain unexplored. In addition, lack of tools and technologies directed to limited number of threats traced in healthcare system. Thus it introduces bias in threat analysis. This study explored the use of biological computational termed Genetic Algorithm (GAs) combined with Cox regression (CoRGA) in identifying a potential threat for healthcare system. The results show that variable described “misused of e-mail” is the major information security threats for healthcare system. Results were compared with manual analysis using the same data, and it is shows that GAs not just introducing new threats for healthcare system but it was similar with others threats proposed by previous researches.
基于遗传算法和Cox回归的医疗信息系统威胁识别
医疗信息系统面临的信息安全威胁急剧增加。造成信息安全威胁的因素多种多样,许多研究人员只关注他们感兴趣的某些因素(如病毒攻击)。某些可能很重要的因素仍未被探索。此外,缺乏针对医疗保健系统中有限数量的威胁的工具和技术。因此,它在威胁分析中引入了偏见。本研究探讨了使用生物计算称为遗传算法(GAs)结合考克斯回归(CoRGA)在识别医疗保健系统的潜在威胁。结果表明,变量“电子邮件滥用”是医疗保健系统面临的主要信息安全威胁。结果与使用相同数据的人工分析结果进行了比较,表明GAs不仅为医疗保健系统引入了新的威胁,而且与以往研究提出的其他威胁相似。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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