Using Linear Regression Analysis and Defense in Depth to Protect Networks during the Global Corona Pandemic

R. Alexander
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Abstract

The purpose of this research was to determine whether the Linear Regression Analysis can be effectively applied to the prioritization of defense-in-depth security tools and procedures to reduce cyber threats during the Global Corona Virus Pandemic. The way this was determined or methods used in this study consisted of scanning 20 peer reviewed Cybersecurity Articles from prominent Cybersecurity Journals for a list of defense in depth measures (tools and procedures) and the threats that those measures were designed to reduce. The methods further involved using the Likert Scale Model to create an ordinal ranking of the measures and threats. The defense in depth tools and procedures were then compared to see whether the Likert scale and Linear Regression Analysis could be effectively applied to prioritize and combine the measures to reduce pandemic related cyber threats. The results of this research reject the H0 null hypothesis that Linear Regression Analysis does not affect the relationship between the prioritization and combining of defense in depth tools and procedures (independent variables) and pandemic related cyber threats (dependent variables).
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利用线性回归分析和深度防御在全球冠状病毒大流行期间保护网络
本研究的目的是确定线性回归分析是否可以有效地应用于深度防御安全工具和程序的优先级,以减少全球冠状病毒大流行期间的网络威胁。确定方法或本研究中使用的方法包括扫描来自著名网络安全期刊的20篇同行评审的网络安全文章,以获取深度防御措施(工具和程序)列表以及这些措施旨在减少的威胁。这些方法进一步涉及使用李克特量表模型来创建措施和威胁的有序排序。然后比较深度防御工具和程序,看看是否可以有效地应用李克特量表和线性回归分析来确定优先级和组合措施,以减少与大流行相关的网络威胁。本研究的结果拒绝了H0零假设,即线性回归分析不影响纵深防御工具和程序(自变量)的优先级和组合与大流行相关的网络威胁(因变量)之间的关系。
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