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

机译:使用线性回归分析和防御深度保护网络在全球电晕大流行期间保护网络

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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).
机译: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防御深度安全工具和程序的优先级,以减少全球电晕病毒流行病中的网络威胁。确定的方式或本研究中使用的方法包括扫描20个同行,从突出的网络安全期刊中审查了一篇关于深度措施(工具和程序)和这些措施旨在减少措施的威胁的突出网络安全期刊的网络安全。进一步涉及使用Likert Scale模型的方法,以创建衡量标准和威胁的序数排名。然后比较深度工具和程序的防御,看看是否可以有效地应用于优先考虑并结合减少大流行相关网络威胁的措施来有效地应用李克特规模和线性回归分析。该研究的结果拒绝了H0 NULL假设,即线性回归分析不会影响深度工具和程序(独立变量)和流行相关网络威胁(依赖变量)之间防御的优先级和防御之间的关系。

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