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Chen, P., Desmet, L. and Huygens, C. (2014) A Study on Advanced Persistent Threats. In: De Decker, B. and Zúquete, A., Eds., Communications and Multimedia Security, CMS 2014, Lecture Notes in Computer Science, Springer, Berlin, 63-72.

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

  AUTHORS: Rodney Alexander

  KEYWORDS: Information Assurance, Defense in Depth, Information Technology, Network Security, Cybersecurity, Linear Regression Analysis, Pandemic

  JOURNAL NAME: Journal of Information Security, Vol.11 No.4, October 21, 2020

  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).