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

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).

Using the Analytical Hierarchy Process Model in the Prioritization of Information Assurance Defense In-Depth Measures?—A Quantitative Study

Organizational computing devices are increasingly becoming targets of cyber-attacks, and organizations have become dependent on the safety and security of their computer networks and their organizational computing devices. Business and government often use defense in-depth information assurance measures such as firewalls, intrusion detection systems, and password procedures across their enterprises to plan strategically and manage IT security risks. This quantitative study explores whether the analytical hierarchy process (AHP) model can be effectively applied to the prioritization of information assurance defense in-depth measures. In response to these threats, the President, legislators, experts, and others have characterized cyber security as a pressing national security issue. The methods used in this study consisted of emailing study participants a survey requesting that they prioritize five defense in-depth information assurance measures, anti-virus, intrusion detection, password, smart-cards, and encryption, with a range of responses from 1 - 5 using a Likert scale to consider standard cost, effectiveness, and perceived ease of use in terms of protection of organizational computing devices. The measures were then weighted, based on ranking. A pair-wise comparison of each of the five measures is then made using AHP to determine whether the Likert scale and the AHP model could be effectively applied to the prioritization of information assurance measures to protect organizational computing devices. The findings of the research reject the H0 null hypothesis that AHP does not affect the relationship between the information technology analysts’ prioritization of five defense in-depth dependent variables and the independent variables of cost, ease of use, and effectiveness in protecting organizational devices against cyber-attacks.

Using the Latin Square Design Model in the Prioritzation of Network Security Threats: A Quantitative Study

Society is becoming increasingly dependent on cyberspace for both business and pleasure. Cyber attackers continue to attack organizational computer networks, as those same computer networks become increasing critical to organizational business process. Strategic planning and managing IT security risks play an important role in the business and government planning process. Deploying defense in depth security measures can ensure that organizations continue to function in times of crisis. This quantitative study explores whether the Latin Square Design (LSD) model can be effectively applied to the prioritization of cybersecurity threats and to the linking of information assurance defense in-depth measures to those threats. The methods used in this study consisted of scanning 10 Cybersecurity Websites such as the Department of Homeland Security US CERT (United States-Computer Emergency Readiness Team [1]) and the SANS Institute (SysAdmin, Audit, Network and Security [2]) using the Likert Scale Model for the Website’s top ten list of cyber threats facing organizations and the network defense in depth measures to fight those threats. A comparison of each cybersecurity threats was then made using LSD to determine whether the Likert scale and the LSD model could be effectively applied to prioritize information assurance measures to protect organizational computing devices. The findings of the research reject the H0 null hypothesis that LSD does not affect the relationship between the ranking of 10 Cybersecurity websites top ten cybersecurity threats dependent variables and the independent variables of defense in depth measures used in protecting organizational devices against cyber-attacks.

Reducing Threats by Using Bayesian Networks to Prioritize and Combine Defense in Depth Security Measures

Studied in this article is whether the Bayesian Network Model (BNM) can be effectively applied to the prioritization of defense in-depth security tools and procedures and to the combining of those measures to reduce cyber threats. The methods used in this study consisted of scanning 24 peer reviewed Cybersecurity Articles from prominent Cybersecurity Journals using the Likert Scale Model for the article’s list of defense in depth measures (tools and procedures) and the threats that those measures were designed to reduce. The defense in depth tools and procedures are then compared to see whether the Likert scale and the Bayesian Network Model could be effectively applied to prioritize and combine the measures to reduce cyber threats attacks against organizational and private computing systems. The findings of the research reject the H0 null hypothesis that BNM does not affect the relationship between the prioritization and combining of 24 Cybersecurity Article’s defense in depth tools and procedures (independent variables) and cyber threats (dependent variables).