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

In this article 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 has been studied. The strategy recommends a balance between
the protection capability and cost, performance, and operational considerations. 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 were 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).