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

Abstract

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 H 0 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).