利用贝叶斯网络对纵深防御措施进行优先级排序和组合以减少威胁

R. Alexander
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引用次数: 3

摘要

本文研究的是贝叶斯网络模型(BNM)是否可以有效地应用于国防深度安全工具和程序的优先级排序,以及将这些措施结合起来以减少网络威胁。本研究中使用的方法包括使用Likert量表模型扫描来自知名网络安全期刊的24篇同行评审的网络安全文章,以获取文章的深度防御措施(工具和程序)列表以及这些措施旨在减少的威胁。然后对深度防御工具和程序进行比较,以确定是否可以有效地应用Likert量表和贝叶斯网络模型来确定措施的优先级并将其组合起来,以减少针对组织和私人计算系统的网络威胁攻击。研究结果否定了H0零假设,即BNM不会影响24篇网络安全文章的深度防御工具和程序(自变量)与网络威胁(因变量)的优先顺序和组合之间的关系。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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).
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