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arXiv 1502.01240cs.CR
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使用攻击图的网络安全分析预测框架

A Predictive Framework for Cyber Security Analytics using Attack Graphs

Subil Abraham, Suku Nair

摘要:

安全度量是组织了解保护计算机网络的有效性的有力工具。然而,大多数这些测量技术不足以帮助企业做出明智的风险管理决策。在本文中,我们提出了一个随机的安全框架,通过考虑到动态属性与漏洞,可以随着时间的推移而变化,以获得定量的安全措施。我们的模型是新颖的,因为现有的攻击图分析研究没有考虑与漏洞相关的时间方面,例如漏洞和补丁的可用性,这些漏洞和补丁可以根据漏洞如何互连和利用来危害系统来影响整体网络安全。为了有一个更现实的表示网络的安全状态将如何随着时间的推移而变化,开发了一个非齐次模型,其中包括一个依赖于时间的协变量,即脆弱性年龄。每日转移概率矩阵估计使用弗雷的脆弱性模型。我们还利用可信的CVSS度量域来分析给定网络的总可利用性和影响度量在一段时间内如何演变。

英文摘要:

Security metrics serve as a powerful tool for organizations to understand the effectiveness of protecting computer networks. However majority of these measurement techniques don't adequately help corporations to make informed risk management decisions. In this paper we present a stochastic security framework for obtaining quantitative measures of security by taking into account the dynamic attributes associated with vulnerabilities that can change over time. Our model is novel as existing research in attack graph analysis do not consider the temporal aspects associated with the vulnerabilities, such as the availability of exploits and patches which can affect the overall network security based on how the vulnerabilities are interconnected and leveraged to compromise the system. In order to have a more realistic representation of how the security state of the network would vary over time, a nonhomogeneous model is developed which incorporates a time dependent covariate, namely the vulnerability age. The daily transition-probability matrices are estimated using Frei's Vulnerability Lifecycle model. We also leverage the trusted CVSS metric domain to analyze how the total exploitability and impact measures evolve over a time period for a given network.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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