基于漏洞检测工具安全度量的网络风险评估

Aniwat Hemanidhi, S. Chimmanee, P. Sanguansat
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引用次数: 3

摘要

网络安全一直是任何组织关心的主要问题。为了确保组织网络能够很好地防范攻击者,我们会定期实施漏洞评估和渗透测试。然而,根据管理员的专业知识,审计和分析这些测试结果是一个非常耗时的过程。因此,安全专业人员更喜欢主动的自动漏洞检测工具,以便在攻击者利用漏洞之前识别漏洞。尽管这些漏洞检测工具表明它们对于安全专业人员来说非常有用,可以更快、更准确地进行审计和分析,但它们也有一些重要的弱点。它们只能识别表面漏洞,无法解决扫描网络的整体风险水平。此外,他们经常使用不同的网络风险等级分类标准,这些标准通常与一些组织或供应商有关。因此,这些漏洞检测工具可能或多或少地对风险评估进行了偏见分类。本文提出了“网络风险度量”的一般概念,将其作为几种漏洞检测工具的无偏风险评估。在本文中,NetClarity(基于硬件)、Nessus(基于软件)和Retina(基于软件)在来自泰国皇家陆军(RTA) IT部门的两个网络上实现。将提出的度量用于评估这三种漏洞检测工具的整体网络风险。结果是对每个网络进行更准确的风险评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Network risk evaluation from security metric of vulnerability detection tools
Network Security is always a major concern in any organizations. To ensure that the organization network is well prevented from attackers, vulnerability assessment and penetration testing are implemented regularly. However, it is a highly time-consuming procedure to audit and analysis these testing results depending on administrator's expertise. Thus, security professionals prefer proactive-automatic vulnerability detection tools to identify vulnerabilities before they are exploited by an adversary. Although these vulnerability detection tools show that they are very useful for security professionals to audit and analysis much faster and more accurate, they have some important weaknesses as well. They only identify surface vulnerabilities and are unable to address the overall risk level of the scanned network. Also, they often use different standard for network risk level classification which habitually related to some organizations or vendors. Thus, these vulnerability detection tools are likely to, more or less, classify risk evaluation biasedly. This article presents a generic idea of “Network Risk Metric” as an unbiased risk evaluation from several vulnerability detection tools. In this paper, NetClarity (hardware-based), Nessus (software-based), and Retina (software-based) are implemented on two networks from an IT department of the Royal Thai Army (RTA). The proposed metric is applied for evaluating overall network risk from these three vulnerability detection tools. The result is a more accurate risk evaluation for each network.
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