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Multivariate models using MCMCBayes for web-browser vulnerability discovery

机译：使用MCMCBayes的多模型用于Web浏览器漏洞发现

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摘要

Vulnerabilities that enable well-known exploit techniques are preventable, but their public discovery continues in software. Vulnerability discovery modeling (VDM) techniques were proposed to assist managers with decisions, but do not include influential variables describing the software release (SR) (e.g., code size and complexity characteristics) and security assessment profile (SAP) (e.g., security team size or skill). Consequently, they have been limited to modeling discoveries over time for SR and SAP scenarios of unique products, whose results are not readily comparable without making assumptions that equate all SR and SAP combinations under study. This article introduces a groundbreaking capability that allows forecasting expected discoveries over time for arbitrary SR and SAP combinations, thus enabling managers to better understand the effects of influential variables they control on the phenomenon. To do this, we use variables that describe arbitrary SR and SAP combinations and construct VDM extensions that parametrically scale results from a defined baseline SR and SAP to the arbitrary SR and SAP of interest. Scaling parameters are estimated using expert judgment data gathered with a novel pairwise comparison approach. These data are then used to demonstrate predictions and how multivariate VDM techniques could be used by software-makers.
机译：启用已知漏洞利用技术的漏洞是可以避免的，但是它们的公开发现仍在软件中继续。提出了漏洞发现建模（VDM）技术以帮助管理人员做出决策，但不包括描述软件版本（SR）（例如，代码大小和复杂性特征）和安全评估配置文件（SAP）（例如，安全团队规模）的有影响力的变量或技能）。因此，他们仅限于对独特产品的SR和SAP场景随时间进行的发现建模，如果不做出与研究中的所有SR和SAP组合相等的假设，其结果就无法轻易比较。本文介绍了一项突破性的功能，该功能可以随时间预测任意SR和SAP组合的预期发现，从而使管理人员可以更好地了解他们控制的影响变量对现象的影响。为此，我们使用描述任意SR和SAP组合的变量，并构建VDM扩展，以参数方式将结果从定义的基准SR和SAP扩展到感兴趣的任意SR和SAP。使用通过新颖的成对比较方法收集的专家判断数据来估算缩放参数。然后将这些数据用于演示预测以及软件制造商如何使用多元VDM技术。

著录项

  • 来源
    《Reliability Engineering & System Safety》 |2018年第8期|52-61|共10页
  • 作者

    Johnston Reuben; Sarkani Shahryar; Mazzuchi Thomas; Holzer Thomas; Eveleigh Timothy;

  • 作者单位

    George Washington Univ, Dept Engn Management & Syst Engn, 800 22nd St NW, Washington, DC 20052 USA;

    George Washington Univ, Dept Engn Management & Syst Engn, 800 22nd St NW, Washington, DC 20052 USA;

    George Washington Univ, Dept Engn Management & Syst Engn, 800 22nd St NW, Washington, DC 20052 USA;

    George Washington Univ, Dept Engn Management & Syst Engn, 800 22nd St NW, Washington, DC 20052 USA;

    George Washington Univ, Dept Engn Management & Syst Engn, 800 22nd St NW, Washington, DC 20052 USA;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Parametric scaling; Regression; Growth curve; Poisson process;

    机译：参数缩放;回归;增长曲线;泊松过程;

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