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Towards Scientific Incident Response

机译:走向科学事件响应

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摘要

A scientific incident analysis is one with a methodical, justifiable approach to the human decision-making process. Incident analysis is a good target for additional rigor because it is the most human-intensive part of incident response. Our goal is to provide the tools necessary for specifying precisely the reasoning process in incident analysis. Such tools are lacking, and are a necessary (though not sufficient) component of a more scientific analysis process. To reach this goal, we adapt tools from program verification that can capture and test abductive reasoning. As Charles Peirce coined the term in 1900, "Abduction is the process of forming an explanatory hypothesis. It is the only logical operation which introduces any new idea." We reference canonical examples as paradigms of decision-making during analysis. With these examples in mind, we design a logic capable of expressing decision-making during incident analysis. The result is that we can express, in machine-readable and precise language, the abductive hypotheses than an analyst makes, and the results of evaluating them. This result is beneficial because it opens up the opportunity of genuinely comparing analyst processes without revealing sensitive system details, as well as opening an opportunity towards improved decision-support via limited automation.
机译:科学的事件分析是对人类决策过程采用系统的,合理的方法进行的分析。事件分析是更严格的一个很好的目标,因为它是事件响应中最耗费人力的部分。我们的目标是提供在事件分析中精确指定推理过程所必需的工具。缺少这样的工具,并且这些工具是更科学的分析过程的必要组成部分(尽管不够充分)。为了实现此目标,我们从程序验证中改编了可捕获和测试归纳推理的工具。正如查尔斯·皮尔斯(Charles Peirce)在1900年创造的术语一样,“绑架是形成解释性假设的过程。这是引入任何新观念的唯一合乎逻辑的操作。”我们将规范示例作为分析过程中决策的范例。考虑到这些示例,我们设计了一种能够在事件分析过程中表达决策的逻辑。结果是,我们可以用机器可读和精确的语言来表达比分析人员所做的假想假设以及评估它们的结果。该结果是有益的,因为它为在不泄露敏感系统细节的情况下提供了真正比较分析人员流程的机会,以及通过有限的自动化机会来改善决策支持的机会。

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