网络攻击阶段的因果发现

2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) Pub Date : 2019-12-01 DOI:10.1109/ICMLA.2019.00219

W. G. Mueller, Alex Memory, Kyle Bartrem

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引用次数: 4

摘要

因果发现算法越来越多地被用于从大量观测数据中发现有效的、新颖的和重要的因果关系。网络攻击被假设为根据网络杀伤链®进化，该链由描述网络攻击阶段的因果模型组成。本文将因果发现引入网络安全研究，并通过对两个真实网络攻击数据库的广泛实证评估提供了杀伤链的证据。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Causal Discovery of Cyber Attack Phases

Causal discovery algorithms are increasingly being used to discover valid, novel, and significant causal relationships from large amounts of observational data. Cyberattacks are hypothesized to evolve according to the Cyber Kill Chain® which consists of a causal model describing the phases of a cyberattack. This paper introduces causal discovery to cybersecurity research and provides evidence of the kill chain with an extensive empirical assessment of two databases of real cyberattacks.

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来源期刊

2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)

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