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Multimodal Graph Analysis of Cyber Attacks

机译:网络攻击的多峰图分析

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The limited information on the cyberattacks available in the unclassified regime, hardens standardizing the analysis. We address the problem of modeling and analyzing cyberattacks using a multimodal graph approach. We formulate the stages, actors, and outcomes of cyberattacks as a multimodal graph. Multimodal graph nodes include cyberattack victims, adversaries, autonomous systems, and the observed cyber events. In multimodal graphs, single-modality graphs are interconnected according to their interaction. We apply community and centrality analysis on the graph to obtain in-depth insights into the attack. In community analysis, we cluster those nodes that exhibit “strong” inter-modal ties. We further use centrality to rank the nodes according to their importance. Classifying nodes according to centrality provides the progression of the attack from the attacker to the targeted nodes. We apply our methods to two popular case studies, namely GhostNet and Putter Panda and demonstrate a clear distinction in the attack stages.
机译:在未分类状态下,有关网络攻击的信息有限,这使得分析标准化变得更加困难。我们解决了使用多峰图方法对网络攻击进行建模和分析的问题。我们将网络攻击的阶段,参与者和结果表述为多模式图。多峰图节点包括网络攻击的受害者,对手,自治系统和观察到的网络事件。在多峰图中,单峰图根据它们的相互作用而相互连接。我们在图表上应用社区和中心性分析,以获得对攻击的深入了解。在社区分析中,我们将那些表现出“强大”联运关系的节点聚在一起。我们进一步使用中心性来根据节点的重要性对节点进行排名。根据中心性对节点进行分类可提供从攻击者到目标节点的攻击过程。我们将我们的方法应用于两个流行的案例研究,即GhostNet和Putter Panda,并证明了在攻击阶段的明显区别。

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