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arXiv 2103.02301cs.CR
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网络威胁情报中的威胁参与者类型推断和特征描述

Threat Actor Type Inference and Characterization within Cyber Threat Intelligence

Vasileios Mavroeidis, Ryan Hohimer, Tim Casey, Audun Jøsang

摘要:

随着网络威胁形势不断变得越来越复杂和多变,了解敌人及其行动方式以减少威胁变得越来越重要。尽管网络安全社区在描述和共享用于通知防御组件的技术指标方面已经发展了一定的成熟度,但我们仍然在努力处理不统一,非结构化和模糊的更高级别信息,例如威胁行为者上下文,从而限制了我们与不同来源相关联以获得更多上下文,准确和相关情报的能力。我们认为有必要克服这一限制,以提高我们制作和更好地操作网络威胁情报的能力。我们的研究表明,通常商定的用于表征威胁参与者及其操作的受控词汇表可用于丰富网络威胁情报,并在可解释和可查询的更高上下文级别上推断新信息。特别是,我们提出了一个本体论的方法来自动推断的威胁行为者的类型的基础上,他们的人物角色,了解他们的性质,并捕捉多态性和变化,他们的行为和特征随着时间的推移。这种方法不仅通过提供用于共享高度上下文网络威胁情报的结构化方式和手段来实现互操作性,而且还以机器速度获得新信息,并最大限度地减少人工分类方法所带来的认知偏差。

英文摘要:

As the cyber threat landscape is constantly becoming increasingly complex and polymorphic, the more critical it becomes to understand the enemy and its modus operandi for anticipatory threat reduction. Even though the cyber security community has developed a certain maturity in describing and sharing technical indicators for informing defense components, we still struggle with non-uniform, unstructured, and ambiguous higher-level information, such as the threat actor context, thereby limiting our ability to correlate with different sources to derive more contextual, accurate, and relevant intelligence. We see the need to overcome this limitation in order to increase our ability to produce and better operationalize cyber threat intelligence. Our research demonstrates how commonly agreed upon controlled vocabularies for characterizing threat actors and their operations can be used to enrich cyber threat intelligence and infer new information at a higher contextual level that is explicable and queryable. In particular, we present an ontological approach to automatically inferring the types of threat actors based on their personas, understanding their nature, and capturing polymorphism and changes in their behavior and characteristics over time. Such an approach not only enables interoperability by providing a structured way and means for sharing highly contextual cyber threat intelligence but also derives new information at machine speed and minimizes cognitive biases that manual classification approaches entail.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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