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介绍 CYSAS-S3 数据集,用于实现面向任务的网络态势感知。

Introducing the CYSAS-S3 Dataset for Operationalizing a Mission-Oriented Cyber Situational Awareness.

机构信息

Indra Digital Labs, Av. de Bruselas, 35, 28108 Alcobendas, Spain.

Universidad Internacional de La Rioja (UNIR), Av. de la Paz, 137, 26006 Logroño, Spain.

出版信息

Sensors (Basel). 2022 Jul 7;22(14):5104. doi: 10.3390/s22145104.

Abstract

The digital transformation of the defence sector is not exempt from innovative requirements and challenges, with the lack of availability of reliable, unbiased and consistent data for training automatisms (machine learning algorithms, decision-making, what-if recreation of operational conditions, support the human understanding of the hybrid operational picture, personnel training/education, etc.) being one of the most relevant gaps. In the context of cyber defence, the state-of-the-art provides a plethora of data network collections that tend to lack presenting the information of all communication layers (physical to application). They are synthetically generated in scenarios far from the singularities of cyber defence operations. None of these data network collections took into consideration usage profiles and specific environments directly related to acquiring a cyber situational awareness, typically missing the relationship between incidents registered at the hardware/software level and their impact on the military mission assets and objectives, which consequently bypasses the entire chain of dependencies between strategic, operational, tactical and technical domains. In order to contribute to the mitigation of these gaps, this paper introduces CYSAS-S3, a novel dataset designed and created as a result of a joint research action that explores the principal needs for datasets by cyber defence centres, resulting in the generation of a collection of samples that correlate the impact of selected Advanced Persistent Threats (APT) with each phase of their cyber kill chain, regarding mission-level operations and goals.

摘要

国防领域的数字化转型也不能免除创新的要求和挑战,其中最相关的差距之一是缺乏可用于训练自动化(机器学习算法、决策、操作条件的假设再现、支持人机对混合作战态势的理解、人员培训/教育等)的可靠、公正和一致的数据。在网络防御方面,现有技术提供了大量的数据网络收集,这些收集往往缺乏呈现所有通信层(从物理层到应用层)的信息。它们是在远离网络防御操作奇点的场景中综合生成的。这些数据网络收集都没有考虑到与获取网络态势感知直接相关的使用情况和特定环境,通常会忽略在硬件/软件级别注册的事件与它们对军事任务资产和目标的影响之间的关系,从而绕过了战略、作战、战术和技术领域之间的整个依赖关系链。为了缓解这些差距,本文介绍了 CYSAS-S3,这是一个新的数据集,是通过联合研究行动设计和创建的,该行动探讨了网络防御中心对数据集的主要需求,生成了一组样本,这些样本将选定的高级持续威胁(APT)的影响与它们的网络杀伤链的每个阶段相关联,涉及任务级别的操作和目标。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c97b/9318677/09c840327389/sensors-22-05104-g001.jpg

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