Big Data Analysis Techniques for Cyber-threat Detection in Critical Infrastructures
Big Data Analysis Techniques for Cyber-threat Detection in Critical Infrastructures
复制标题
用于关键基础设施网络威胁检测的大数据分析技术
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
P. Fergus
中科院分区：
文献类型：
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作者：
William Hurst;M. Merabti;P. Fergus
本文中提出的研究提供了一种通过使用行为观察和大数据分析技术来增加深度防御（DiD）来支持关键基础设施中当前存在的安全性的方法。正如这项工作所表明的，将行为观察应用于关键基础设施的保护会产生有效的结果。我们的关键基础设施安全支持行为观察（BOCISS）设计处理模拟的关键基础设施数据，以检测对系统构成威胁的异常情况。这是通过特征提取和数据分类来实现的。数据是由一个核电站模拟使用西门子Tecnomatix工厂模拟器和编程语言SimTalk的发展。使用这种模拟，构建和收集广泛的真实数据集，当系统正常运行时，在网络攻击场景中。介绍了大数据分析技术、分类结果和结果评估。
The research presented in this paper offers a way of supporting the security currently in place in critical infrastructures by using behavioural observation and big data analysis techniques to add to the Defence in Depth (DiD). As this work demonstrates, applying behavioural observation to critical infrastructure protection has effective results. Our design for Behavioural Observation for Critical Infrastructure Security Support (BOCISS) processes simulated critical infrastructure data to detect anomalies which constitute threats to the system. This is achieved using feature extraction and data classification. The data is provided by the development of a nuclear power plant simulation using Siemens Tecnomatix Plant Simulator and the programming language SimTalk. Using this simulation, extensive realistic data sets are constructed and collected, when the system is functioning as normal and during a cyber-attack scenario. The big data analysis techniques, classification results and an assessment of the outcomes is presented.