监控和数据采集系统中基于流的入侵检测算法:一种实时方法

IF 1.7 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS IET Cyber-Physical Systems: Theory and Applications Pub Date : 2021-05-31 DOI:10.1049/cps2.12016
Marcio Andrey Teixeira, Maede Zolanvari, Khaled M. Khan, Raj Jain, Nader Meskin
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引用次数: 4

摘要

由于监控和数据采集(SCADA)系统在工业中的关键作用,入侵检测在这些系统中是不可或缺的。然而,文献中可用的方法缺乏代表性的基于流的数据集和可靠的实时适应和评估。提出了一个公开可用的标记数据集,用于支持特定于SCADA系统的基于流的入侵检测研究。针对我们的SCADA系统测试平台进行了网络攻击,以生成这个基于流的数据集。此外,针对SCADA系统,利用深度学习算法开发了基于流的入侵检测系统(IDS)。我们使用该数据集开发了用于SCADA系统实时操作的IDS模型,以便在攻击发生后瞬间检测到攻击。结果表明,该模型的充分性,当部署在线实时检测网络攻击的经验证明。
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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Flow-based intrusion detection algorithm for supervisory control and data acquisition systems: A real-time approach

Intrusion detection in supervisory control and data acquisition (SCADA) systems is integral because of the critical roles of these systems in industries. However, available approaches in the literature lack representative flow-based datasets and reliable real-time adaption and evaluation. A publicly available labelled dataset to support flow-based intrusion detection research specific to SCADA systems is presented. Cyberattacks were carried out against our SCADA system test bed to generate this flow-based dataset. Moreover, a flow-based intrusion detection system (IDS) is developed for SCADA systems using a deep learning algorithm. We used the dataset to develop this IDS model for real-time operations of SCADA systems to detect attacks momentarily after they happen. The results show empirical proof of the model’s adequacy when deployed online to detect cyberattacks in real time.

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来源期刊
IET Cyber-Physical Systems: Theory and Applications
IET Cyber-Physical Systems: Theory and Applications Computer Science-Computer Networks and Communications
CiteScore
5.40
自引率
6.70%
发文量
17
审稿时长
19 weeks
期刊最新文献
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