Flow-based intrusion detection algorithm for supervisory control and data acquisition systems: A real-time approach

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
{"title":"Flow-based intrusion detection algorithm for supervisory control and data acquisition systems: A real-time approach","authors":"Marcio Andrey Teixeira,&nbsp;Maede Zolanvari,&nbsp;Khaled M. Khan,&nbsp;Raj Jain,&nbsp;Nader Meskin","doi":"10.1049/cps2.12016","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"6 3","pages":"178-191"},"PeriodicalIF":1.7000,"publicationDate":"2021-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.12016","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Cyber-Physical Systems: Theory and Applications","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/cps2.12016","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 4

Abstract

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.

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
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
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。
求助全文
约1分钟内获得全文 去求助
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
Guest Editorial: IoT-based secure health monitoring and tracking through estimated computing SEIR-driven semantic integration framework: Internet of Things-enhanced epidemiological surveillance in COVID-19 outbreaks using recurrent neural networks A machine learning model for Alzheimer's disease prediction Securing the Internet of Medical Things with ECG-based PUF encryption Status, challenges, and promises of data-driven battery lifetime prediction under cyber-physical system context
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1