水处理和分配中的网络物理系统分析调查:安全挑战、入侵检测和未来方向

IF 1.5 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Security and Privacy Pub Date : 2024-07-04 DOI:10.1002/spy2.440
Qawsar Gulzar, Khurram Mustafa
{"title":"水处理和分配中的网络物理系统分析调查:安全挑战、入侵检测和未来方向","authors":"Qawsar Gulzar, Khurram Mustafa","doi":"10.1002/spy2.440","DOIUrl":null,"url":null,"abstract":"Since the inception of the Industrial 4.0 revolution, industrial cyber‐physical systems (CPSs) have become integral to critical infrastructures and industrial sectors, including water treatment and distribution systems. Integrating physical and digital worlds has made communication systems within these plants—comprising actuators, sensors, and controllers—vulnerable to advanced cyber‐attacks. Safeguarding the nation's critical infrastructure has thus attracted significant interest from both academia and industry. This article thoroughly examines water treatment and distribution CPSs, detailing their architectural design, devices, applications, and security standards. It analyzes various cyber‐attacks and explores CPS security vulnerabilities and their detection and mitigation techniques. Additionally, it reviews the trends in machine learning (ML) and deep learning (DL) intrusion detection system (IDS) solutions, highlighting their advantages and disadvantages. The article evaluates current datasets and testbeds, identifying some of the best‐performing IDS algorithms tested on each dataset compared to previous research, which could serve as benchmarks in this field. Finally, it proposes data augmentation techniques to generate comprehensive datasets, identifies research gaps, and suggests potential improvements to enhance IDS performance.","PeriodicalId":29939,"journal":{"name":"Security and Privacy","volume":null,"pages":null},"PeriodicalIF":1.5000,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An analytical survey of cyber‐physical systems in water treatment and distribution: Security challenges, intrusion detection, and future directions\",\"authors\":\"Qawsar Gulzar, Khurram Mustafa\",\"doi\":\"10.1002/spy2.440\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Since the inception of the Industrial 4.0 revolution, industrial cyber‐physical systems (CPSs) have become integral to critical infrastructures and industrial sectors, including water treatment and distribution systems. Integrating physical and digital worlds has made communication systems within these plants—comprising actuators, sensors, and controllers—vulnerable to advanced cyber‐attacks. Safeguarding the nation's critical infrastructure has thus attracted significant interest from both academia and industry. This article thoroughly examines water treatment and distribution CPSs, detailing their architectural design, devices, applications, and security standards. It analyzes various cyber‐attacks and explores CPS security vulnerabilities and their detection and mitigation techniques. Additionally, it reviews the trends in machine learning (ML) and deep learning (DL) intrusion detection system (IDS) solutions, highlighting their advantages and disadvantages. The article evaluates current datasets and testbeds, identifying some of the best‐performing IDS algorithms tested on each dataset compared to previous research, which could serve as benchmarks in this field. Finally, it proposes data augmentation techniques to generate comprehensive datasets, identifies research gaps, and suggests potential improvements to enhance IDS performance.\",\"PeriodicalId\":29939,\"journal\":{\"name\":\"Security and Privacy\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.5000,\"publicationDate\":\"2024-07-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Security and Privacy\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1002/spy2.440\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Security and Privacy","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1002/spy2.440","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: 0

摘要

自工业 4.0 革命开始以来,工业网络物理系统 (CPS) 已成为包括水处理和分配系统在内的关键基础设施和工业部门不可或缺的组成部分。物理世界与数字世界的融合使得这些工厂内的通信系统(包括执行器、传感器和控制器)很容易受到高级网络攻击。因此,保护国家的关键基础设施引起了学术界和工业界的极大兴趣。本文深入研究了水处理和配水 CPS,详细介绍了它们的结构设计、设备、应用和安全标准。文章分析了各种网络攻击,探讨了 CPS 的安全漏洞及其检测和缓解技术。此外,文章还回顾了机器学习(ML)和深度学习(DL)入侵检测系统(IDS)解决方案的发展趋势,强调了它们的优缺点。文章评估了当前的数据集和测试平台,确定了与以前的研究相比,在每个数据集上测试的一些性能最佳的 IDS 算法,这些数据集可作为该领域的基准。最后,文章提出了生成综合数据集的数据增强技术,确定了研究空白,并提出了提高 IDS 性能的潜在改进建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
An analytical survey of cyber‐physical systems in water treatment and distribution: Security challenges, intrusion detection, and future directions
Since the inception of the Industrial 4.0 revolution, industrial cyber‐physical systems (CPSs) have become integral to critical infrastructures and industrial sectors, including water treatment and distribution systems. Integrating physical and digital worlds has made communication systems within these plants—comprising actuators, sensors, and controllers—vulnerable to advanced cyber‐attacks. Safeguarding the nation's critical infrastructure has thus attracted significant interest from both academia and industry. This article thoroughly examines water treatment and distribution CPSs, detailing their architectural design, devices, applications, and security standards. It analyzes various cyber‐attacks and explores CPS security vulnerabilities and their detection and mitigation techniques. Additionally, it reviews the trends in machine learning (ML) and deep learning (DL) intrusion detection system (IDS) solutions, highlighting their advantages and disadvantages. The article evaluates current datasets and testbeds, identifying some of the best‐performing IDS algorithms tested on each dataset compared to previous research, which could serve as benchmarks in this field. Finally, it proposes data augmentation techniques to generate comprehensive datasets, identifies research gaps, and suggests potential improvements to enhance IDS performance.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
5.30%
发文量
80
期刊最新文献
IoT malware detection using static and dynamic analysis techniques: A systematic literature review An approach for mitigating cognitive load in password management by integrating QR codes and steganography Cryptographic methods for secured communication in SDN‐based VANETs: A performance analysis Exploring security and privacy enhancement technologies in the Internet of Things: A comprehensive review Research on privacy leakage of celebrity's ID card number based on real‐name authentication
×
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