保护工业控制系统免受网络攻击:基于堆叠神经网络的方法

IF 3.7 4区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE IEEE Consumer Electronics Magazine Pub Date : 2024-01-01 DOI:10.1109/mce.2022.3168997
Sujeet S. Jagtap, Shankar Sriram V. S., K. Kotecha, S. V.
{"title":"保护工业控制系统免受网络攻击:基于堆叠神经网络的方法","authors":"Sujeet S. Jagtap, Shankar Sriram V. S., K. Kotecha, S. V.","doi":"10.1109/mce.2022.3168997","DOIUrl":null,"url":null,"abstract":"Demanding scientific evolution and undisrupted resource requirement of consumers signified the amalgamation of mechanical production, mass production, and digitalized production for the fourth industrial revolution, “Industry 4.0.” Critical infrastructures that operate and govern industrial sectors and public utilities, such as water desalination plants, smart grids, and gas pipelines, incorporated this cognitive-mechatronic augmentation for the seamless integration of software, control components, and production employees to increase the productivity scale. Although connectivity, automation, and optimization made industrial sectors realize the full potential of smart manufacturing, the inclusion of supervisory control and data acquisition systems into cyberspace expanded the attack vectors that made industrial control systems the prime target for cyber-attackers. Conventional security solutions, such as firewalls, traditional intrusion-detection systems, and antivirus, have been proposed and developed by the research community acted as a proficient line of cyber-defense. However, protecting critical infrastructures from heterogeneous cyber-attacks for resilient operability still pose a significant research challenge. In addition, although machine learning and deep-learning-based intrusion-detection models have been proposed and optimized in the literature, operational viability still poses a significant setback for real-time intrusion detection on industrial control systems. By considering the limitations identified in the literature, a stacked deep-learning model is proposed and validated over laboratory-scale industrial datasets. Furthermore, this article provides an overview of cyber-physical systems, conventional security solutions, and their challenges in identifying unseen exploits. As a concluding remark, JARA: a hybrid opensource deployment-ready intelligent intrusion-detection system, has been presented that feasibly detects the HnS IIoT malware when deployed on a Linux virtual machine.","PeriodicalId":54330,"journal":{"name":"IEEE Consumer Electronics Magazine","volume":"1 1","pages":"30-38"},"PeriodicalIF":3.7000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Securing Industrial Control Systems from Cyber-Attacks: A Stacked Neural-Network based Approach\",\"authors\":\"Sujeet S. Jagtap, Shankar Sriram V. S., K. Kotecha, S. V.\",\"doi\":\"10.1109/mce.2022.3168997\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Demanding scientific evolution and undisrupted resource requirement of consumers signified the amalgamation of mechanical production, mass production, and digitalized production for the fourth industrial revolution, “Industry 4.0.” Critical infrastructures that operate and govern industrial sectors and public utilities, such as water desalination plants, smart grids, and gas pipelines, incorporated this cognitive-mechatronic augmentation for the seamless integration of software, control components, and production employees to increase the productivity scale. Although connectivity, automation, and optimization made industrial sectors realize the full potential of smart manufacturing, the inclusion of supervisory control and data acquisition systems into cyberspace expanded the attack vectors that made industrial control systems the prime target for cyber-attackers. Conventional security solutions, such as firewalls, traditional intrusion-detection systems, and antivirus, have been proposed and developed by the research community acted as a proficient line of cyber-defense. However, protecting critical infrastructures from heterogeneous cyber-attacks for resilient operability still pose a significant research challenge. In addition, although machine learning and deep-learning-based intrusion-detection models have been proposed and optimized in the literature, operational viability still poses a significant setback for real-time intrusion detection on industrial control systems. By considering the limitations identified in the literature, a stacked deep-learning model is proposed and validated over laboratory-scale industrial datasets. Furthermore, this article provides an overview of cyber-physical systems, conventional security solutions, and their challenges in identifying unseen exploits. As a concluding remark, JARA: a hybrid opensource deployment-ready intelligent intrusion-detection system, has been presented that feasibly detects the HnS IIoT malware when deployed on a Linux virtual machine.\",\"PeriodicalId\":54330,\"journal\":{\"name\":\"IEEE Consumer Electronics Magazine\",\"volume\":\"1 1\",\"pages\":\"30-38\"},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2024-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Consumer Electronics Magazine\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1109/mce.2022.3168997\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Consumer Electronics Magazine","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1109/mce.2022.3168997","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
引用次数: 0
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
Securing Industrial Control Systems from Cyber-Attacks: A Stacked Neural-Network based Approach
Demanding scientific evolution and undisrupted resource requirement of consumers signified the amalgamation of mechanical production, mass production, and digitalized production for the fourth industrial revolution, “Industry 4.0.” Critical infrastructures that operate and govern industrial sectors and public utilities, such as water desalination plants, smart grids, and gas pipelines, incorporated this cognitive-mechatronic augmentation for the seamless integration of software, control components, and production employees to increase the productivity scale. Although connectivity, automation, and optimization made industrial sectors realize the full potential of smart manufacturing, the inclusion of supervisory control and data acquisition systems into cyberspace expanded the attack vectors that made industrial control systems the prime target for cyber-attackers. Conventional security solutions, such as firewalls, traditional intrusion-detection systems, and antivirus, have been proposed and developed by the research community acted as a proficient line of cyber-defense. However, protecting critical infrastructures from heterogeneous cyber-attacks for resilient operability still pose a significant research challenge. In addition, although machine learning and deep-learning-based intrusion-detection models have been proposed and optimized in the literature, operational viability still poses a significant setback for real-time intrusion detection on industrial control systems. By considering the limitations identified in the literature, a stacked deep-learning model is proposed and validated over laboratory-scale industrial datasets. Furthermore, this article provides an overview of cyber-physical systems, conventional security solutions, and their challenges in identifying unseen exploits. As a concluding remark, JARA: a hybrid opensource deployment-ready intelligent intrusion-detection system, has been presented that feasibly detects the HnS IIoT malware when deployed on a Linux virtual machine.
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
IEEE Consumer Electronics Magazine
IEEE Consumer Electronics Magazine Computer Science-Hardware and Architecture
CiteScore
10.00
自引率
8.90%
发文量
151
期刊介绍: The scope will cover the following areas that are related to “consumer electronics” and other topics considered of interest to consumer electronics: Video technology, Audio technology, White goods, Home care products, Mobile communications, Gaming, Air care products, Home medical devices, Fitness devices, Home automation & networking devices, Consumer solar technology, Home theater, Digital imaging, In Vehicle technology, Wireless technology, Cable & satellite technology, Home security, Domestic lighting, Human interface, Artificial intelligence, Home computing, Video Technology, Consumer storage technology.
期刊最新文献
Novel Data Fusion Scheme for Enhanced User Experiences in Terahertz-Enabled IoNT Evaluating Sustainability and Social Costs of Adversarial Training in Machine Learning Advanced Context-aware Computing for Human Machine Interaction in Consumer Electronics Heterogeneous Parallel Acceleration for Edge Intelligence Systems: Challenges and Solutions Hardware Implementation of Low-Latency Image Denoising with Noise2Noise-Extended Learning for Ultra-High Definition Cameras
×
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