基于半监督学习的工业控制网络安全基线技术研究

Yixiang Jiang, Chengting Zhang, Wen Jin
{"title":"基于半监督学习的工业控制网络安全基线技术研究","authors":"Yixiang Jiang, Chengting Zhang, Wen Jin","doi":"10.2991/ICMEIT-19.2019.9","DOIUrl":null,"url":null,"abstract":"With the rapid development of industrial control network, performance management and risk prevention based on network traffic data, especially abnormal traffic detection, have gradually attracted people's attention. However, the traditional flow detection method based on fixed baseline cannot adapt to the growing data and increasingly complex data types. It leads to inaccurate test results and false alarms, and also consumes a lot of manpower and resources. In this paper, a semisupervised learning method is proposed to realize the self-construction of baseline and the automatic detection of abnormal index data.","PeriodicalId":223458,"journal":{"name":"Proceedings of the 3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019)","volume":"234 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Research on Baseline Technology of Industrial Control Network Security based on Semi-supervised Learning\",\"authors\":\"Yixiang Jiang, Chengting Zhang, Wen Jin\",\"doi\":\"10.2991/ICMEIT-19.2019.9\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"With the rapid development of industrial control network, performance management and risk prevention based on network traffic data, especially abnormal traffic detection, have gradually attracted people's attention. However, the traditional flow detection method based on fixed baseline cannot adapt to the growing data and increasingly complex data types. It leads to inaccurate test results and false alarms, and also consumes a lot of manpower and resources. In this paper, a semisupervised learning method is proposed to realize the self-construction of baseline and the automatic detection of abnormal index data.\",\"PeriodicalId\":223458,\"journal\":{\"name\":\"Proceedings of the 3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019)\",\"volume\":\"234 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2991/ICMEIT-19.2019.9\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2991/ICMEIT-19.2019.9","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

随着工业控制网络的快速发展,基于网络流量数据的性能管理和风险防范,特别是异常流量检测逐渐受到人们的重视。然而,传统的基于固定基线的流量检测方法已不能适应日益增长的数据量和日益复杂的数据类型。导致检测结果不准确和虚警,也消耗了大量的人力和资源。本文提出了一种半监督学习方法来实现基线的自构建和异常指标数据的自动检测。
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
Research on Baseline Technology of Industrial Control Network Security based on Semi-supervised Learning
With the rapid development of industrial control network, performance management and risk prevention based on network traffic data, especially abnormal traffic detection, have gradually attracted people's attention. However, the traditional flow detection method based on fixed baseline cannot adapt to the growing data and increasingly complex data types. It leads to inaccurate test results and false alarms, and also consumes a lot of manpower and resources. In this paper, a semisupervised learning method is proposed to realize the self-construction of baseline and the automatic detection of abnormal index data.
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
0.00%
发文量
0
期刊最新文献
Feedback-Based Scheduling for Load-Balanced Crosspoint Buffered Crossbar Switches Research on Traffic Congestion Resolution Mechanism based on Genetic Algorithm and Multi-Agent Decentralized Location Privacy Protection Method of Offset Grid Real-Time Bidding by Proportional Control in Display Advertising Simulation Analysis of Friction and Wear of New TiAl based Alloy Joint Bearings
×
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