变电站网络安全态势感知策略与设备远程运维研究

IF 3.1 Q1 Mathematics
Jing Bai, Jianlin Jiao, Meng Han, Xianfei Zhou, Chao Liu
{"title":"变电站网络安全态势感知策略与设备远程运维研究","authors":"Jing Bai, Jianlin Jiao, Meng Han, Xianfei Zhou, Chao Liu","doi":"10.2478/amns-2024-0714","DOIUrl":null,"url":null,"abstract":"\n Substation network security is the key to maintaining the stable operation of power systems. In the face of growing threats of network attacks, traditional security protection measures have been brutal to meet the needs of modern power systems. Research on substation network security, situational awareness strategies, and remote operation and maintenance of equipment is essential to improve network defense capability and ensure the continuity and reliability of power supply. This study explores effective security situational awareness methods and remote operation and maintenance techniques to provide new solutions for substation network security. This paper builds an efficient network attack detection model by introducing linear discriminant analysis (LDA) and radial basis function (RBF) neural networks. The experiment uses the KDD Cup99 dataset, which is preprocessed to provide the model training and testing data. The LDA-RBF model in this paper outperforms the traditional RNF neural and BP neural networks regarding recognition rate. Specifically, the recognition rate reaches 90.2% for the Smurf attack and 100% for the Ipsweep attack. The proposed model of the study also performs well in terms of leakage and false alarm rates, with an overall recognition rate of 97.00%. This study proposes a network security situational awareness strategy and equipment remote operation and maintenance method that can effectively enhance substation networks’ security and operation and maintenance efficiency.","PeriodicalId":52342,"journal":{"name":"Applied Mathematics and Nonlinear Sciences","volume":null,"pages":null},"PeriodicalIF":3.1000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Research on Substation Network Security Situational Awareness Strategy and Equipment Remote Operation and Maintenance\",\"authors\":\"Jing Bai, Jianlin Jiao, Meng Han, Xianfei Zhou, Chao Liu\",\"doi\":\"10.2478/amns-2024-0714\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n Substation network security is the key to maintaining the stable operation of power systems. In the face of growing threats of network attacks, traditional security protection measures have been brutal to meet the needs of modern power systems. Research on substation network security, situational awareness strategies, and remote operation and maintenance of equipment is essential to improve network defense capability and ensure the continuity and reliability of power supply. This study explores effective security situational awareness methods and remote operation and maintenance techniques to provide new solutions for substation network security. This paper builds an efficient network attack detection model by introducing linear discriminant analysis (LDA) and radial basis function (RBF) neural networks. The experiment uses the KDD Cup99 dataset, which is preprocessed to provide the model training and testing data. The LDA-RBF model in this paper outperforms the traditional RNF neural and BP neural networks regarding recognition rate. Specifically, the recognition rate reaches 90.2% for the Smurf attack and 100% for the Ipsweep attack. The proposed model of the study also performs well in terms of leakage and false alarm rates, with an overall recognition rate of 97.00%. This study proposes a network security situational awareness strategy and equipment remote operation and maintenance method that can effectively enhance substation networks’ security and operation and maintenance efficiency.\",\"PeriodicalId\":52342,\"journal\":{\"name\":\"Applied Mathematics and Nonlinear Sciences\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2024-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied Mathematics and Nonlinear Sciences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2478/amns-2024-0714\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"Mathematics\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Mathematics and Nonlinear Sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2478/amns-2024-0714","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Mathematics","Score":null,"Total":0}
引用次数: 0

摘要

变电站网络安全是维护电力系统稳定运行的关键。面对日益增长的网络攻击威胁,传统的安全保护措施已难以满足现代电力系统的需求。对变电站网络安全、态势感知策略和设备远程运维的研究对于提高网络防御能力、确保供电的连续性和可靠性至关重要。本研究探讨了有效的安全态势感知方法和远程运维技术,为变电站网络安全提供了新的解决方案。本文通过引入线性判别分析(LDA)和径向基函数(RBF)神经网络,建立了一个高效的网络攻击检测模型。实验使用 KDD Cup99 数据集,该数据集经过预处理,可提供模型训练和测试数据。本文中的 LDA-RBF 模型在识别率方面优于传统的 RNF 神经网络和 BP 神经网络。具体来说,对 Smurf 攻击的识别率达到 90.2%,对 Ipsweep 攻击的识别率达到 100%。本研究提出的模型在泄漏率和误报率方面也表现出色,总体识别率达到 97.00%。本研究提出了一种网络安全态势感知策略和设备远程运维方法,可有效提高变电站网络的安全性和运维效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on Substation Network Security Situational Awareness Strategy and Equipment Remote Operation and Maintenance
Substation network security is the key to maintaining the stable operation of power systems. In the face of growing threats of network attacks, traditional security protection measures have been brutal to meet the needs of modern power systems. Research on substation network security, situational awareness strategies, and remote operation and maintenance of equipment is essential to improve network defense capability and ensure the continuity and reliability of power supply. This study explores effective security situational awareness methods and remote operation and maintenance techniques to provide new solutions for substation network security. This paper builds an efficient network attack detection model by introducing linear discriminant analysis (LDA) and radial basis function (RBF) neural networks. The experiment uses the KDD Cup99 dataset, which is preprocessed to provide the model training and testing data. The LDA-RBF model in this paper outperforms the traditional RNF neural and BP neural networks regarding recognition rate. Specifically, the recognition rate reaches 90.2% for the Smurf attack and 100% for the Ipsweep attack. The proposed model of the study also performs well in terms of leakage and false alarm rates, with an overall recognition rate of 97.00%. This study proposes a network security situational awareness strategy and equipment remote operation and maintenance method that can effectively enhance substation networks’ security and operation and maintenance efficiency.
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
Applied Mathematics and Nonlinear Sciences
Applied Mathematics and Nonlinear Sciences Engineering-Engineering (miscellaneous)
CiteScore
2.90
自引率
25.80%
发文量
203
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信