云平台下的电网安全大数据监测与分析

Xu Shenu-guo, Liu Jian, Guo Liang, Xue Jia
{"title":"云平台下的电网安全大数据监测与分析","authors":"Xu Shenu-guo, Liu Jian, Guo Liang, Xue Jia","doi":"10.1117/12.2682321","DOIUrl":null,"url":null,"abstract":"With the rapid development of cloud computing technology and various applications, enterprises have also built their own cloud platforms.As each information system gradually goes to the cloud and monitoring data increases, the security protection pressure of the cloud platform increases accordingly, resulting in weak data analysis ability and low alarm failure. Computing performance has become a key problem that restricts the security monitoring and analysis of power network under cloud platform. Aiming at the performance bottleneck of security monitoring under cloud level, this paper proposes a method to improve the efficiency of big data monitoring and analysis. This method collects the log data of all kinds of security equipment and security system in the network, and uses the real-time processing framework of Flink and ODPS data processing service on the cloud to quickly analyze the big data. Experimental results show that compared with traditional self-built Hadoop platform and real-time computing Storm, this method consumes less resources and has a faster processing speed for the same log volume.","PeriodicalId":440430,"journal":{"name":"International Conference on Electronic Technology and Information Science","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Big data monitoring and analysis of power network security under cloud platform\",\"authors\":\"Xu Shenu-guo, Liu Jian, Guo Liang, Xue Jia\",\"doi\":\"10.1117/12.2682321\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"With the rapid development of cloud computing technology and various applications, enterprises have also built their own cloud platforms.As each information system gradually goes to the cloud and monitoring data increases, the security protection pressure of the cloud platform increases accordingly, resulting in weak data analysis ability and low alarm failure. Computing performance has become a key problem that restricts the security monitoring and analysis of power network under cloud platform. Aiming at the performance bottleneck of security monitoring under cloud level, this paper proposes a method to improve the efficiency of big data monitoring and analysis. This method collects the log data of all kinds of security equipment and security system in the network, and uses the real-time processing framework of Flink and ODPS data processing service on the cloud to quickly analyze the big data. Experimental results show that compared with traditional self-built Hadoop platform and real-time computing Storm, this method consumes less resources and has a faster processing speed for the same log volume.\",\"PeriodicalId\":440430,\"journal\":{\"name\":\"International Conference on Electronic Technology and Information Science\",\"volume\":\"19 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-06-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Conference on Electronic Technology and Information Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1117/12.2682321\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Conference on Electronic Technology and Information Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1117/12.2682321","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

随着云计算技术和各种应用的快速发展,企业也纷纷建立了自己的云平台。随着各个信息系统逐渐走向云端,监控数据增多,云平台的安全防护压力随之增大,导致数据分析能力较弱,报警失败率较低。计算性能已成为制约云平台下电网安全监控与分析的关键问题。针对云级别下安防监控的性能瓶颈,本文提出了一种提高大数据监控分析效率的方法。该方法采集网络中各类安全设备和安全系统的日志数据,利用Flink和云端ODPS数据处理服务的实时处理框架,对大数据进行快速分析。实验结果表明,与传统的自建Hadoop平台和实时计算Storm相比,对于相同的日志量,该方法消耗的资源更少,处理速度更快。
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
Big data monitoring and analysis of power network security under cloud platform
With the rapid development of cloud computing technology and various applications, enterprises have also built their own cloud platforms.As each information system gradually goes to the cloud and monitoring data increases, the security protection pressure of the cloud platform increases accordingly, resulting in weak data analysis ability and low alarm failure. Computing performance has become a key problem that restricts the security monitoring and analysis of power network under cloud platform. Aiming at the performance bottleneck of security monitoring under cloud level, this paper proposes a method to improve the efficiency of big data monitoring and analysis. This method collects the log data of all kinds of security equipment and security system in the network, and uses the real-time processing framework of Flink and ODPS data processing service on the cloud to quickly analyze the big data. Experimental results show that compared with traditional self-built Hadoop platform and real-time computing Storm, this method consumes less resources and has a faster processing speed for the same log volume.
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
Network traffic classification based on multi-head attention and deep metric learning A study of regional precipitation data fusion model based on BP-LSTM in Qinghai province Design and application of an intelligent monitoring and early warning system for bioremediation of coking contaminated sites Research on improved adaptive spectrum access mechanism for millimetre wave Unloading optimization of networked vehicles based on improved genetic and particle swarm optimization
×
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