云计算环境下的网络安全分析

Linjiang Xie, Feilu Hang, W. Guo, Zhenhong Zhang, Hanruo Li
{"title":"云计算环境下的网络安全分析","authors":"Linjiang Xie, Feilu Hang, W. Guo, Zhenhong Zhang, Hanruo Li","doi":"10.1142/s1793962322500544","DOIUrl":null,"url":null,"abstract":"Information technology services for businesses and consumers can be delivered via the Internet using cloud computing (CC) because it is agile, cost-effective, and time-tested. For many real-world applications, the data are kept in the cloud by a third-party service and accessible through the Internet as needed through CC approaches. Risks associated with CC involve the data security and network security account for real-time systems. This paper discusses different security threats in CC and suggests a solution by designing a network security analysis scheme with machine learning (NSA-ML). The ML classifier predicts the network vulnerabilities and prevents insecure communication in a CC environment. The proposed NSA-ML presents a data authentication scheme with a novel encryption methodology to ensure data security. The experimental results show that the proposed NSA-ML outperforms the existing cloud security approaches by gaining an efficiency of 95.4%.","PeriodicalId":13657,"journal":{"name":"Int. J. Model. Simul. Sci. Comput.","volume":"64 1","pages":"2250054:1-2250054:20"},"PeriodicalIF":0.0000,"publicationDate":"2022-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Network security analysis for cloud computing environment\",\"authors\":\"Linjiang Xie, Feilu Hang, W. Guo, Zhenhong Zhang, Hanruo Li\",\"doi\":\"10.1142/s1793962322500544\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Information technology services for businesses and consumers can be delivered via the Internet using cloud computing (CC) because it is agile, cost-effective, and time-tested. For many real-world applications, the data are kept in the cloud by a third-party service and accessible through the Internet as needed through CC approaches. Risks associated with CC involve the data security and network security account for real-time systems. This paper discusses different security threats in CC and suggests a solution by designing a network security analysis scheme with machine learning (NSA-ML). The ML classifier predicts the network vulnerabilities and prevents insecure communication in a CC environment. The proposed NSA-ML presents a data authentication scheme with a novel encryption methodology to ensure data security. The experimental results show that the proposed NSA-ML outperforms the existing cloud security approaches by gaining an efficiency of 95.4%.\",\"PeriodicalId\":13657,\"journal\":{\"name\":\"Int. J. Model. Simul. Sci. Comput.\",\"volume\":\"64 1\",\"pages\":\"2250054:1-2250054:20\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-03-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Int. J. Model. Simul. Sci. Comput.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1142/s1793962322500544\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Int. J. Model. Simul. Sci. Comput.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1142/s1793962322500544","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

摘要

面向企业和消费者的信息技术服务可以通过使用云计算(CC)的互联网交付,因为它是敏捷的、具有成本效益的,并且经过了时间的考验。对于许多现实世界的应用程序,数据由第三方服务保存在云中,并根据需要通过Internet通过CC方法进行访问。与CC相关的风险涉及实时系统的数据安全和网络安全帐户。本文讨论了CC中的各种安全威胁,并通过设计一种基于机器学习的网络安全分析方案(NSA-ML)提出了解决方案。ML分类器预测网络漏洞,防止CC环境中的不安全通信。提出的NSA-ML提出了一种具有新颖加密方法的数据认证方案,以确保数据安全。实验结果表明,本文提出的NSA-ML方法的效率达到95.4%,优于现有的云安全方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Network security analysis for cloud computing environment
Information technology services for businesses and consumers can be delivered via the Internet using cloud computing (CC) because it is agile, cost-effective, and time-tested. For many real-world applications, the data are kept in the cloud by a third-party service and accessible through the Internet as needed through CC approaches. Risks associated with CC involve the data security and network security account for real-time systems. This paper discusses different security threats in CC and suggests a solution by designing a network security analysis scheme with machine learning (NSA-ML). The ML classifier predicts the network vulnerabilities and prevents insecure communication in a CC environment. The proposed NSA-ML presents a data authentication scheme with a novel encryption methodology to ensure data security. The experimental results show that the proposed NSA-ML outperforms the existing cloud security approaches by gaining an efficiency of 95.4%.
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
×
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