Linjiang Xie, Feilu Hang, W. Guo, Zhenhong Zhang, Hanruo Li
{"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}
引用次数: 2
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%.