{"title":"反馈熵:一种检测云环境下信任计算不公平评级攻击的新度量","authors":"Manel Mrabet, Yosra Ben Saied, L. Saïdane","doi":"10.1109/Trustcom/BigDataSE/ICESS.2017.282","DOIUrl":null,"url":null,"abstract":"Trust management systems provide a means for trustworthy interactions in cloud environments. However, trust establishment could be compromised when malicious cloud users intentionally provide unfair feedbacks to decrease the reputation of some cloud providers or to benefit others. In this paper, we define \"Feedback Entropy\" as a newmetric to detect unfair rating attacks. As such, we propose a new detection system able to detect unfair rating attacks by monitoring users' feedbacks during short periods of time. Our proposed approach is designed to detect rapidly such attacks at the point in time they appear and to scale effectively with the increase of the number of feedbacks. Experimental results prove the advantages of the introduced metric and the good performance of the proposed detection system.","PeriodicalId":170253,"journal":{"name":"2017 IEEE Trustcom/BigDataSE/ICESS","volume":"8 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Feedback Entropy: A New Metric to Detect Unfair Rating Attacks for Trust Computing in Cloud Environments\",\"authors\":\"Manel Mrabet, Yosra Ben Saied, L. Saïdane\",\"doi\":\"10.1109/Trustcom/BigDataSE/ICESS.2017.282\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Trust management systems provide a means for trustworthy interactions in cloud environments. However, trust establishment could be compromised when malicious cloud users intentionally provide unfair feedbacks to decrease the reputation of some cloud providers or to benefit others. In this paper, we define \\\"Feedback Entropy\\\" as a newmetric to detect unfair rating attacks. As such, we propose a new detection system able to detect unfair rating attacks by monitoring users' feedbacks during short periods of time. Our proposed approach is designed to detect rapidly such attacks at the point in time they appear and to scale effectively with the increase of the number of feedbacks. Experimental results prove the advantages of the introduced metric and the good performance of the proposed detection system.\",\"PeriodicalId\":170253,\"journal\":{\"name\":\"2017 IEEE Trustcom/BigDataSE/ICESS\",\"volume\":\"8 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE Trustcom/BigDataSE/ICESS\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/Trustcom/BigDataSE/ICESS.2017.282\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE Trustcom/BigDataSE/ICESS","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/Trustcom/BigDataSE/ICESS.2017.282","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Feedback Entropy: A New Metric to Detect Unfair Rating Attacks for Trust Computing in Cloud Environments
Trust management systems provide a means for trustworthy interactions in cloud environments. However, trust establishment could be compromised when malicious cloud users intentionally provide unfair feedbacks to decrease the reputation of some cloud providers or to benefit others. In this paper, we define "Feedback Entropy" as a newmetric to detect unfair rating attacks. As such, we propose a new detection system able to detect unfair rating attacks by monitoring users' feedbacks during short periods of time. Our proposed approach is designed to detect rapidly such attacks at the point in time they appear and to scale effectively with the increase of the number of feedbacks. Experimental results prove the advantages of the introduced metric and the good performance of the proposed detection system.