{"title":"在加密通信中识别web浏览器","authors":"Jiangmin Yu, Eric Chan-Tin","doi":"10.1145/2665943.2665968","DOIUrl":null,"url":null,"abstract":"Webbrowser fingerprinting is a powerful tool to identify an Internet end-user. Previous research has shown that the information extracted from webbrowsers can uniquely identify an end-user. To collect webbrowser specific information, intentional JavaScript codes are embedded in web pages. In this paper, we show that fingerprinting characteristics of a webbrowser can also be collected by solely checking the network traffic data generated when browsing a website. We collect network traffic data generated by browsing the homepage of the most popular websites. Based on this data, we show that the browser fingerprinting characteristics can be inferred with high accuracy. Among these characteristics, type of webbrowser can be identified with over 70\\% accuracy rate. Usage status of popular plug-ins like JavaScript and flash can also be accurately identified.","PeriodicalId":408627,"journal":{"name":"Proceedings of the 13th Workshop on Privacy in the Electronic Society","volume":"33 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Identifying Webbrowsers in Encrypted Communications\",\"authors\":\"Jiangmin Yu, Eric Chan-Tin\",\"doi\":\"10.1145/2665943.2665968\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Webbrowser fingerprinting is a powerful tool to identify an Internet end-user. Previous research has shown that the information extracted from webbrowsers can uniquely identify an end-user. To collect webbrowser specific information, intentional JavaScript codes are embedded in web pages. In this paper, we show that fingerprinting characteristics of a webbrowser can also be collected by solely checking the network traffic data generated when browsing a website. We collect network traffic data generated by browsing the homepage of the most popular websites. Based on this data, we show that the browser fingerprinting characteristics can be inferred with high accuracy. Among these characteristics, type of webbrowser can be identified with over 70\\\\% accuracy rate. Usage status of popular plug-ins like JavaScript and flash can also be accurately identified.\",\"PeriodicalId\":408627,\"journal\":{\"name\":\"Proceedings of the 13th Workshop on Privacy in the Electronic Society\",\"volume\":\"33 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-11-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 13th Workshop on Privacy in the Electronic Society\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2665943.2665968\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 13th Workshop on Privacy in the Electronic Society","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2665943.2665968","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Identifying Webbrowsers in Encrypted Communications
Webbrowser fingerprinting is a powerful tool to identify an Internet end-user. Previous research has shown that the information extracted from webbrowsers can uniquely identify an end-user. To collect webbrowser specific information, intentional JavaScript codes are embedded in web pages. In this paper, we show that fingerprinting characteristics of a webbrowser can also be collected by solely checking the network traffic data generated when browsing a website. We collect network traffic data generated by browsing the homepage of the most popular websites. Based on this data, we show that the browser fingerprinting characteristics can be inferred with high accuracy. Among these characteristics, type of webbrowser can be identified with over 70\% accuracy rate. Usage status of popular plug-ins like JavaScript and flash can also be accurately identified.
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