A. Shuba, Anh Le, Minas Gjoka, Janus Varmarken, Simon Langhoff, A. Markopoulou
{"title":"AntMonitor:移动设备的网络流量监控和实时隐私泄露预防","authors":"A. Shuba, Anh Le, Minas Gjoka, Janus Varmarken, Simon Langhoff, A. Markopoulou","doi":"10.1145/2801694.2801707","DOIUrl":null,"url":null,"abstract":"Mobile devices play an essential role in the Internet today, and there is an increasing interest in using them as a vantage point for network measurement from the edge. At the same time, these devices store personal, sensitive information, and there is a growing number of applications that leak it. We propose AntMonitor -- the first system of its kind that supports (i) collection of large-scale, semantic-rich network traffic in a way that respects users' privacy preferences and (ii) detection and prevention of leakage of private information in real time. The first property makes AntMonitor a powerful tool for network researchers who want to collect and analyze large-scale yet fine-grained mobile measurements. The second property can work as an incentive for using AntMonitor and contributing data for analysis. As a proof-of-concept, we have developed a prototype of AntMonitor, deployed it to monitor 9 users for 2 months, and collected and analyzed 20 GB of mobile data from 151 applications. Preliminary results show that fine-grained data collected from AntMonitor could enable application classification with higher accuracy than state-of-the-art approaches. In addition, we demonstrated that AntMonitor could help prevent several apps from leaking private information over unencrypted traffic, including phone numbers, emails, and device identifiers.","PeriodicalId":62224,"journal":{"name":"世界中学生文摘","volume":"10 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2015-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"AntMonitor: Network Traffic Monitoring and Real-Time Prevention of Privacy Leaks in Mobile Devices\",\"authors\":\"A. Shuba, Anh Le, Minas Gjoka, Janus Varmarken, Simon Langhoff, A. Markopoulou\",\"doi\":\"10.1145/2801694.2801707\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Mobile devices play an essential role in the Internet today, and there is an increasing interest in using them as a vantage point for network measurement from the edge. At the same time, these devices store personal, sensitive information, and there is a growing number of applications that leak it. We propose AntMonitor -- the first system of its kind that supports (i) collection of large-scale, semantic-rich network traffic in a way that respects users' privacy preferences and (ii) detection and prevention of leakage of private information in real time. The first property makes AntMonitor a powerful tool for network researchers who want to collect and analyze large-scale yet fine-grained mobile measurements. The second property can work as an incentive for using AntMonitor and contributing data for analysis. As a proof-of-concept, we have developed a prototype of AntMonitor, deployed it to monitor 9 users for 2 months, and collected and analyzed 20 GB of mobile data from 151 applications. Preliminary results show that fine-grained data collected from AntMonitor could enable application classification with higher accuracy than state-of-the-art approaches. In addition, we demonstrated that AntMonitor could help prevent several apps from leaking private information over unencrypted traffic, including phone numbers, emails, and device identifiers.\",\"PeriodicalId\":62224,\"journal\":{\"name\":\"世界中学生文摘\",\"volume\":\"10 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-09-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"世界中学生文摘\",\"FirstCategoryId\":\"90\",\"ListUrlMain\":\"https://doi.org/10.1145/2801694.2801707\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"世界中学生文摘","FirstCategoryId":"90","ListUrlMain":"https://doi.org/10.1145/2801694.2801707","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
AntMonitor: Network Traffic Monitoring and Real-Time Prevention of Privacy Leaks in Mobile Devices
Mobile devices play an essential role in the Internet today, and there is an increasing interest in using them as a vantage point for network measurement from the edge. At the same time, these devices store personal, sensitive information, and there is a growing number of applications that leak it. We propose AntMonitor -- the first system of its kind that supports (i) collection of large-scale, semantic-rich network traffic in a way that respects users' privacy preferences and (ii) detection and prevention of leakage of private information in real time. The first property makes AntMonitor a powerful tool for network researchers who want to collect and analyze large-scale yet fine-grained mobile measurements. The second property can work as an incentive for using AntMonitor and contributing data for analysis. As a proof-of-concept, we have developed a prototype of AntMonitor, deployed it to monitor 9 users for 2 months, and collected and analyzed 20 GB of mobile data from 151 applications. Preliminary results show that fine-grained data collected from AntMonitor could enable application classification with higher accuracy than state-of-the-art approaches. In addition, we demonstrated that AntMonitor could help prevent several apps from leaking private information over unencrypted traffic, including phone numbers, emails, and device identifiers.