Proceedings of the ACM Internet Measurement Conference最新文献_第4页

Demystifying the Messaging Platforms' Ecosystem Through the Lens of Twitter 从Twitter的视角揭秘信息平台的生态系统

Proceedings of the ACM Internet Measurement Conference Pub Date : 2020-10-27 DOI: 10.1145/3419394.3423651

M. Hoseini, P. Melo, Manoel Júnior, Fabrício Benevenuto, B. Chandrasekaran, A. Feldmann, Savvas Zannettou

{"title":"Demystifying the Messaging Platforms' Ecosystem Through the Lens of Twitter","authors":"M. Hoseini, P. Melo, Manoel Júnior, Fabrício Benevenuto, B. Chandrasekaran, A. Feldmann, Savvas Zannettou","doi":"10.1145/3419394.3423651","DOIUrl":"https://doi.org/10.1145/3419394.3423651","url":null,"abstract":"Online messaging platforms such as WhatsApp, Telegram, and Discord, each with hundreds of millions of users, are one of the dominant modes of communicating or interacting with one another. Despite the widespread use of public group chats, there exists no systematic or detailed characterization of these group chats. There is, more importantly, lack of a general understanding of how these (public) groups differ in characteristics and use across the different platforms. We also do not know whether the messaging platforms expose personally identifiable information, and we lack a comprehensive view of the privacy implications of leaks for the users. In this work, we address these gaps by analyzing the messaging platforms' ecosystem through the lens of a popular social media platform---Twitter. We search for WhatsApp, Telegram, and Discord group URLs posted on Twitter over a period of 38 days and amass a set of 351K unique group URLs. We analyze the content accompanied by group URLs on Twitter, finding interesting differences related to the topics of the groups across the multiple messaging platforms. By monitoring the characteristics of these groups, every day for more than a month, and, furthermore, by joining a subset of 616 groups across the different messaging platforms, we share key insights into the discovery of these groups via Twitter and reveal how these groups change over time. Finally, we analyze whether messaging platforms expose personally identifiable information. In this paper, we show that (a) Twitter is a rich source for discovering public groups in the different messaging platforms, (b) group URLs from messaging platforms are ephemeral, and (c) the considered messaging platforms expose personally identifiable information, with such leaks being more prevalent on WhatsApp than on Telegram and Discord.","PeriodicalId":255324,"journal":{"name":"Proceedings of the ACM Internet Measurement Conference","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123335881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 15

How the Internet reacted to Covid-19: A perspective from Facebook's Edge Network 互联网如何应对Covid-19:来自Facebook边缘网络的视角

Proceedings of the ACM Internet Measurement Conference Pub Date : 2020-10-27 DOI: 10.1145/3419394.3423621

T. Böttger, G. Ibrahim, Ben Vallis

引用次数: 66

Who Touched My Browser Fingerprint?: A Large-scale Measurement Study and Classification of Fingerprint Dynamics 谁碰了我的浏览器指纹?:指纹动力学的大规模测量研究与分类

Proceedings of the ACM Internet Measurement Conference Pub Date : 2020-10-27 DOI: 10.1145/3419394.3423614

Song Li, Yinzhi Cao

引用次数: 8

Hiding in Plain Site: Detecting JavaScript Obfuscation through Concealed Browser API Usage 隐藏在普通站点:通过隐藏浏览器API使用检测JavaScript混淆

Proceedings of the ACM Internet Measurement Conference Pub Date : 2020-10-27 DOI: 10.1145/3419394.3423616

Shaown Sarker, Jordan Jueckstock, A. Kapravelos

{"title":"Hiding in Plain Site: Detecting JavaScript Obfuscation through Concealed Browser API Usage","authors":"Shaown Sarker, Jordan Jueckstock, A. Kapravelos","doi":"10.1145/3419394.3423616","DOIUrl":"https://doi.org/10.1145/3419394.3423616","url":null,"abstract":"In this paper, we perform a large-scale measurement study of JavaScript obfuscation of browser APIs in the wild. We rely on a simple, but powerful observation: if dynamic analysis of a script's behavior (specifically, how it interacts with browser APIs) reveals browser API feature usage that cannot be reconciled with static analysis of the script's source code, then that behavior is obfuscated. To quantify and test this observation, we create a hybrid analysis platform using instrumented Chromium to log all browser API accesses by the scripts executed when a user visits a page. We filter the API access traces from our dynamic analysis through a static analysis tool that we developed in order to quantify how much and what kind of functionality is hidden on the web. When applying this methodology across the Alexa top 100k domains, we discover that 95.90% of the domains we successfully visited contain at least one script which invokes APIs that cannot be resolved from static analysis. We observe that eval is no longer the prominent obfuscation method on the web and we uncover families of novel obfuscation techniques that no longer rely on the use of eval.","PeriodicalId":255324,"journal":{"name":"Proceedings of the ACM Internet Measurement Conference","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131295801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 14

Lumos5G

Proceedings of the ACM Internet Measurement Conference Pub Date : 2020-10-27 DOI: 10.1145/3419394.3423629

Arvind Narayanan, Eman Ramadan, Rishabh Mehta, Xinyue Hu, Qingxu Liu, Rostand A. K. Fezeu, Udhaya Kumar Dayalan, Saurabh Verma, Peiqi Ji, Tao Li, Fengqi Qian, Zhi-Li Zhang

{"title":"Lumos5G","authors":"Arvind Narayanan, Eman Ramadan, Rishabh Mehta, Xinyue Hu, Qingxu Liu, Rostand A. K. Fezeu, Udhaya Kumar Dayalan, Saurabh Verma, Peiqi Ji, Tao Li, Fengqi Qian, Zhi-Li Zhang","doi":"10.1145/3419394.3423629","DOIUrl":"https://doi.org/10.1145/3419394.3423629","url":null,"abstract":"The emerging 5G services offer numerous new opportunities for networked applications. In this study, we seek to answer two key questions: i) is the throughput of mmWave 5G predictable, and ii) can we build \"good\" machine learning models for 5G throughput prediction? To this end, we conduct a measurement study of commercial mmWave 5G services in a major U.S. city, focusing on the throughput as perceived by applications running on user equipment (UE). Through extensive experiments and statistical analysis, we identify key UE-side factors that affect 5G performance and quantify to what extent the 5G throughput can be predicted. We then propose Lumos5G -- a composable machine learning (ML) framework that judiciously considers features and their combinations, and apply state-of-the-art ML techniques for making context-aware 5G throughput predictions. We demonstrate that our framework is able to achieve 1.37X to 4.84X reduction in prediction error compared to existing models. Our work can be viewed as a feasibility study for building what we envisage as a dynamic 5G throughput map (akin to Google traffic map). We believe this approach provides opportunities and challenges in building future 5G-aware apps.","PeriodicalId":255324,"journal":{"name":"Proceedings of the ACM Internet Measurement Conference","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114188758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Quantifying the Impact of Blocklisting in the Age of Address Reuse 量化黑名单在地址复用时代的影响

Proceedings of the ACM Internet Measurement Conference Pub Date : 2020-10-27 DOI: 10.1145/3419394.3423657

Sivaramakrishnan Ramanathan, Anushah Hossain, J. Mirkovic, Minlan Yu, Sadia Afroz

引用次数: 10

BGP Beacons, Network Tomography, and Bayesian Computation to Locate Route Flap Damping BGP信标、网络层析成像和贝叶斯计算定位路由Flap阻尼

Proceedings of the ACM Internet Measurement Conference Pub Date : 2020-10-27 DOI: 10.1145/3419394.3423624

Caitlin Gray, Clemens Mosig, R. Bush, C. Pelsser, M. Roughan, T. Schmidt, Matthias Wählisch

引用次数: 14

Accept the Risk and Continue: Measuring the Long Tail of Government https Adoption 接受风险并继续:衡量政府采用https的长尾效应

Proceedings of the ACM Internet Measurement Conference Pub Date : 2020-10-27 DOI: 10.1145/3419394.3423645

S. Singanamalla, E. Jang, Richard J. Anderson, Tadayoshi Kohno, Kurtis Heimerl

引用次数: 12

Unresolved Issues: Prevalence, Persistence, and Perils of Lame Delegations 未解决的问题:跛脚代表团的普遍性、持久性和危险性

Proceedings of the ACM Internet Measurement Conference Pub Date : 2020-10-27 DOI: 10.1145/3419394.3423623

Gautam Akiwate, M. Jonker, Raffaele Sommese, Ian D. Foster, G. Voelker, S. Savage, K. Claffy

引用次数: 19

Trufflehunter

Proceedings of the ACM Internet Measurement Conference Pub Date : 2020-10-27 DOI: 10.1145/3419394.3423640

A. Randall, Enze Liu, Gautam Akiwate, R. Padmanabhan, G. Voelker, S. Savage, Aaron Schulman

引用次数: 21