CAREER: Quality of Experience and Network Management in the Encrypted Internet

职业：加密互联网中的体验质量和网络管理

基本信息

批准号：
2103038
负责人：
Zubair Shafiq
金额：
$ 50万
依托单位：
University of California-Davis
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-10-01 至 2024-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2103038&HistoricalAwards=false
关键词：
CAREER Quality Experience Network Management

项目摘要

While encryption is essential for security and privacy, it limits the ability of content providers (e.g. Netflix and YouTube) and mobile network operators (e.g. AT&T and Verizon) to effectively and efficiently deliver content to users. First, encryption limits the ability of content providers to maintain effective control over modern applications such as augmented reality or virtual reality (AR/VR) video streaming. Second, encryption limits the ability of mobile network operators to efficiently share the scarce radio spectrum among their many users. These issues can significantly degrade users' Quality of Experience (or QoE). The goal of this project is to improve QoE of modern applications in the increasingly encrypted Internet. This project will investigate implicit and explicit cooperation between network operators and content providers for QoE and network management in the encrypted Internet. First, the project will investigate implicit cooperation between network operators and content providers through machine learning based analysis of encrypted traffic. Second, the project will investigate in-band and out-of-band protocols and API frameworks for explicit information exchange between network operators and content providers. The project investigates a spectrum of innovative approaches for cooperative QoE and network management, ranging from clean slate approaches to the ones that are backward compatible and incrementally deployable. The proposed research will help improve Internet experience of modern high-throughput low-delay applications such as AR/VR video streaming, especially for users in underserved areas with poor Internet connectivity. The project will also engage undergraduates and under-represented minority students.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

虽然加密对于安全和隐私至关重要，但它限制了内容提供商（例如 Netflix 和 YouTube）和移动网络运营商（例如 AT&T 和 Verizon）向用户有效且高效地交付内容的能力。首先，加密限制了内容提供商对增强现实或虚拟现实 (AR/VR) 视频流等现代应用程序保持有效控制的能力。其次，加密限制了移动网络运营商在众多用户之间有效共享稀缺无线电频谱的能力。这些问题会显着降低用户的体验质量 (QoE)。该项目的目标是提高日益加密的互联网中现代应用程序的 QoE。该项目将研究网络运营商和内容提供商之间在加密互联网中的 QoE 和网络管理方面的隐式和显式合作。首先，该项目将通过基于机器学习的加密流量分析来调查网络运营商和内容提供商之间的隐性合作。其次，该项目将研究带内和带外协议以及 API 框架，以实现网络运营商和内容提供商之间的显式信息交换。该项目研究了一系列用于协作 QoE 和网络管理的创新方法，从干净的方法到向后兼容和增量部署的方法。拟议的研究将有助于改善现代高吞吐量低延迟应用（例如 AR/VR 视频流）的互联网体验，特别是对于互联网连接较差的服务欠缺地区的用户。该项目还将吸引本科生和代表性不足的少数族裔学生。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

期刊论文数量（8）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

AutoFR: Automated Filter Rule Generation for Adblocking

AutoFR：自动生成广告拦截过滤规则

DOI：
发表时间：
2023-01
期刊：
USENIX Security Symposium
影响因子：
0
作者：
Hieu Le;Salma Elmalaki;Athina Markopoulou;Zubair Shafiq
通讯作者：
Zubair Shafiq

WebGraph: Capturing Advertising and Tracking Information Flows for Robust Blocking

WebGraph：捕获广告并跟踪信息流以实现强大的阻止

DOI：
发表时间：
2022-01
期刊：
USENIX Security Symposium
影响因子：
0
作者：
Sandra, Siby;Umar, Iqbal;Steven, Englehardt;Zubair, Shafiq;Carmela, Troncoso
通讯作者：
Carmela, Troncoso

FP-Radar: Longitudinal Measurement and Early Detection of Browser Fingerprinting.

FP-Radar：浏览器指纹的纵向测量和早期检测。