NeTS: Small: Web Traffic Monitoring Using Anonymized TCP/IP Traces

NeTS：小型：使用匿名 TCP/IP 跟踪进行 Web 流量监控

• 批准号: 1526268
• 负责人: Jasleen Kaur Sahni
• 金额: $ 50万
• 依托单位: University of North Carolina at Chapel Hill
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-10-01 至 2020-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1526268&HistoricalAwards=false
• 关键词: NeTS; Small; Web; Traffic; Monitoring

Monitoring web traffic is critical to several players in the Internet eco-system--including Internet Service Providers, regulators, network administrators, as well as researchers. Because of the increasing use of traffic encryption, as well as growing privacy concerns, however, such monitoring may soon need to be conducted using only limited information (anonymized TCP/IP headers). This project considers a learning-based classification approach for web traffic monitoring, that can work with such limited information and yet identify the type of web page being downloaded. The project argues through a proof of concept that the potential impact of this approach is quite significant in several application domains, including profiling of user content preference, profiling of user navigation behavior, as well as identifying the usage of video streaming and mobile devices.The project, however, also identifies several fundamental issues that challenge the promise to deliver this impact in practice. The proposed research will evaluate and address these risks by: (i) designing robust statistical techniques for web page boundary detection; (ii) conducting an extensive study of web traffic and page diversity across client platforms, client locations, and time; (iii) identifying stable and robust traffic features and using these to study the performance of classification for several contemporary labeling schemes; and (iv) incorporating and evaluating the proposed approach within several real-world application domains.Broader Impacts: This project is expected to have a transformative impact on several domains. The first impact is on the current debate on privacy-violating monitoring techniques. While most arguments are either for or against allowing deep packet monitoring, this project shifts the focus to a different paradigm -- that of simultaneously achieving a balance between monitoring and privacy goals. The proposed classification approach provides a great alternative to network managers, regulators, ISPs, as well as researchers, who can understand client preferences and application prevalence, without relying on slow and privacy-threatening techniques. Second, the project will be an excellent source of undergraduate and graduate students trained in experimentation, measurements, and scientific analysis of big data---skills that are invaluable for many federal, commercial and academic institutions that are involved in mining for information in large data-sets. Third, through involvement of minorities, the project will help broaden the diversity of the Computer-Science work force. Finally, through outreach using demos to middle- and high-schoolers, especially on a topic related to web browsing that is near and dear to most, the project will help increase community engagement with science and technology.

监控网络流量对于互联网生态系统中的多个参与者（包括互联网服务提供商、监管机构、网络管理员以及研究人员）至关重要。然而，由于越来越多地使用流量加密以及日益增长的隐私问题，此类监控可能很快就需要仅使用有限的信息（匿名 TCP/IP 标头）进行。该项目考虑了一种基于学习的网络流量监控分类方法，该方法可以处理如此有限的信息，并识别正在下载的网页类型。该项目通过概念验证证明，这种方法的潜在影响在多个应用领域非常重要，包括用户内容偏好分析、用户导航行为分析，以及识别视频流和移动设备的使用情况。然而，该项目还确定了几个基本问​​题，这些问题对在实践中实现这一影响的承诺提出了挑战。拟议的研究将通过以下方式评估和解决这些风险：（i）设计用于网页边界检测的稳健统计技术； (ii) 对跨客户端平台、客户端位置和时间的网络流量和页面多样性进行广泛研究； (iii) 识别稳定且稳健的流量特征，并利用这些特征来研究几种当代标签方案的分类性能；更广泛的影响：该项目预计将对多个领域产生变革性影响。 第一个影响是当前关于侵犯隐私的监控技术的争论。虽然大多数论点要么支持要么反对允许深度数据包监控，但该项目将焦点转移到了不同​​的范式上——同时实现监控和隐私目标之间的平衡。 所提出的分类方法为网络管理者、监管者、ISP 以及研究人员提供了一个很好的替代方案，他们可以了解客户偏好和应用程序流行程度，而无需依赖缓慢且威胁隐私的技术。其次，该项目将成为受过大数据实验、测量和科学分析培训的本科生和研究生的绝佳来源——这些技能对于许多涉及大规模信息挖掘的联邦、商业和学术机构来说是无价的。数据集。第三，通过少数民族的参与，该项目将有助于扩大计算机科学劳动力的多样性。最后，通过向中学生和高中生使用演示进行推广，特别是在大多数人都关心的与网络浏览相关的主题上，该项目将有助于提高社区对科学和技术的参与。