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

监控网络流量对互联网生态系统中的几个参与者至关重要，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。然而，由于越来越多地使用流量加密，以及越来越多的隐私问题，这种监控可能很快就需要使用有限的信息（匿名TCP/IP报头）来进行。这个项目考虑了一种基于学习的网络流量监控分类方法，它可以处理如此有限的信息，但仍能识别被下载的网页的类型。该项目通过概念验证证明，该方法在几个应用领域的潜在影响相当显著，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，该项目也指出了几个基本问题，这些问题挑战了在实践中实现这种影响的承诺。建议的研究将通过以下方式评估和解决这些风险：(i)为网页边界检测设计稳健的统计技术；（ii）对跨客户平台、客户地点和时间的网络流量和页面多样性进行广泛研究；（iii）识别稳定和稳健的交通特征，并利用这些特征研究几种当代标签方案的分类性能；（iv）在几个实际应用领域中整合和评估所提出的方法。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辩论。虽然大多数争论都是支持或反对允许深度数据包监控，但这个项目将重点转移到一个不同的范式上——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人员、监管机构、isp以及研究人员提供了一个很好的选择，他们可以了解客户偏好和应用的流行程度，而不依赖于缓慢和威胁隐私的技术。其次，该项目将成为培养在大数据实验、测量和科学分析方面受过训练的本科生和研究生的绝佳来源——这些技能对于许多参与大型数据集信息挖掘的联邦、商业和学术机构来说是无价的。第三，通过少数族裔的参与，该项目将有助于扩大计算机科学工作人员的多样性。最后，通过向初高中学生提供演示，特别是在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度。

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.