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

监控网络流量对于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，由于流量加密的使用越来越多，以及越来越多的隐私问题，这种监控可能很快就需要只使用有限的信息（匿名TCP/IP报头）进行。该项目考虑了一种基于学习的分类方法，用于网络流量监测，该方法可以处理这种有限的信息，但可以识别正在下载的网页类型。该项目通过概念验证论证了这种方法在多个应用领域的潜在影响是相当显著的，包括用户内容偏好的分析、用户导航行为的分析以及识别视频流和移动的设备的使用。然而，该项目也确定了在实践中挑战这种影响的承诺的几个基本问题。拟议的研究将评估和解决这些风险：（一）设计强大的统计技术，网页边界检测;（二）进行广泛的研究，网络流量和网页多样性的客户端平台，客户端的位置，和时间;（三）确定稳定和强大的流量功能，并使用这些来研究性能的分类几个当代标签计划;以及（iv）在几个实际应用领域中结合和评估所提出的方法。更广泛的影响：该项目预计将对几个领域产生变革性的影响。 第一个影响是目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，但该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。 建议的分类方法为网络管理员，监管机构，ISP以及研究人员提供了一个很好的替代方案，他们可以了解客户端的偏好和应用程序的流行程度，而不依赖于缓慢和隐私威胁的技术。其次，该项目将成为本科生和研究生的一个很好的来源，他们接受了大数据实验、测量和科学分析方面的培训--这些技能对许多参与挖掘大型数据集信息的联邦、商业和学术机构来说是无价的。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进行演示，特别是就大多数人都熟悉和喜爱的网页浏览主题进行宣传，该项目将有助于增加社区对科学和技术的参与。

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.