Collaborative Research: NeTS: Medium: Large Scale Analysis of Configurations and Management Practices in the Domain Name System

该项目将建立一个关于互联网域名系统（DNS）运行中使用的配置和管理做法的特点的广泛和基本的知识体系。大型公共数据集将用于分析和统计建模DNS内容。基于图形的分析将用于描述和量化注册商和域名运营商的活动。将为研究界开发工具，以支持DNS的机器学习应用程序、DNS中的异常检测以及“DNS测试”，从而能够生成合成DNS工作负载，用于模拟和基于实验室的DNS配置、管理和性能研究。科学部分将侧重于通过开发和应用图论、统计学和机器学习技术，对DNS的整体和部分进行经验表征。工程部分将使用实证分析的结果来确定操作实践，为工具调查提供信息，并推动有关DNS在实践中如何使用的新见解。该项目将提供有关如何改进DNS的新见解，以及用于导出和复制这些见解的新工具。该项目将有助于更深入地了解DNS内容的常见和不寻常之处。该项目的新技术和发现将带来更强大，更易于管理和性能更好的DNS，这有可能对整个社会产生积极影响。网络和数据科学课程的新材料将根据我们的研究开发。参与该项目的学生将获得指导和导师。研究成果将通过在学术会议和研讨会上发表的方式传播，所有软件和数据工件将向社区提供。项目网页将托管在https://macro-dns.github.io该网页将包括项目概述，项目参与者，最新成果和出版物的更新。它还将包括项目期间开发的所有软件和数据工件。该网页和相关的存储库将在项目结束后持续在github上提供。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This project will build a broad and fundamental body of knowledge on the characteristics of configurations and management practices used in the operation of the Internet’s Domain Name System (DNS). Large public datasets will be used to analyze and statistically model the DNS contents. Graph-based analysis will be used to characterize and quantify activities performed by registrars and domain operators. Tools will be developed for the research community to support machine learning applications for the DNS, anomaly detection in the DNS, and a “DNS Workbench” enabling synthetic DNS workload generation for simulation and laboratory-based research on DNS configuration, management and performance.This project consists of two complementary components. The scientific component will focus on empirical characterization of the DNS as a whole and in parts by developing and applying techniques from graph theory, statistics and machine learning. The engineering component will use findings from the empirical analysis to identify operational practices, to inform investigation of tools and to drive new insights about how the DNS is used in practice. The project will deliver new insights about how the DNS can be improved as well as new tools for deriving and replicating those insights.This project will contribute a deeper understanding of what is usual, and what is unusual, with respect to the contents of the DNS. New techniques and findings from this project will lead to a more robust, manageable, and better performing DNS, which has potential to positively impact society as a whole. New materials for networking and data science courses will be developed based on our research. The students who work on the project will receive guidance and mentorship. Research results will be disseminated by publishing in respected academic conferences and workshops, and all software and data artifacts will be made available to the community.The project webpage will be hosted at https://macro-dns.github.io The webpage will include an overview of the project, project participants, updates on latest results and publications. It will also include all software and data artifacts developed during the project. The webpage and associated repositories will be available on github on an on-going basis after the conclusion of the project.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.