This project will investigate techniques for measuring the contents of the Domain Name System (DNS) toward the goal of producing a comprehensive census of all record types. Our team will focus on developing methods for generating domain names that will be used in queries of the global DNS. Strategies for name generation that will be considered include making use of underutilized data sources and machine learning techniques including Large Language Models. New metrics and a system called EverDNS will be developed to evaluate the effectiveness of the name generation methods by sending high-speed queries to the DNS from different Internet vantage points.
The first component of this project will investigate techniques for assembling the largest-feasible collection of domain names that will be used as targets to query the global DNS. The second component will assess these techniques by developing EverDNS, which will include a prototype data collector and a centralized controller. EverDNS will build on state-of-the-art DNS measurement tools and augment their methods to capture the greatest amount of information within a limited measurement budget. This will enable our team to deliver new insights about how to assemble a comprehensive census of the DNS and new data sets for research that will be made available to the community.
The techniques we create will enable generation of data sets that will advance scientific understanding of the DNS. It is expected that the insights gained through this research will enable methods for ensuring a more robust, manageable, and better performing DNS. Given the critical role of DNS in the Internet, this project has the potential to positively impact society as a whole. 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.