Supervision : Olivier FOURMAUX

Co-supervision : FRIEDMAN Timur

Internet-Scale Route Tracing Capture and Analysis

The Internet is one of the most remarkable human creations, enabling communication among about two thirds of the global population. This network of networks spans the entire globe and is managed in a highly decentralized way, making it impossible to fully comprehend at IP-level. Nonetheless, for over two decades, researchers have been devising new techniques, developing new tools, and creating new platforms to capture and provide more precise and comprehensive maps of the Internet’s topology. These efforts support network operators in the industry and other researchers in improving core features of the Internet such as its connectivity, performance, security, or neutrality.
This thesis presents new contributions that improve the scalability of Internet topology measurement. It introduces a state-of-the-art measurement platform that enables the use of high-speed probing techniques for IP route tracing at Internet scale, as well as a reinforcement learning approach to maximize the discovery of the Internet topology. Because the analysis of the route tracing data collected requires additional metadata, the evolution of IP address geolocation over a 10-year period in a widely used proprietary database is examined, and lessons are provided to avoid biases in studies using this database. Finally, a large-scale analysis framework is developed to effectively utilize the large number of collected data and augmented metadata from other sources, such as IP address geolocation, to produce insightful studies at the Internet scale.
This work aims to considerably improve the study of the Internet topology by providing tools to collect and analyze large amounts of Internet topology data. This will allow researchers to better understand how the Internet is structured and how it evolves over time, leading to a more comprehensive understanding of this complex system.

2020-2023 Publications