Passport: Enabling Accurate Country-Level Router Geolocation using Inaccurate Sources
This work addresses a critical need for policymakers and network operators to analyze international Internet traffic, with implications for security, privacy, and performance.
The paper tackles the problem of accurately mapping Internet routers to their countries, which is crucial for understanding cross-border traffic, by introducing Passport, a system that uses machine learning to combine multiple data sources and achieves substantially better performance than existing techniques.
When does Internet traffic cross international borders? This question has major geopolitical, legal and social implications and is surprisingly difficult to answer. A critical stumbling block is a dearth of tools that accurately map routers traversed by Internet traffic to the countries in which they are located. This paper presents Passport: a new approach for efficient, accurate country-level router geolocation and a system that implements it. Passport provides location predictions with limited active measurements, using machine learning to combine information from IP geolocation databases, router hostnames, whois records, and ping measurements. We show that Passport substantially outperforms existing techniques, and identify cases where paths traverse countries with implications for security, privacy, and performance.