Hidden-SAGE: For the Inference of Complex Autonomous System Business Relationships Involving Hidden Links

Abstract

Routing security is a crucial aspect of internet security. The main issues involved in routing security include Border Gateway Protocol (BGP) route leak and prefix hijacking. Currently, numerous solutions have been proposed for these issues, and significant breakthroughs have been achieved. However, these methods focus on visible data on the internet, overlooking the limited coverage of vantage points (VPs). Existing research indicates that attackers can cleverly design route announcements to evade detection by route collectors, thus executing routing attacks. Furthermore, many current methods for detecting route leaks rely on traditional business relationships between Autonomous Systems (AS), but the modeling of traditional AS business relationships is increasingly challenging to comprehensively cover business interactions between ASs. Therefore, we have developed Hidden-SAGE. A framework that extends AS-level internet topology and extracts complex business relationships between ASs from limited routing information. Hidden-SAGE utilizes graph neural networks to discover hidden AS links and employs random forests to infer complex business relationships between links. It successfully reduces visual bias caused by uneven VP distribution and constructs a more comprehensive AS-level internet rich-text topology. Compared to advanced inference algorithms, Hidden-SAGE performs better across various metrics and imposes fewer restrictions on the inference target.