Minhyeok Kang (Seoul National University), Weitong Li (Virginia Tech), Roland van Rijswijk-Deij (University of Twente), Ted "Taekyoung" Kwon (Seoul National University), Taejoong Chung (Virginia Tech)
Border Gateway Protocol (BGP) provides a way of exchanging routing information to help routers construct their routing tables. However, due to the lack of security considerations, BGP has been suffering from vulnerabilities such as BGP hijacking attacks. To mitigate these issues, two data sources have been used, Internet Routing Registry (IRR) and Resource Public Key Infrastructure (RPKI), to provide reliable mappings between IP prefixes and their authorized Autonomous Systems (ASes). Each of the data sources, however, has its own limitations. IRR has been well-known for its stale Route objects with outdated AS information since network operators do not have enough incentives to keep them up to date, and RPKI has been slowly deployed due to its operational complexities. In this paper, we measure the prevalent inconsistencies between Route objects in IRR and ROA objects in RPKI. We next characterize inconsistent and consistent Route objects, respectively, by focusing on their BGP announcement patterns. Based on this insight, we develop a technique that identifies stale Route objects by leveraging a machine learning algorithm and evaluate its performance. From real trace-based experiments, we show that our technique can offer advantages against the status quo by reducing the percentage of potentially stale Route objects from 72% to 40% (of the whole IRR Route objects). In this way, we achieve 93% of the accuracy of validating BGP announcements while covering 87% of BGP announcements.