RoLL+: Real-Time and Accurate Route Leak Locating With AS Triplet Features at Scale

Abstract

Border Gateway Protocol (BGP) is the only inter-domain routing protocol that plays an important role on the Internet. However, BGP suffers from route leaks, which can cause serious security threats. To mitigate the effects of route leaks, accurate and timely route leak locating is of great importance. Prior studies leverage AS business relationships to locate route leaks in real time. However, they fail to achieve high locating accuracy. Recent studies apply machine learning to accurately detect route leaks from statistical features of massive BGP messages. Nevertheless, they have high detection latency and cannot further locate route leaks. In this paper, we propose a real-time and accurate route leak locating system named RoLL+. It leverages distinctive AS triplet features to accurately locate AS triplets with route leaks from each BGP message in real time. Considering that RoLL+ may receive a substantial volume of BGP update messages per second, we integrate a cache-like design and a lazy update mechanism into the system to effectively identify route leaks at scale. Our experimental results on real-world BGP route leak data demonstrate that it can achieve 92% locating accuracy with less than 1 ms locating latency. Furthermore, the results show that RoLL+ can process over 7,000 AS triplets per second, meeting real-world throughput requirements.