6Search: A reinforcement learning-based traceroute approach for efficient IPv6 topology discovery

Abstract

Topology discovery can infer the interconnection relationship between network entities. A complete network topology is of great significance for network security analysis, application research, etc. However, due to the huge address space and uneven distribution of active addresses in the IPv6 Internet, it is infeasible to use brute-force traceroute to discover the entire topology. To address this problem, we propose 6Search, a target generation method based on reinforcement learning algorithm for IPv6 topology discovery. 6Search first obtains the routeable BGP prefixes and then carries out traceroute in each (/32) prefix. The number of probes allocated is dynamically adjusted based on the results of previous scans. Using the reinforcement learning algorithm, 6Search allocates more probes to prefixes with more address discovery in each scan iteration. Real-world experiments demonstrate that 6Search has better performance in terms of discovery efficiency, which is 24.1%–139.8% improvement over the existing methods.