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The discovery of active IPv6 addresses represents a pivotal challenge in IPv6network survey, as it is a prerequisite for downstream tasks such as networktopology measurements and security analysis. With the rapid spread of IPv6networks in recent years, many researchers have focused on improving the hitrate, efficiency, and coverage of IPv6 scanning methods, resulting inconsiderable advancements. However, existing approaches remain heavilydependent on seed addresses, thereby limiting their effectiveness in unseededprefixes. Consequently, this paper proposes 6Rover, a reinforcementlearning-based model for active address discovery in unseeded environments. Toovercome the reliance on seeded addresses, 6Rover constructs patterns withhigher generality that reflects the actual address allocation strategies ofnetwork administrators, thereby avoiding biased transfers of patterns fromseeded to unseeded prefixes. After that, 6Rover employs a multi-armed banditmodel to optimize the probing resource allocation when applying patterns tounseeded spaces. It models the challenge of discovering optimal patterns inunseeded spaces as an exploration-exploitation dilemma, and progressivelyuncover the potential patterns applied in unseeded spaces, leading to theefficient discovery of active addresses without seed address as the priorknowledge. Experiments on large-scale unseeded datasets show that 6Rover has ahigher hit rate than existing methods in the absence of any seed addresses asprior knowledge. In real network environments, 6Rover achieved a 5% - 8% hitrate in seedless spaces with 100 million budget scale, representing anapproximate 200\% improvement over the existing state-of-the-art methods.

6Rover: Leveraging Reinforcement Learning-based Address Pattern Mining Approach for Discovering Active Targets in IPv6 Unseeded Space