  The discovery of active IPv6 addresses represents a pivotal challenge in IPv6
network survey, as it is a prerequisite for downstream tasks such as network
topology measurements and security analysis. With the rapid spread of IPv6
networks in recent years, many researchers have focused on improving the hit
rate, efficiency, and coverage of IPv6 scanning methods, resulting in
considerable advancements. However, existing approaches remain heavily
dependent on seed addresses, thereby limiting their effectiveness in unseeded
prefixes. Consequently, this paper proposes 6Rover, a reinforcement
learning-based model for active address discovery in unseeded environments. To
overcome the reliance on seeded addresses, 6Rover constructs patterns with
higher generality that reflects the actual address allocation strategies of
network administrators, thereby avoiding biased transfers of patterns from
seeded to unseeded prefixes. After that, 6Rover employs a multi-armed bandit
model to optimize the probing resource allocation when applying patterns to
unseeded spaces. It models the challenge of discovering optimal patterns in
unseeded spaces as an exploration-exploitation dilemma, and progressively
uncover the potential patterns applied in unseeded spaces, leading to the
efficient discovery of active addresses without seed address as the prior
knowledge. Experiments on large-scale unseeded datasets show that 6Rover has a
higher hit rate than existing methods in the absence of any seed addresses as
prior knowledge. In real network environments, 6Rover achieved a 5% - 8% hit
rate in seedless spaces with 100 million budget scale, representing an
approximate 200\% improvement over the existing state-of-the-art methods.
