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.

翻译：IPv6活跃地址的发现是IPv6网络测量中的关键挑战，其作为网络拓扑测量与安全分析等下游任务的前提条件。近年来随着IPv6网络的快速普及，众多研究者致力于提升IPv6扫描方法的命中率、效率与覆盖范围，并取得了显著进展。然而现有方法仍严重依赖种子地址，在无种子前缀中效能受限。为此，本文提出6Rover——一种面向无种子环境的强化学习活跃地址发现模型。为突破对种子地址的依赖，6Rover构建了反映网络管理员真实地址分配策略的泛化性更强的模式，从而避免种子前缀到无种子前缀的模式偏差迁移。继而，6Rover采用多臂老虎机模型优化模式在无种子空间应用时的探测资源分配。该模型将无种子空间最优模式发现难题建模为探索-利用困境，逐步揭示无种子空间中潜在的应用模式，无需将种子地址作为先验知识即可高效发现活跃地址。面向大规模无种子数据集的实验表明：在完全缺失种子地址先验知识的条件下，6Rover的命中率优于现有方法。真实网络环境中，6Rover在1亿预算规模的无种子空间中实现了5%-8%的命中率，较现有最先进方法提升约200%。