发表机构
Johns Hopkins University; University of Maryland; San Diego State University（约翰斯·霍普金斯大学; 马里兰大学; 圣地亚哥州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对IPv6地址发现中TGA评估不一致的问题，提出可扩展框架6SEVEN，集成八种算法并标准化流程，揭示种子集等因素对性能影响显著。
AI 中文摘要
庞大、稀疏且往往短暂的IPv6地址空间使得发现活跃地址变得极具挑战性。为此，社区已开发了三十多种不同的IPv6目标生成算法（TGA）。TGA从种子（训练）数据集中学习地址结构，构建代表性模型，并生成候选地址。然而，现有文献采用了各种各样的输入种子、数据清洗方法和成功度量标准，阻碍了直接比较。为使TGA评估保持一致，我们提出了6SEVEN，一个可扩展的框架，将TGA作为插件托管，并将它们连接到共享的数据清洗、探测、去别名和结果制表组件。我们将八种流行的TGA移植为6SEVEN插件，并展示了一个受控案例研究。我们的结果表明，这些TGA的性能因独立于主算法的因素而显著变化，尤其是种子集组成、去别名和制表过程。我们观察到TGA性能的理想特性之间存在权衡，包括产出和探索。我们期望6SEVEN成为一个促进当前和未来TGA科学以及IPv6测量的社区资源。
英文摘要
The vast, sparsely populated, and often ephemeral IPv6 address space makes discovering active addresses challenging. In response, the community has developed over thirty different IPv6 Target Generation Algorithms (TGAs). TGAs learn addressing structure from seed (training) datasets, build a representative model, and generate candidate addresses. Unfortunately, the existing literature employs a wide variety of input seeds, data cleansing, and metrics of success that prevent ready comparison. Toward making TGA evaluation consistent, we present 6SEVEN, an extensible framework that hosts TGAs as plugins and links them to shared data cleaning, probing, dealiasing, and result tabulation components. We port as 6SEVEN plugins eight popular TGAs and demonstrate a controlled case study. Our results show that the performance of these TGAs varies substantially due to factors independent of the main algorithm---especially the seed set composition, dealiasing, and tabulation procedures. We observe tradeoffs between desirable features of TGA performance, including yield and exploration. We envision 6SEVEN as an enabling community resource to advance the science of current and future TGAs, and, by extension, IPv6 measurement.
CommentsAccepted at IMC 2026
