Internet measurements are a crucial foundation of IPv6-related research. Due to the infeasibility of full address space scans for IPv6 however, those measurements rely on collections of reliably responsive, unbiased addresses, as provided e.g., by the IPv6 Hitlist service. Although used for various use cases, the hitlist provides an unfiltered list of responsive addresses, the hosts behind which can come from a range of different networks and devices, such as web servers, customer-premises equipment (CPE) devices, and Internet infrastructure. In this paper, we demonstrate the importance of tailoring hitlists in accordance with the research goal in question. By using PeeringDB we classify hitlist addresses into six different network categories, uncovering that 42% of hitlist addresses are in ISP networks. Moreover, we show the different behavior of those addresses depending on their respective category, e.g., ISP addresses exhibiting a relatively low lifetime. Furthermore, we analyze different Target Generation Algorithms (TGAs), which are used to increase the coverage of IPv6 measurements by generating new responsive targets for scans. We evaluate their performance under various conditions and find generated addresses to show vastly differing responsiveness levels for different TGAs.

翻译：互联网测量是IPv6相关研究的重要基础。然而，由于对IPv6地址空间进行全量扫描不可行，这些测量依赖于可靠响应、无偏见的地址集合，例如由IPv6活跃地址列表服务所提供的地址。尽管活跃地址列表被用于多种应用场景，但它仅提供了响应地址的未过滤列表，这些地址对应的主机可能来自不同类型的网络和设备，例如网络服务器、用户驻地设备（CPE）以及互联网基础设施。本文中，我们论证了根据具体研究目标定制活跃地址列表的重要性。通过利用PeeringDB，我们将活跃地址列表中的地址划分为六种不同的网络类别，发现其中42%的地址位于ISP网络中。此外，我们展示了这些地址根据其所属类别表现出的不同行为特征，例如ISP地址的存活时间相对较短。进一步地，我们分析了不同的目标生成算法，这些算法通过生成新的可响应扫描目标来提升IPv6测量的覆盖范围。我们在多种条件下评估了它们的性能，并发现不同目标生成算法生成的地址在响应水平上存在显著差异。