论文标题
生锈的簇?除尘IPv6研究基金会
Rusty Clusters? Dusting an IPv6 Research Foundation
论文作者
论文摘要
长期运行的IPv6 HITLIST服务是IPv6测量研究的重要基础。通过收集有价值的,公正的IPv6地址候选者并定期测试其响应能力,它有助于克服不可行的,完整的地址空间扫描。但是,互联网本身是一个快速变化的生态系统,可能会影响漫长的服务,潜在地诱使偏见和晦涩难懂的数据收集手段。经常进行分析,但还需要进行更新,以便为社区提供宝贵的服务。 在本文中,我们表明现有的列表受到中国大防火墙的影响很大,我们就响应迅速的地址的发展提供了清洁的看法。尽管累积的输入显示出对某些网络的偏见越来越大,但清洁的响应式地址集的分布良好,并且显示出稳定的增加。 尽管这是从IPv6 Hitlists中删除别名前缀的最佳实践,但我们表明这也可以删除主要的内容输送网络。迅速宣布的所有IPv6地址中,超过98%被标记为混蛋,并且排除了超过1000万个域的Cloudflare前缀。取决于列表使用列表,例如,更高层协议扫描,包括这些提供商的地址包含可能是有价值的。 最后,我们评估了不同的新地址候选资源,包括目标生成算法,以提高当前IPv6命中列表的覆盖范围。我们表明,不同方法的组合能够识别560万个新的,响应式的地址。这占174%的增加,并与当前的IPv6命中列表相结合,我们确定了880万个响应地址。
The long-running IPv6 Hitlist service is an important foundation for IPv6 measurement studies. It helps to overcome infeasible, complete address space scans by collecting valuable, unbiased IPv6 address candidates and regularly testing their responsiveness. However, the Internet itself is a quickly changing ecosystem that can affect longrunning services, potentially inducing biases and obscurities into ongoing data collection means. Frequent analyses but also updates are necessary to enable a valuable service to the community. In this paper, we show that the existing hitlist is highly impacted by the Great Firewall of China, and we offer a cleaned view on the development of responsive addresses. While the accumulated input shows an increasing bias towards some networks, the cleaned set of responsive addresses is well distributed and shows a steady increase. Although it is a best practice to remove aliased prefixes from IPv6 hitlists, we show that this also removes major content delivery networks. More than 98% of all IPv6 addresses announced by Fastly were labeled as aliased and Cloudflare prefixes hosting more than 10M domains were excluded. Depending on the hitlist usage, e.g., higher layer protocol scans, inclusion of addresses from these providers can be valuable. Lastly, we evaluate different new address candidate sources, including target generation algorithms to improve the coverage of the current IPv6 Hitlist. We show that a combination of different methodologies is able to identify 5.6M new, responsive addresses. This accounts for an increase by 174% and combined with the current IPv6 Hitlist, we identify 8.8M responsive addresses.