TY - GEN
T1 - Rusty Clusters? Dusting an IPv6 Research Foundation
AU - Zirngibl, Johannes
AU - Steger, Lion
AU - Sattler, Patrick
AU - Gasser, Oliver
AU - Carle, Georg
N1 - Publisher Copyright:
© 2022 Copyright held by the owner/author(s).
PY - 2022/10/25
Y1 - 2022/10/25
N2 - 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 long-running 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 10 M 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 5.6 M new, responsive addresses. This accounts for an increase by 174 % and combined with the current IPv6 Hitlist, we identify 8.8 M responsive addresses.
AB - 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 long-running 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 10 M 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 5.6 M new, responsive addresses. This accounts for an increase by 174 % and combined with the current IPv6 Hitlist, we identify 8.8 M responsive addresses.
KW - Aliased Prefixes
KW - Hitlist
KW - IPv6
KW - Target Generation
UR - http://www.scopus.com/inward/record.url?scp=85141392256&partnerID=8YFLogxK
U2 - 10.1145/3517745.3561440
DO - 10.1145/3517745.3561440
M3 - Conference contribution
AN - SCOPUS:85141392256
T3 - Proceedings of the ACM SIGCOMM Internet Measurement Conference, IMC
SP - 395
EP - 409
BT - IMC 2022 - Proceedings of the 2022 ACM Internet Measurement Conference
PB - Association for Computing Machinery
T2 - 22nd ACM Internet Measurement Conference, IMC 2022
Y2 - 25 October 2022 through 27 October 2022
ER -