TY - GEN
T1 - Clusters in the expanse
T2 - 2018 Internet Measurement Conference, IMC 2018
AU - Gasser, Oliver
AU - Lone, Qasim
AU - Scheitle, Quirin
AU - Korczyński, Maciej
AU - Foremski, Pawel
AU - Strowes, Stephen D.
AU - Hendriks, Luuk
AU - Carle, Georg
N1 - Publisher Copyright:
© 2018 Copyright held by the owner/author(s). Publication rights licensed to ACM.
PY - 2018/10/31
Y1 - 2018/10/31
N2 - Network measurements are an important tool in understanding the Internet. Due to the expanse of the IPv6 address space, exhaustive scans as in IPv4 are not possible for IPv6. In recent years, several studies have proposed the use of target lists of IPv6 addresses, called IPv6 hitlists. In this paper, we show that addresses in IPv6 hitlists are heavily clustered. We present novel techniques that allow IPv6 hitlists to be pushed from quantity to quality. We perform a longitudinal active measurement study over 6 months, targeting more than 50 M addresses. We develop a rigorous method to detect aliased prefixes, which identifies 1.5 % of our prefixes as aliased, pertaining to about half of our target addresses. Using entropy clustering, we group the entire hitlist into just 6 distinct addressing schemes. Furthermore, we perform client measurements by leveraging crowdsourcing. To encourage reproducibility in network measurement research and to serve as a starting point for future IPv6 studies, we publish source code, analysis tools, and data.
AB - Network measurements are an important tool in understanding the Internet. Due to the expanse of the IPv6 address space, exhaustive scans as in IPv4 are not possible for IPv6. In recent years, several studies have proposed the use of target lists of IPv6 addresses, called IPv6 hitlists. In this paper, we show that addresses in IPv6 hitlists are heavily clustered. We present novel techniques that allow IPv6 hitlists to be pushed from quantity to quality. We perform a longitudinal active measurement study over 6 months, targeting more than 50 M addresses. We develop a rigorous method to detect aliased prefixes, which identifies 1.5 % of our prefixes as aliased, pertaining to about half of our target addresses. Using entropy clustering, we group the entire hitlist into just 6 distinct addressing schemes. Furthermore, we perform client measurements by leveraging crowdsourcing. To encourage reproducibility in network measurement research and to serve as a starting point for future IPv6 studies, we publish source code, analysis tools, and data.
KW - Aliasing
KW - Clustering
KW - Entropy
KW - Hitlist
KW - IPv6
UR - https://www.scopus.com/pages/publications/85058158791
U2 - 10.1145/3278532.3278564
DO - 10.1145/3278532.3278564
M3 - Conference contribution
AN - SCOPUS:85058158791
T3 - Proceedings of the ACM SIGCOMM Internet Measurement Conference, IMC
SP - 364
EP - 378
BT - IMC 2018 - Proceedings of the Internet Measurement Conference
PB - Association for Computing Machinery
Y2 - 31 October 2018 through 2 November 2018
ER -