This repository provides the source code of 6Massive, a large-scale target generation algorithm. The design and methodology of 6Massive are comprehensively presented in our INFOCOM 2026 paper "6Massive: An Efficient IPv6 Large-Scale Target Generation Framework". 6Massive can predict 1.644 billion IPv6 active addresses in 1.76 days.
6Massive used the Hitlist of July 20, 2024. The latest Hitlist can be obtained in the following way.
curl https://alcatraz.net.in.tum.de/ipv6-hitlist-service/open/responsive-addresses.txt.xz --output responsive-addresses.txt.xz
xz -d responsive-addresses.txt.xz
go mod init
go mod tidy
go build
Multiple seed address sets of the same scale can be obtained through expansion strategy.
The -S parameter is the seed address source, such as Hitlist. The -num parameter is the number of seed address sets. -size is the size of the seed address set, that is, the number of seed addresses contained. -prefix is the prefix of the file name of the seed address set.
./6Massive -o expand -S hitlist_2024_07_20 -num 10 -size 100000 -prefix random100K
Execute MDHC strategy on the input set of seed addresses, construct 4 IPv6 address space trees, and generate low-dimensional address patterns and high-dimensional address patterns.
The -s parameter represents the seed address set of the input, the -t parameter represents the target address generated in the low-dimensional pattern space, and the -h parameter represents the high-dimensional patterns.
./6Massive -o MDHC -s random100K1 -t targetAddress -h highDimPattern
Based on actual bandwidth, utilize the asynchronous scanning tool ZMap to scan IPv6 target addresses and collect responsive IPv6 active addresses.
sudo zmap --probe-module=icmp6_echoscan --ipv6-target-file=targetAddressFile --output-file=activeAddressFile --ipv6-source-ip=(Machine IPv6 address) --bandwidth=10M(Actual bandwidth) --cooldown-time=4
- Format active addresses.
The -a parameter represents the IPv6 active address probed in the low-dimensional pattern space, and the -t parameter represents the file output after formatting.
./6Massive -o convert -a activeAddress -t targetAddress
- Execute feedback strategy.
The feedback strategy is used to filter out active high-dimensional patterns based on the active addresses in the low-dimensional pattern space, and generate IPv6 target addresses in these pattern spaces.
The -a parameter is the active address after formatting, -h is the high-dimensional address pattern file, -p is the pattern dimension of the high-dimensional address pattern, and -t is the target address to be output
./6Massive -o feedback -a activeAddress -h highDimPattern5 -p 5 -t targetAddress
Execute the Sampling-based IPv6 seed address expansion strategy to generate more target addresses.
./6Massive -o extremeExpansion -S random1M1_random1M2_random1M3_random1M4_random1M5_random1M6_random1M7_random1M8_random1M9_random1M10 -t targetAddress
Durumeric, Z., Wustrow, E. & Halderman, J. A. ZMap: Fast Internet-wide scanning and its security applications. In 22nd
USENIX Security Symposium (USENIX Security 13), 605–620 (2013). https://www.usenix.org/conference/usenixsecurity13/
technical-sessions/paper/durumeric.
ZMap. ZMap Github code. https://github.com/tumi8/zmap.
Murdock, A., Li, F., Bramsen, P., Durumeric, Z. & Paxson, V. Target generation for Internet-wide IPv6 scanning. In
Proceedings ofthe 2017 Internet Measurement Conference, 242–253, DOI: 10.1145/3131365.3131405 (2017).
Liu, Z., Xiong, Y., Liu, X., Xie, W. & Zhu, P. 6Tree: Efficient dynamic discovery of active addresses in the IPv6 address
space. Comput. Networks 155, 31–46, DOI: 10.1016/j.comnet.2019.03.010 (2019).
Hou, B., Cai, Z., Wu, K., Su, J. & Xiong, Y. 6Hit: A reinforcement learning-based approach to target generation for
Internet-wide IPv6 scanning. In IEEE INFOCOM 2021-IEEE Conference on Computer Communications, 1–10, DOI:
10.1109/INFOCOM42981.2021.9488794 (2021).
Yang, T., Cai, Z., Hou, B. & Zhou, T. 6Forest: An ensemble learning-based approach to target generation for Internet-
wide IPv6 scanning. In IEEE INFOCOM 2022-IEEE Conference on Computer Communications, 1679–1688, DOI:
10.1109/INFOCOM48880.2022.9796925 (2022).
Hou, B., Cai, Z., Wu, K., Yang, T. & Zhou, T. Search in the expanse: Towards active and global IPv6 hitlists. In IEEE
INFOCOM 2023-IEEE Conference on Computer Communications, 1–10, DOI: 10.1109/INFOCOM53939.2023.10229089
(2023).
Hou, B., Cai, Z., Wu, K., Yang, T. & Zhou, T. 6Scan: A high-efficiency dynamic Internet-wide IPv6 scanner with regional
encoding. IEEE/ACMTransactions on Netw. 31, 1870–1885, DOI: 10.1109/TNET.2023.3233953 (2023).
6Scan. 6Scan github code. https://github.com/hbn1987/6Scan.git.
Treestrace. Treestrace github code. https://github.com/6Seeks/Treestrace.git.
O. Gasser et al., “Clusters in the Expanse: Understanding and Unbiasing IPv6 Hitlists,” in IMC, 2018.
Hitlist. Hitlist data. https://alcatraz.net.in.tum.de/ipv6-hitlist-service/open/responsive-addresses.txt.xz
Aliases. Aliases data. https://alcatraz.net.in.tum.de/ipv6-hitlist-service/open/aliased-prefixes.txt.xz
Longest prefix matching for aliased prefixes. Longest prefix matching for aliased prefixes github code. https://ipv6hitlist.github.io/lpm/aliases-lpm.py
Bloom Filter. Bloom Filter github code. https://github.com/6Seeks/Treestrace.git.
DFA. DFA github code. https://github.com/NezhaFan/sieve.git.