Internet Census
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- Publication date
- 2012
- Usage
- CC0 1.0 Universal
- Contributor
- Michael Best
- Language
- English
- Item Size
- 569.4G
While playing around with the Nmap Scripting Engine (NSE) we discovered an amazing number of open embedded devices on the Internet. Many of them are based on Linux and allow login to standard BusyBox with empty or default credentials. We used these devices to build a distributed port scanner to scan all IPv4 addresses. These scans include service probes for the most common ports, ICMP ping, reverse DNS and SYN scans. We analyzed some of the data to get an estimation of the IP address usage.
The full download is 568GB large. The data is segmented and organized, so you may just choose the files you need and don't have to download everything. The data is tab separated, ordered by IP and timestamp. The torrent also contains an offline version of this website and tab separated lists of the data which can be browsed in the service probe overview section, in the Hilbert Browser and in the reverse DNS overview.
The data is compressed using ZPAQ 1.10, which is default in Debian and Ubuntu. It was found to have the smallest filesize, although it comes at the cost of very high cpu usage. Python code to distribute decompression workload across LAN computers is part of the code pack. Decompressing all data results in 9TB of raw logfiles, but this code can also be used to recompress the data into gzip files. The gziped dataset should be ~1.5TB.
The full download is 568GB large. The data is segmented and organized, so you may just choose the files you need and don't have to download everything. The data is tab separated, ordered by IP and timestamp. The torrent also contains an offline version of this website and tab separated lists of the data which can be browsed in the service probe overview section, in the Hilbert Browser and in the reverse DNS overview.
The data is compressed using ZPAQ 1.10, which is default in Debian and Ubuntu. It was found to have the smallest filesize, although it comes at the cost of very high cpu usage. Python code to distribute decompression workload across LAN computers is part of the code pack. Decompressing all data results in 9TB of raw logfiles, but this code can also be used to recompress the data into gzip files. The gziped dataset should be ~1.5TB.
- Addeddate
- 2015-09-22 06:17:34.153222
- Identifier
- Carna_Internet_Census
- Identifier-ark
- ark:/13960/t1bk51531
- Location
- Internet
- Scanner
- FTP
- Year
- 2012
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