The latest ITDK release, 2019-04, currently consists of
- two related IPv4 router-level topologies,
- router-to-AS assignments,
- geographic location of each router, and
- DNS lookups of all observed IP addresses.
This ITDK was produced from traceroutes conducted on the Archipelago (Ark) measurement infrastructure. We used a subset of the IPv4 Routed /24 Topology Dataset, which contains traceroutes to randomly-chosen destinations in each routed /24 BGP prefix. We used traces collected Mar 25 to Apr 8, 2019 on 107 Ark monitors located in 43 countries. We also used a subset of the IPv4 Prefix-Probing Traceroute Dataset, which contains traceroutes to a target in every routed BGP prefix. We used traces collected from 30 monitors over the same time period as the Routed /24 dataset above.
- ITDK-2017-02 ITDK-2017-08
- ITDK-2016-03 ITDK-2016-09
- ITDK-2014-04 ITDK-2014-12
- ITDK-2013-04 ITDK-2013-07
- ITDK-2011-04 ITDK-2011-10
- ITDK-2010-01 ITDK-2010-04 ITDK-2010-07
- historical ITDK releases 0204 and 0304 from April 2002 and 2003 collected with skitter (use with caution)
The two included IPv4 router-level topologies are generated from the same IP-level topology but differ in the accuracy and completeness of the alias resolution performed to create them. The first topology is derived from aliases resolved with MIDAR and iffinder, which yield the highest confidence aliases with very low false positives. The second topology also uses MIDAR and iffinder but further includes aliases resolved with kapar, which significantly increases the coverage of aliases but at the cost of false positives (which inflate the size of routers and decrease the router count). Researchers should choose the topology to use depending on the relative importance they place on accuracy vs. comprehensiveness of alias resolution. Choose the most accurate alias resolution if uncertain about which to use.
Each router-level topology is provided in two files, one giving the nodes and another giving the links. There are additional files that assign ASes to each node, provide the geographic location of each node, and provide the DNS name of each observed interface.
The nodes file lists the set of interfaces that were inferred to be on each router.
node <node_id>: <i1> <i2> ... <in>
Each line indicates that a node node_id has interfaces i1 to in. Interface addresses in 220.127.116.11/3 (IANA reserved space for multicast) are not real addresses. They were artificially generated to identify potentially unique non-responding interfaces in traceroute paths.
NOTE: In ITDK release 2013-04 and earlier, we used addresses in 0.0.0.0/8 instead of 18.104.22.168/3 for these non-real addresses.
The links file lists the set of routers and router interfaces that were inferred to be sharing each link. Note that these are IP layer links, not physical cables or graph edges. More than two nodes can share the same IP link if the nodes are all connected to the same layer 2 switch (POS, ATM, Ethernet, etc).
link <link_id>: <N1>:i1 <N2>:i2
[<N3>:[i3]] .. [<Nm>:[im]]
Each line indicates that a link link_id connects nodes N1 to Nm. If it is known which router interface is connected to the link, then the interface address is given after the node ID separated by a colon (e.g., "N1:22.214.171.124"); otherwise, only the node ID is given (e.g., "N1").
By joining the node and link data, one can obtain the known and inferred interfaces of each router. Known interfaces actually appeared in some traceroute path. Inferred interfaces arise when we know that some router N1 connects to a known interface i2 of another router N2, but we never saw an actual interface on the former router. The interfaces on an IP link are typically assigned IP addresses from the same prefix, so we assume that router N1 must have an inferred interface from the same prefix as i2.
The node-AS file assigns an AS to each node found in the nodes file. We use our final bordermapIT assignment heuristic to infer the owner AS of each node.
Addresses that belong to the address space of an Internet exchange point (as self-identified in PeeringDB: https://www.peeringdb.com/) are excluded from the AS analysis, as we don't consider them to be part of the AS-level topology.
node.AS <node_id> <AS> <method>
Each line indicates that the node node_id is owned/operated by the given AS, as inferred with the given method. There are three inference methods:
- a router has only a single choice of AS
- multiple ASes are present on a router, and one AS occurs more frequently than the rest
- multiple ASes are present on a router, but no AS occurs the most frequently, so the choice is based on AS degree
The hostnames file contains the hostname for every IP
address in the router-level topology for which a successful reverse
DNS lookup could be found.
<timestamp> <IP_address> <hostname>
The node-geolocation file contains the geographic location for each node in the nodes file. We use MaxMind's GeoLite City database for the geographic mapping.
We are in the process of expanding the ITDK with additional data that will combine router and AS-level views of the Internet topology. The AS link dataset will contain the set of AS links as inferred from combining the AS assignment and node datasets. The AS relationship dataset will contain the business relationship associated with each AS link in the AS link dataset. These datasets will be available in the coming months.
Data older than one year is available as a public dataset.
Access to this data is subject to the terms of the following CAIDA Acceptable Use Agreement
(printable version in PDF format).
The CAIDA Internet Topology Data Kit - <release dates >,
Access to the most recent one year of data is provided through the website of the
Information Marketplace for Policy and Analysis of Cyber-risk and Trust
(IMPACT) and subject to the following:
The CAIDA Ark IPv4 Internet Topology Data Kits Dataset - <release dates>, www.impactcybertrust.org, DOI 10.23721/107/1423888
Please, report your publications using this dataset to CAIDA.
Request Data Access
- Access the publicly available CAIDA Ark IPv4 Internet Topology Data Kits Dataset (and other topology data)
- Request Access to the restricted CAIDA Ark IPv4 Internet Topology Data Kits Dataset via IMPACT
Note that two historical ITDK releases made in 2002 and 2003 are also available as public datasets. These datasets should be used with caution, as they were constructed using completely different procedures and using topology data collected on the now decommissioned skitter measurement infrastructure.
For alias resolution, we rely on several CAIDA tools: iffinder, kapar, MIDAR, (recent tech report), and speedtrap. MIDAR (Monotonic ID-based Alias Resolution, a tool we hope to release soon) expands on the IP velocity techniques of RadarGun, while kapar expands the analytical techniques of APAR. We use the traceroute dataset as input to MIDAR and iffinder, which generate output files used as input to kapar. kapar heuristically infers the set of interfaces that belong to the same router, and the set of two or more routers on the same IP link (a construct that represents either a point-to-point link, or LAN or cloud with multiple attached IP addresses). We use iffinder, kapar, and MIDAR to construct IPv4 topologies, and speedtrap to construct IPv6 topologies.
We have an in-house bulk DNS lookup service called HostDB that can look up millions of addresses per day. We look up all intermediate addresses and responding destinations seen in the Topology Dataset. Each ITDK contains a list of the successful lookups for each IP address found in the nodes dataset.
To assign IP addresses to ASes, we used a publicly available BGP dump provided by Routeviews. BGP (Border Gateway Protocol) is the protocol for exchanging interdomain routing information among ASes in the Internet. A single origin AS typically announces ("originates") each routable prefix via BGP. We perform IP-to-AS mapping by assigning an IP address to the origin AS of the longest matching prefix for that IP address in the BGP tables.
We used the BGP data to annotate each interdomain link with one of three simplified business relationships -- customer-provider (the customer pays the provider), settlement-free peer (typically no money is exchanged), and sibling (both ASes belong to the same organization) -- using the classification algorithm by Luckie et al., resulting in what we call the AS relationship dataset.
BordermapIT, bdrmapIT, combines the AS inference heuristics found in both bdrmap and MAP-IT. By synthesis of these two techniques, bdrmapIT is designed to accurately identify interdomain links in a traceroute dataset using a graph refinement strategy. Our algorithm proceeds as follows:
- The first step (Graph Construction) processes the traceroutes, extracting paths and generating a prioritized graph of the interfaces. This interface graph is used by our heuristics in Step 3 to infer router ownership and interdomain links.
- In the second step (Graph Initialization), we use the paths and alias resolution to annotate routers that always appear at the end of a traceroute. We process these first since we are unable to refine these inferences later.
- Finally, in the third step (Graph Refinement), we use the graph refinement loop to annotate the remaining routers and interfaces using the prioritized interface graph and the path data. After each iteration of the loop we refine the inferences, enabling additional accuracy.
For further details, please see the papers bdrmap: Inference of Borders Between IP Networks and MAP-IT: Multipass Accurate Passive Inferences from Traceroute.
We use a combination of publicly known Internet eXchange (IX) point information, DDec hostname mapping, and MaxMind's free GeoLite City database to provide the geographic location (at city granularity) of routers in the router-level graph.
We generated an internal IX database containing information combined from: BGP Looking glass database, Wikipedia's list of Internet Exchange Points, PeeringDB, and PCH's IX database. The geographic city names are then mapped to Geoname's locations. This data provides a set of geographic locations and prefixes for each IX. A router is mapped to an IX's location if the router contains at least one interface from the IX's address space and the IX is located in a single city.
We then collect the hostnames for interfaces on routers that are not geolocated to an IX. These hostnames are then mapped to geographic locations using DDec's heuristics.
All remaining routers are geolocated using MaxMind Lite. Because this database maps individual IP addresses to locations, we take the following steps to find the location of each router (which by definition has multiple interfaces). We first map each interface on a router to a location. If all interfaces map to the same location, then we assign that location to the router; otherwise, we do not assign any location to the router (that is, the router does not appear in the geolocation file).
In order to evaluate the accuracy of these methods, we compared the distance between the inferred geographic location and the geographic location of collection of routers which were with a 3 millisecond of the known location of an Atlas monitor. MaxMind provided geographic locations for the largest number of routers with 1980. It was followed by DDec with 848 and IX with 349 routers. Accuracy was the inverse with IX mapping 92% of it's routers to with in 10 km. DDec had 67% routers with in 10 km. MaxMind only had 33% with in 10 km.