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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
snapshot_month: string
snapshot_id: string
schema_version: string
generated_at_utc: string
artifacts: struct<snapshot_parquet: string, manifest: string, schema_json: string, schema_md: string, data_sour (... 12 chars omitted)
  child 0, snapshot_parquet: string
  child 1, manifest: string
  child 2, schema_json: string
  child 3, schema_md: string
  child 4, data_sources: string
snapshot: struct<format: string, compression: string, num_asns: int64, columns: list<item: string>, column_sto (... 2833 chars omitted)
  child 0, format: string
  child 1, compression: string
  child 2, num_asns: int64
  child 3, columns: list<item: string>
      child 0, item: string
  child 4, column_storage_types: struct<asn: string, LACeS-anycast_v4_cnt: string, LACeS-anycast_v6_cnt: string, apnic-eyeball_cc_cnt (... 2720 chars omitted)
      child 0, asn: string
      child 1, LACeS-anycast_v4_cnt: string
      child 2, LACeS-anycast_v6_cnt: string
      child 3, apnic-eyeball_cc_cnt: string
      child 4, apnic-eyeball_eyeball_cnt: string
      child 5, apnic-eyeball_gini: string
      child 6, apnic-eyeball_top_cc(frac): string
      child 7, apnic-eyeball_top_cc(num): string
      child 8, apnic-eyeball_top_frac: string
      child 9, caida-asrel_cone_/24_cnt: string
      child 10, caida-asrel_cone_/64_cnt: string
      child 11, caida-asrel_cone_as_cnt: string
      child 12, caida-asrel_customer_all_sibling: string
      child 13, caida-asrel_customer_cnt: string
      child 14, caida-asre
...
.. 756 chars omitted)
      child 0, month: string
      child 1, package: string
      child 2, size: int64
      child 3, sha256: string
      child 4, schema_version: string
      child 5, num_asns: int64
      child 6, source_dates: struct<RIR delegation files: string, APNIC Eyeball: string, OpenIntel Active DNS Measurements: Tranc (... 609 chars omitted)
          child 0, RIR delegation files: string
          child 1, APNIC Eyeball: string
          child 2, OpenIntel Active DNS Measurements: Tranco: string
          child 3, RouteViews Prefix2AS from CAIDA: string
          child 4, IIJ AS Hegemony IPv4: string
          child 5, IIJ AS Hegemony IPv6: string
          child 6, IIJ Traceroute Hegemony: string
          child 7, Internet Intelligence Lab AS2Org: string
          child 8, CAIDA AS Relationship: string
          child 9, MaxMind GeoLite2: timestamp[s]
          child 10, Hypergiants' off-nets estimations: timestamp[s]
          child 11, ISI ANT Censuses of the Internet Address Space: timestamp[s]
          child 12, CAIDA Macroscopic Internet Topology Data Kit: string
          child 13, PeeringDB daily snapshot data from CAIDA: string
          child 14, LACeS Anycast Census: string
          child 15, Censys Universal Internet Bigquery data: string
          child 16, M-Lab NDT Bigquery data: string
          child 17, Merit Network Telescope: string
      child 7, inputs_sha256: string
months: list<item: string>
  child 0, item: string
dataset: string
to
{'dataset': Value('string'), 'generated_at_utc': Value('string'), 'schema_version': Value('string'), 'concept_doi': Value('string'), 'record_id': Value('null'), 'package_prefix': Value('string'), 'months': List(Value('string')), 'snapshots': List({'month': Value('string'), 'package': Value('string'), 'size': Value('int64'), 'sha256': Value('string'), 'schema_version': Value('string'), 'num_asns': Value('int64'), 'source_dates': {'RIR delegation files': Value('string'), 'APNIC Eyeball': Value('string'), 'OpenIntel Active DNS Measurements: Tranco': Value('string'), 'RouteViews Prefix2AS from CAIDA': Value('string'), 'IIJ AS Hegemony IPv4': Value('string'), 'IIJ AS Hegemony IPv6': Value('string'), 'IIJ Traceroute Hegemony': Value('string'), 'Internet Intelligence Lab AS2Org': Value('string'), 'CAIDA AS Relationship': Value('string'), 'MaxMind GeoLite2': Value('timestamp[s]'), "Hypergiants' off-nets estimations": Value('timestamp[s]'), 'ISI ANT Censuses of the Internet Address Space': Value('timestamp[s]'), 'CAIDA Macroscopic Internet Topology Data Kit': Value('string'), 'PeeringDB daily snapshot data from CAIDA': Value('string'), 'LACeS Anycast Census': Value('string'), 'Censys Universal Internet Bigquery data': Value('string'), 'M-Lab NDT Bigquery data': Value('string'), 'Merit Network Telescope': Value('string')}, 'inputs_sha256': Value('string')})}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 129, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 544, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 3052, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2559, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2584, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 558, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 440, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 361, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2381, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2309, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              snapshot_month: string
              snapshot_id: string
              schema_version: string
              generated_at_utc: string
              artifacts: struct<snapshot_parquet: string, manifest: string, schema_json: string, schema_md: string, data_sour (... 12 chars omitted)
                child 0, snapshot_parquet: string
                child 1, manifest: string
                child 2, schema_json: string
                child 3, schema_md: string
                child 4, data_sources: string
              snapshot: struct<format: string, compression: string, num_asns: int64, columns: list<item: string>, column_sto (... 2833 chars omitted)
                child 0, format: string
                child 1, compression: string
                child 2, num_asns: int64
                child 3, columns: list<item: string>
                    child 0, item: string
                child 4, column_storage_types: struct<asn: string, LACeS-anycast_v4_cnt: string, LACeS-anycast_v6_cnt: string, apnic-eyeball_cc_cnt (... 2720 chars omitted)
                    child 0, asn: string
                    child 1, LACeS-anycast_v4_cnt: string
                    child 2, LACeS-anycast_v6_cnt: string
                    child 3, apnic-eyeball_cc_cnt: string
                    child 4, apnic-eyeball_eyeball_cnt: string
                    child 5, apnic-eyeball_gini: string
                    child 6, apnic-eyeball_top_cc(frac): string
                    child 7, apnic-eyeball_top_cc(num): string
                    child 8, apnic-eyeball_top_frac: string
                    child 9, caida-asrel_cone_/24_cnt: string
                    child 10, caida-asrel_cone_/64_cnt: string
                    child 11, caida-asrel_cone_as_cnt: string
                    child 12, caida-asrel_customer_all_sibling: string
                    child 13, caida-asrel_customer_cnt: string
                    child 14, caida-asre
              ...
              .. 756 chars omitted)
                    child 0, month: string
                    child 1, package: string
                    child 2, size: int64
                    child 3, sha256: string
                    child 4, schema_version: string
                    child 5, num_asns: int64
                    child 6, source_dates: struct<RIR delegation files: string, APNIC Eyeball: string, OpenIntel Active DNS Measurements: Tranc (... 609 chars omitted)
                        child 0, RIR delegation files: string
                        child 1, APNIC Eyeball: string
                        child 2, OpenIntel Active DNS Measurements: Tranco: string
                        child 3, RouteViews Prefix2AS from CAIDA: string
                        child 4, IIJ AS Hegemony IPv4: string
                        child 5, IIJ AS Hegemony IPv6: string
                        child 6, IIJ Traceroute Hegemony: string
                        child 7, Internet Intelligence Lab AS2Org: string
                        child 8, CAIDA AS Relationship: string
                        child 9, MaxMind GeoLite2: timestamp[s]
                        child 10, Hypergiants' off-nets estimations: timestamp[s]
                        child 11, ISI ANT Censuses of the Internet Address Space: timestamp[s]
                        child 12, CAIDA Macroscopic Internet Topology Data Kit: string
                        child 13, PeeringDB daily snapshot data from CAIDA: string
                        child 14, LACeS Anycast Census: string
                        child 15, Censys Universal Internet Bigquery data: string
                        child 16, M-Lab NDT Bigquery data: string
                        child 17, Merit Network Telescope: string
                    child 7, inputs_sha256: string
              months: list<item: string>
                child 0, item: string
              dataset: string
              to
              {'dataset': Value('string'), 'generated_at_utc': Value('string'), 'schema_version': Value('string'), 'concept_doi': Value('string'), 'record_id': Value('null'), 'package_prefix': Value('string'), 'months': List(Value('string')), 'snapshots': List({'month': Value('string'), 'package': Value('string'), 'size': Value('int64'), 'sha256': Value('string'), 'schema_version': Value('string'), 'num_asns': Value('int64'), 'source_dates': {'RIR delegation files': Value('string'), 'APNIC Eyeball': Value('string'), 'OpenIntel Active DNS Measurements: Tranco': Value('string'), 'RouteViews Prefix2AS from CAIDA': Value('string'), 'IIJ AS Hegemony IPv4': Value('string'), 'IIJ AS Hegemony IPv6': Value('string'), 'IIJ Traceroute Hegemony': Value('string'), 'Internet Intelligence Lab AS2Org': Value('string'), 'CAIDA AS Relationship': Value('string'), 'MaxMind GeoLite2': Value('timestamp[s]'), "Hypergiants' off-nets estimations": Value('timestamp[s]'), 'ISI ANT Censuses of the Internet Address Space': Value('timestamp[s]'), 'CAIDA Macroscopic Internet Topology Data Kit': Value('string'), 'PeeringDB daily snapshot data from CAIDA': Value('string'), 'LACeS Anycast Census': Value('string'), 'Censys Universal Internet Bigquery data': Value('string'), 'M-Lab NDT Bigquery data': Value('string'), 'Merit Network Telescope': Value('string')}, 'inputs_sha256': Value('string')})}
              because column names don't match

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YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

AS feature snapshots (IIL-as-feature-snapshot)

Monthly per-ASN feature tables aggregating signals from routing, DNS, scanning, geolocation, topology, and organization-mapping data sources.

These feature snapshots are published with the AS Tagging Toolkit (pip install as-tagging). The methodology is described in the paper Rethinking and Facilitating How We Classify Autonomous Systems by Network Properties (ACM IMC 2026).

How to use

With the toolkit (recommended):

from as_tagging import ASTagging, OnlineSnapshotProvider

provider = OnlineSnapshotProvider()          # reads this dataset
tagger = ASTagging(snapshot_provider=provider, date="2026-07")

Or access the files directly. On GitHub, each released month is stored as data/IIL-as-feature-snapshot.<YYYY-MM>.tar.gz; on HuggingFace, the corresponding archive is under archives/. Each archive unpacks to a <YYYY-MM>/ folder containing:

  • IIL-as-feature-snapshot.<YYYY-MM>.parquet — the feature table, one row per ASN (Snappy-compressed)
  • manifest.json — per-source input dates, provenance, row count
  • schema.json / schema.md — column definitions
  • data_sources.txt — raw source listing

index.json lists every released month with its size, SHA-256, and per-source input dates. The HuggingFace mirror also exposes each month pre-extracted under snapshots/<YYYY-MM>/, with the parquet file named data.parquet, for direct access.

The dataset is organized per ASN. Each ASN is associated with the features below (feature keys are shown as they appear in the snapshots).

Schema version

The current schema version is v1. The feature set has grown slightly over time as new sources were added, so schema.json and schema.md inside each monthly package are the authoritative column definitions for that snapshot.

Monthly aggregation strategy

We generate monthly feature snapshots by aggregating data from the sources below.

  • For six measurement-based sources that update daily or at least weekly (RouteViews Prefix-to-AS, IIJ AS Hegemony, APNIC Eyeball, LACeS Anycast Census, OpenIntel Tranco, and IIJ Traceroute Hegemony), we collect all available data within each month and compute the monthly average (mean), to mitigate daily fluctuations and leverage all available measurements.
    Note: For these sources, features with names like *_cnt may be floats because they are monthly averages.

  • For Censys Universal Internet, we use snapshots published on Tuesdays (more comprehensive because they include scans for both hosts and virtual hosts). Due to Google BigQuery quota constraints, we average over two Tuesday snapshots per month.

  • For other sources that update more frequently than monthly, we use simpler strategies:

    • M-Lab NDT: aggregate speed tests from the entire month, deduplicated across NDT5 and NDT7.
    • PeeringDB and RIR delegation: use a single snapshot per month (no averaging).
    • MaxMind GeoLite2: use one release per monthly snapshot to geolocate prefixes. The 2024-08 through 2024-11 snapshots use the 2024-11-22 release; each month's manifest.json records its source date.

Data sources and citations

Each snapshot aggregates the upstream datasets below. The exact per-source input date for a given month is recorded in that month's manifest.json (source_dates) and data_sources.txt; the list here is the static provenance and citations.

[1] Fontugne, Romain, Anant Shah, and Emile Aben. "The (thin) bridges of as connectivity: Measuring dependency using as hegemony." Passive and Active Measurement: 19th International Conference, PAM 2018, Berlin, Germany, March 26–27, 2018, Proceedings 19. Springer International Publishing, 2018.

[2] Tashiro, Malte, Romain Fontugne, and Kensuke Fukuda. "Following the Data Trail: An Analysis of IXP Dependencies." International Conference on Passive and Active Network Measurement. Cham: Springer Nature Switzerland, 2024.

[3] Hendriks, Remi, et al. "Laces: an open, fast, responsible and efficient longitudinal anycast census system." Proceedings of the 2025 ACM Internet Measurement Conference. 2025.

[4] van Rijswijk-Deij, Roland, et al. "A high-performance, scalable infrastructure for large-scale active DNS measurements." IEEE journal on selected areas in communications 34.6 (2016): 1877-1888.

[5] Durumeric, Zakir, et al. "A search engine backed by Internet-wide scanning." Proceedings of the 22nd ACM SIGSAC conference on computer and communications security. 2015.

[6] Chen, Zhiyi, et al. "Improving the Inference of Sibling Autonomous Systems." International Conference on Passive and Active Network Measurement. Cham: Springer Nature Switzerland, 2023.

[7] Heidemann, John, et al. "Census and survey of the visible Internet." Proceedings of the 8th ACM SIGCOMM conference on Internet measurement. 2008.

[8] Gigis, Petros, et al. "Seven years in the life of Hypergiants' off-nets." Proceedings of the 2021 ACM SIGCOMM 2021 Conference. 2021.

Features per data source

  • Delegation (delegation): (single monthly snapshot)

    • delegation_rir (qualitative): The Regional Internet Registry that delegated the AS number.
    • delegation_cc (qualitative): The country code where the AS number was registered.

  • APNIC eyeball dataset (apnic-eyeball): (monthly average over all available days in the month)

    • apnic-eyeball_top_frac: Monthly average of the maximal country eyeball fraction across all countries.
    • apnic-eyeball_eyeball_cnt: Monthly average of total inferred eyeballs across all countries.
    • apnic-eyeball_cc_cnt: Monthly average number of countries in which the AS has eyeballs.
    • apnic-eyeball_gini: Monthly average Gini coefficient of the country eyeball distribution (higher means more uneven).
    • apnic-eyeball_top_cc(frac) (qualitative): Most frequent daily country code with the maximal eyeball fraction.
    • apnic-eyeball_top_cc(num) (qualitative): Most frequent daily country code with the maximal inferred-eyeball count.

  • Hypergiants' off-nets estimations (hg-offnet): (single monthly snapshot / simple aggregation)

    • hg-offnet_v4addr_cnt: Number of inferred Hypergiant off-net IPv4 prefixes associated with the ASN.

  • MaxMind GeoLite2 (maxmind-geolite2): (one source release per monthly snapshot; see manifest.json for its date)

    • maxmind-geolite2_cc_v4_cnt: Number of geolocated countries for originated IPv4 addresses.
    • maxmind-geolite2_cc_v6_cnt: Number of geolocated countries for originated IPv6 addresses.
    • maxmind-geolite2_topfrac_v4: Fraction of IPv4 addresses in the top geolocated country.
    • maxmind-geolite2_topfrac_v6: Fraction of IPv6 addresses in the top geolocated country.
    • maxmind-geolite2_gini_v4: Gini coefficient of the IPv4 geolocation distribution (higher means more uneven).
    • maxmind-geolite2_gini_v6: Gini coefficient of the IPv6 geolocation distribution (higher means more uneven).
    • maxmind-geolite2_cc_v4_dict (qualitative): Country distribution dictionary for IPv4 geolocation.
    • maxmind-geolite2_cc_v6_dict (qualitative): Country distribution dictionary for IPv6 geolocation.
    • maxmind-geolite2_topcc_v4 (qualitative): Top geolocated country code for IPv4.
    • maxmind-geolite2_topcc_v6 (qualitative): Top geolocated country code for IPv6.

  • Censys Universal Internet (censys): (average of two Tuesday snapshots per month)

    • censys_v4addr_cnt: Monthly-averaged number of responsive IPv4 addresses.

    • censys_os_cnt: Monthly-averaged number of distinct scanned operating systems.

    • censys_service_cnt: Monthly-averaged number of distinct scanned services.

    • censys_port_cnt: Monthly-averaged number of distinct scanned ports.

    • censys_voip_cnt: Monthly-averaged number of IPs with open port 5060 or 5061.

    • censys_ics_cnt: Monthly-averaged number of IPs with ports commonly used by industrial control systems.

    • censys_http_cnt: Monthly-averaged number of IPs hosting the HTTP service.

    • censys_ssh_cnt: Monthly-averaged number of IPs with open port 22.

    • censys_auth_cnt: Monthly-averaged number of IPs hosting an authoritative DNS server.

    • censys_forw_cnt: Monthly-averaged number of IPs hosting a forwarding DNS server.

    • censys_recu_cnt: Monthly-averaged number of IPs hosting a recursive DNS server.

    • censys_fdnsname_cnt: Monthly-averaged number of host names detected via forward DNS.

    • censys_rdnsname_cnt: Monthly-averaged number of host names detected via reverse DNS.

    • Top OS (monthly-averaged counts + names):

      • censys_os_1_cnt, censys_os_1_name (qualitative)
      • censys_os_2_cnt, censys_os_2_name (qualitative)
      • censys_os_3_cnt, censys_os_3_name (qualitative)

    • Top Port (monthly-averaged counts + names):

      • censys_port_1_cnt, censys_port_1_name (qualitative)
      • censys_port_2_cnt, censys_port_2_name (qualitative)
      • censys_port_3_cnt, censys_port_3_name (qualitative)

    • Top Service (monthly-averaged counts + names):

      • censys_service_1_cnt, censys_service_1_name (qualitative)
      • censys_service_2_cnt, censys_service_2_name (qualitative)
      • censys_service_3_cnt, censys_service_3_name (qualitative)

  • OpenIntel Active DNS Measurements: Tranco (openintel-tranco): (monthly average over all available data in the month)

    • openintel-tranco_v4addr_cnt: Monthly-averaged number of distinct IPv4 addresses hosting web servers for Tranco domains.
    • openintel-tranco_v6addr_cnt: Monthly-averaged number of distinct IPv6 addresses hosting web servers for Tranco domains.
    • openintel-tranco_topdomain_cnt: Monthly-averaged number of distinct hosted Tranco top domains.

  • CAIDA AS Relationship (caida-asrel): (single monthly snapshot)

    • caida-asrel_provider_cnt: Number of inferred providers.
    • caida-asrel_customer_cnt: Number of inferred customers.
    • caida-asrel_peer_cnt: Number of inferred peers.
    • caida-asrel_cone_/24_cnt: IPv4 space in /24s of the customer cone.
    • caida-asrel_cone_/64_cnt: IPv6 space in /64s of the customer cone.
    • caida-asrel_cone_as_cnt: Number of ASes inferred in the customer cone.
    • caida-asrel_customer_all_sibling: Whether all customer ASes from CAIDA AS Relationship are sibling ASes in IIL-AS2Org (false if the AS has no customers).
    • caida-asrel_customer_has_nonsibling: Whether at least one customer AS from CAIDA AS Relationship is not a sibling AS in IIL-AS2Org.

    The raw per-ASN provider/customer/peer/customer-cone AS lists are omitted from public releases and may be reconstructed locally from CAIDA data obtained by the user under the Public-AUA. The two booleans above are derived from those lists at generation time and are what the AS Tagging Toolkit's Sibling Transit / Public Transit composite tags read from this dataset.

  • RouteViews Prefix2AS from CAIDA (pfx2as): (monthly average over all available days in the month)

    • pfx2as_/24_cnt: Monthly-averaged IPv4 space in /24s originated by the AS.
    • pfx2as_/64_cnt: Monthly-averaged IPv6 space in /64s originated by the AS.

  • ISI ANT Censuses of the Internet Address Space (isi): (single monthly snapshot / simple aggregation)

    • isi_/24_cnt: Number of /24s with at least one active IP address based on ISI Internet Census.

  • Merit Network Telescope (merit): (single monthly snapshot / simple aggregation)

    • merit_/24_cnt: Maximal number of /24s observed hourly by Merit network telescope within one month.

  • M-Lab NDT (mlab-ndt): (aggregate over the entire month)

    • mlab-ndt_v4addr_cnt: Number of distinct IPv4 client IP addresses that initiated at least one NDT speed test during the month (deduplicated across NDT5 and NDT7).
    • mlab-ndt_v6addr_cnt: Number of distinct IPv6 client IP addresses that initiated at least one NDT speed test during the month (deduplicated across NDT5 and NDT7).
    • mlab-ndt_/24_cnt: Number of distinct IPv4 /24 client subnets (derived from client IPs) that initiated at least one NDT speed test during the month.
    • mlab-ndt_/64_cnt: Number of distinct IPv6 /64 client subnets (derived from client IPs) that initiated at least one NDT speed test during the month.

  • CAIDA Macroscopic Internet Topology Data Kit (ITDK) (caida-itdk): (single monthly snapshot)

    • Aggregate:
      • caida-itdk_router_cnt: Number of routers.
      • caida-itdk_topfrac: Fraction of routers in the top geolocated country.
      • caida-itdk_cc_cnt: Number of geolocated countries for routers.
      • caida-itdk_gini: Gini coefficient of router geolocation distribution (higher means more uneven).
      • caida-itdk_topcc (qualitative): Top geolocated country code for routers.
    • IPv4-only:
      • caida-itdk_router_cnt_v4
      • caida-itdk_topfrac_v4
      • caida-itdk_cc_cnt_v4
      • caida-itdk_gini_v4
      • caida-itdk_topcc_v4 (qualitative)
    • IPv6-only:
      • caida-itdk_router_cnt_v6
      • caida-itdk_topfrac_v6
      • caida-itdk_cc_cnt_v6
      • caida-itdk_gini_v6
      • caida-itdk_topcc_v6 (qualitative)

  • IIJ AS Hegemony (iij-hege): (monthly average over all available data in the month)

    • iij-hege_global_hege_v4: Monthly average AS hegemony based on global IPv4 BGP data.
    • iij-hege_global_hege_v6: Monthly average AS hegemony based on global IPv6 BGP data.

  • IIJ Traceroute Hegemony (iij-tr-hege): (monthly average over all available data in the month)

    • iij-tr-hege_ix_cnt: Monthly-averaged number of peered IXPs (traceroute-based).
    • iij-tr-hege_peers_cnt: Monthly-averaged number of peered ASNs through IXPs (traceroute-based).
    • iij-tr-hege_ix_hegemony: Monthly-averaged summation of IXP-peered ASNs hegemony.

  • Internet Intelligence Lab AS2Org (inetintel-as2org): (single monthly snapshot)

    • inetintel-as2org_sibling_cnt: Number of inferred sibling ASes.
    • inetintel-as2org_sibling_list (qualitative): List of inferred sibling ASes.

  • PeeringDB (pdb): (single monthly snapshot)

    • pdb_ix_cnt: Number of peered IXPs based on PeeringDB.

  • LACeS Anycast Census (LACeS-anycast): (monthly average over all available data in the month)

    • LACeS-anycast_v4_cnt: Monthly average sum of per-prefix IPv4 anycast-site estimates for the AS.
    • LACeS-anycast_v6_cnt: Monthly average sum of per-prefix IPv6 anycast-site estimates for the AS.

License

The feature-snapshot data, like the toolkit code and the rest of the IIL-AS-Tagging repository, is distributed under Georgia Tech's Acceptable Use Agreement. See the repository-wide LICENSE.
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