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You Can Find Me Here: A Study of the Early Adoption of Geofeeds

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Passive and Active Measurement (PAM 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14538))

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Abstract

IP geolocation is a popular mechanism for determining the physical locations of Internet-connected devices. However, despite its widespread use, IP geolocation is known to be inaccurate, especially for devices in less industrialized nations. In 2020, geofeeds were standardized by the IETF, providing a mechanism for owners of IP addresses (i.e., autonomous systems) to self-report the physical locations of IP blocks under their control. Assuming IP address owners accurately report these locations, geofeeds conceptually have the potential to enable “groundtruth” location data. This short paper takes a first look at the roll-out of geofeeds. We examine the opt-in rates of geofeeds by autonomous systems, and surmise the use of geofeed data by two major IP geolocation providers. Over the course of our 14-month data collection efforts (August 2022–October 2023), the number of IP addresses covered by geofeeds has increased tenfold; however, the adoption rate is still low—less than 1% of the IPv4 address space is covered by geofeeds. We find that the rollout is also uneven, with more industrialized nations opting into geofeeds at rates higher than those of less industrialized ones. Moreover, our comparison of geofeed data to locations reported by commercial IP geolocation services suggests that these commercial services may be beginning to incorporate geofeed data into their resolutions. We discuss the implications of our findings, including the potential that uneven adoption rates may further disenfranchise Internet users in less industrialized nations.

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Notes

  1. 1.

    For ease of exposition, we will often use the shorthand geolocation to refer to IP-based geolocation.

  2. 2.

    A user could always use a means of obscuring their IP address such as Tor [16], a VPN, SmartDNS services [18], or a proxy. However, using these technologies requires technical sophistication and imposes performance and usability bottlenecks [17, 34].

  3. 3.

    For example, while most commercial providers showed over 90% accuracy in identifying routers in the U.S., most providers showed between 20% and 39% accuracy when locating routers in Canada [28].

  4. 4.

    Measurements were initially pulled manually once every two weeks and were later automated to run on the 13th and 28th of each month.

  5. 5.

    To account for locales having numerous names or versions of the same name (e.g., the city name for Đakovo, Croatia could also be spelled Djakovo or Dakovo), we computed the normalized Damerau-Levenshtein distance [15] between the two location names and asserted that to match, the result had to be less than 0.5.

  6. 6.

    To account for locations having multiple names or spellings, we used fuzzy matching with tokenized Levenshtein distance to find many of the named locations.

  7. 7.

    See https://github.com/GUSecLab/geofeed-measurement.

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Acknowledgments

We thank the anonymous reviewers and shepherd for their invaluable feedback and suggestions. This work is partially funded by the National Science Foundation through grants 1925497 and 2138078, and by the Callahan Family Chair fund. The opinions and findings expressed in this paper are those of the authors and do not necessarily those of any employer or funding agency.

Author information

Authors and Affiliations

  1. Georgetown University, Washington, DC, USA

    Rahel A. Fainchtein & Micah Sherr

  2. The Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA

    Rahel A. Fainchtein

Authors
  1. Rahel A. Fainchtein
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  2. Micah Sherr
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Corresponding author

Correspondence to Rahel A. Fainchtein .

Editor information

Editors and Affiliations

  1. Akamai Technologies, Cambridge, MA, USA

    Philipp Richter

  2. Hasso Plattner Institute, Potsdam, Germany

    Vaibhav Bajpai

  3. Northwestern University, Evanston, IL, USA

    Esteban Carisimo

Appendices

A Geofeed and Commercial IP Fetch Dates

Table 3 lists the dates of fetches for the geofeeds and the corresponding dates of the commercial IP datasets that were used for comparison.

Table 3. Mapping of pull dates for geofeed results and matched commercial DB pulls. Pairings were selected to minimize the time between the geofeed and commercial pull dates (or vice versa).
Full size table

B Country’s Representation Within the Geofeed Results

Table 4 presents a breakdown of the top ten most and bottom 20 least represented countries within the geofeed results before normalization.

Figure 9 provides a breakdown of countries’ representation within the geofeeds normalized by their respective number of Internet users [8] and Fig. 10 shows geofeeds normalized by each country’s IPv4 address allocation. Additionally, Fig. 11 provides a country-wise breakdown of the total ASes categorized as ISPs by the ASdb in the November 10, 2023 geofeed results and Fig. 12 denotes the proportion of ISPs amongst each represented country’s ASes in the same geofeed data.

Table 4. Top ten most (top) and bottom 20 least (bottom) represented countries.
Full size table
Fig. 9.
figure 9

Countries’ IPv4 address representation within the geofeeds normalized by number of Internet users [8].

Full size image
Fig. 10.
figure 10

Countries’ IPv4 address representation within the geofeeds normalized by their IPv4 address allocations.

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Fig. 11.
figure 11

Total number of ISPs in each country within the Geofeed Results for November 10, 2023 as categorized by the Stanford ASdb.

Full size image
Fig. 12.
figure 12

Proportion of each country’s ASNs that were categorized as ISPs by the Stanford ASdb in the November 10, 2023 Geofeed Results.

Full size image

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Fainchtein, R.A., Sherr, M. (2024). You Can Find Me Here: A Study of the Early Adoption of Geofeeds. In: Richter, P., Bajpai, V., Carisimo, E. (eds) Passive and Active Measurement. PAM 2024. Lecture Notes in Computer Science, vol 14538. Springer, Cham. https://doi.org/10.1007/978-3-031-56252-5_11

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  • DOI: https://doi.org/10.1007/978-3-031-56252-5_11

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