manafg – ANT Research News

The paper “A Look at Router Geolocation in Public and Commercial Databases” has appeared in the 2017 Internet Measurement Conference (IMC) on November 1-3, 2017 in London, United Kingdom.

From the abstract:

Regional breakdown of the geolocation error for the geolocation databases vs. ground truth data.

Internet measurement research frequently needs to map infrastructure components, such as routers, to their physical locations. Although public and commercial geolocation services are often used for this purpose, their accuracy when applied to network infrastructure has not been sufficiently assessed. Prior work focused on evaluating the overall accuracy of geolocation databases, which is dominated by their performance on end-user IP addresses. In this work, we evaluate the reliability of router geolocation in databases. We use a dataset of about 1.64M router interface IP addresses extracted from the CAIDA Ark dataset to examine the country- and city-level coverage and consistency of popular public and commercial geolocation databases. We also create and provide a ground-truth dataset of 16,586 router interface IP addresses and their city-level locations, and use it to evaluate the databases’ accuracy with a regional breakdown analysis. Our results show that the databases are not reliable for geolocating routers and that there is room to improve their country- and city-level accuracy. Based on our results, we present a set of recommendations to researchers concerning the use of geolocation databases to geolocate routers.

The work in this paper was joint work by Manaf Gharaibeh, Anant Shah, Han Zhang, Christos Papadopoulos (Colorado State University), Brad Huffaker (CAIDA / UC San Diego), and Roya Ensafi (University of Michigan). The findings of this work are highlighted in an APNIC blog post “Should we trust the geolocation databases to geolocate routers?”. The ground truth datasets used in the paper are available via IMPACT.

The paper “Assessing Co-Locality of IP Blocks” appeared in the 19th IEEE Global Internet Symposium on April 11, 2016 in San Francisco, CA, USA and is available at (http://www.cs.colostate.edu/~manafgh/publications/Assessing-Co-Locality-of-IP-Block-GI2016.pdf). The datasets are available at (https://ant.isi.edu/datasets/geolocation/).

From the abstract:

Many IP Geolocation services and applications assume that all IP addresses within the same /24 IPv4 prefix (a /24 block) reside in close physical proximity. For blocks that contain addresses in very different locations (such as blocks identifying network backbones), this assumption can result in a large geolocation error. In this paper we evaluate the co-location assumption. We first develop and validate a hierarchical clustering method to find clusters of IP addresses with similar observed delay measurements within /24 blocks. We validate our methodology against two ground-truth datasets, confirming that 93% of the identified multi-cluster blocks are true positives with multiple physical locations and an upper bound for false positives of only about 5.4%. We then apply our methodology to a large dataset of 1.41M /24 blocks extracted from a delay-measurement study of the entire responsive IPv4 address space. We find that about 247K (17%) out of 1.41M blocks are not co-located, thus quantifying the error in the /24 block co-location assumption.

The work in this paper is by Manaf Gharaibeh, Han Zhang, Christos Papadopoulos (Colorado State University) and John Heidemann (USC/ISI).