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      • Computing and Mathematical Sciences
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      Learning to extract geographic information from internet router hostnames

      Luckie, Matthew John; Huffaker, Bradley; Marder, Alexander; Bischof, Zachary; Fletcher, Marianne; Claffy, kc
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      Files
      learning_extract_geographic_information1.pdf
      Accepted version, 489.8Kb
      DOI
       10.1145/3485983.3494869
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      Permanent link to Research Commons version
      https://hdl.handle.net/10289/15254
      Abstract
      Geolocating Internet routers is a long-standing and notoriously difficult challenge, and current solutions lack the accuracy and adaptability to yield reliable results. We revisit this problem, designing a solution capable of accurately and comprehensively extracting geographic information that network operators embed into router interface hostnames. We train our system using dictionaries that map geographic codes to known locations, and constrain inferences with delay measurements conducted from a distributed set of vantage points. While most operators use known geographic codes, some devise their own mnemonic codes for locations, which our system also extracts and interprets. We evaluate our system on Internet-wide topology datasets, automatically learning regular expressions (regexes) for 1023 different domain suffixes with IPv4 routers, and 241 different domain suffixes with IPv6 routers. We received ground truth from operators of 13 domain suffixes, all of whom confirmed the correctness of our learned regexes, and that our system correctly interpreted 78.6% of the custom geographic codes. For these 13 suffixes, our solution more accurately extracts and interprets geographic information than the previous state-of-the-art, correctly geolocating 94.0% of router hostnames with a geohint compared to DRoP (56.6%) and HLOC (73.1%). This work advances the ability of researchers and network operators to characterize the location of critical Internet infrastructure, a foundational building block of network performance, security, and resilience analysis. We release the source code of our system and our inferred regexes.
      Date
      2021
      Type
      Conference Contribution
      Publisher
      ACM
      Rights
      This is an author’s accepted version of an article published in the Proceedings of 17th International Conference on emerging Networking EXperiments and Technologies (CoNEXT 2021)
      Collections
      • Computing and Mathematical Sciences Papers [1455]
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