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dc.contributor.authorLuckie, Matthew Johnen_NZ
dc.contributor.authorHuffaker, Bradleyen_NZ
dc.contributor.authorMarder, Alexanderen_NZ
dc.contributor.authorBischof, Zacharyen_NZ
dc.contributor.authorFletcher, Marianneen_NZ
dc.contributor.authorClaffy, kcen_NZ
dc.coverage.spatialVirtual Event, Germanyen_NZ
dc.date.accessioned2022-10-18T01:45:23Z
dc.date.available2022-10-18T01:45:23Z
dc.date.issued2021en_NZ
dc.identifier.isbn9781450390989en_NZ
dc.identifier.urihttps://hdl.handle.net/10289/15254
dc.description.abstractGeolocating 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.en_NZ
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherACMen_NZ
dc.rightsThis 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)
dc.sourceCoNEXT 2021en_NZ
dc.subjectcomputer scienceen_NZ
dc.titleLearning to extract geographic information from internet router hostnamesen_NZ
dc.typeConference Contribution
dc.identifier.doi10.1145/3485983.3494869en_NZ
dc.relation.isPartOfProceedings of 17th International Conference on emerging Networking EXperiments and Technologies (CoNEXT 2021)en_NZ
pubs.begin-page440
pubs.elements-id266996
pubs.end-page453
pubs.finish-date2021-12-10en_NZ
pubs.place-of-publicationNew York, NY, USAen_NZ
pubs.publication-statusPublisheden_NZ
pubs.start-date2021-12-07en_NZ


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