Learning Regexes to Extract Network Names from Hostnames

Luckie, Matthew; Marder, Alexander; Huffaker, Bradley; claffy, kc

doi:10.1145/3497777.3498545

Cited by 7 publications

(4 citation statements)

References 25 publications

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“…To examine the data at different granularities, we include three topological features to our inferences: (i) IPto-router mappings using MaxMind-based geolocation inferences [4], (ii) router-to-country geolocation inferences using bdrmapIT [65], and (iii) router-to-AS mappings using MIDAR alias resolution [54]. We also include PTR records for the IP addresses (router hostnames) in the long-haul link data collection since this information can provide insights on geolocation [63], customers [64] or other relevant features about the network deployments and structure.…”

Section: Complementary Datasetsmentioning

confidence: 99%

A hop away from everywhere: A view of the intercontinental long-haul infrastructure

Carisimo¹,

Weaver²,

Barford³

et al. 2023

Preprint

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Over the past two decades, a desire to reduce transit cost, improve control over routing and performance, and enhance the quality of experience for users, has yielded a more densely connected, flat network with fewer hops between sources and destinations. The shortening of paths in terms of the number of hops or links has also meant, for what is at the end an infrastructure-bound network, the lengthening of many of these links. In this paper, we focus on an important aspect of the evolving logical connectivity of the Internet that has received little attention to date: intercontinental long-haul links. We develop a methodology and associated processing system for identifying long haul links in traceroute measurements. We apply this system to a large corpus of traceroute data and report on multiple aspects of long haul connectivity including country-level prevalence, routers as international gateways, preferred long-haul destinations, and the evolution of these characteristics over a 7 year period. We identify over 9K layer 3 links that satisfy our definition for intercontinental long haul with many of them terminating in a relatively small number of nodes. An analysis of connected components shows a clearly dominant one with a relative size that remains stable despite a significant growth of the long-haul infrastructure.

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Section: Complementary Datasetsmentioning

confidence: 99%

A hop away from everywhere: A view of the intercontinental long-haul infrastructure

Carisimo¹,

Weaver²,

Barford³

et al. 2023

Preprint

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show abstract

“…Huffaker et al extract geographic information from hostnames [10], using a dictionary of city names and airport codes. Follow-up works by Luckie et al focus on learning how to extract autonomous systems and network names from hostnames [15,16].…”

Section: Related Workmentioning

confidence: 99%

Saving Brian's Privacy: the Perils of Privacy Exposure through Reverse DNS

Toorn¹,

Sommese²,

Sperotto³

et al. 2022

Preprint

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Given the importance of privacy, many Internet protocols are nowadays designed with privacy in mind (e.g., using TLS for confidentiality). Foreseeing all privacy issues at the time of protocol design, however, is challenging and may become near impossible when interaction out of protocol bounds occurs.One demonstrably not well understood interaction occurs when DHCP exchanges are accompanied by automated changes to the global DNS, for example to dynamically add hostnames for allocated IP addresses. As we will substantiate in this paper, this is a privacy risk: the presence of specific clients and network dynamics may be learned from virtually anywhere on the Internet, even if other mechanisms to limit tracking by outsiders (e.g., blocking pings) are in place.We present a first of its kind study into this risk. We identify networks that expose client identifiers in reverse DNS records and study the relation between the presence of clients and said records. Our results show a strong link: in 9 out of 10 cases, records linger for at most an hour, for a selection of academic, enterprise and ISP networks alike. We also demonstrate how client patterns and network dynamics can be learned, by tracking devices owned by persons named Brian over time, revealing shifts in work patterns caused by COVID-19 related work-from-home measures, and by determining a good time to stage a heist.

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“…Huffaker et al extract geographic information from hostnames [197], using a dictionary of city names and airport codes. Follow-up works by Luckie et al focus on learning how to extract autonomous systems and network names from hostnames [198], [199].…”

Section: Information In Hostnamesmentioning

confidence: 99%

“…Examples are north and south. We use these terms to exclude router-level PTR records (see also [198], [199]).…”

Section: Generic Termsmentioning

confidence: 99%

Proactive threat detection : a DNS based approach

Toorn¹

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Peter and Anneke, I'd like to thank you personally for allowing me to figure-out my own path in life. You have always supported me in every choice I made, regardless of the direction it would take me. And you were there when I needed you. Thank you.Finally, and certainly not last. Lien, you are truly special to me. Even when my Ph.D. work, Storro work, the combination thereof, or life in general, got me stressed and moody you stood by my side. You were there when I needed you, or offered me some 'Twentse nuchterheid' to get me back on the rails.

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Learning Regexes to Extract Network Names from Hostnames

Cited by 7 publications

References 25 publications

A hop away from everywhere: A view of the intercontinental long-haul infrastructure

A hop away from everywhere: A view of the intercontinental long-haul infrastructure

Saving Brian's Privacy: the Perils of Privacy Exposure through Reverse DNS

Proactive threat detection : a DNS based approach

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