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Creators/Authors contains: "Thiagarajan, Kedar"

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  1. ; ; (, Proceedings of the ACM SIGCOMM 2024 Conference: Posters and Demos)
    Geolocating network devices is essential for various research areas. Yet, despite notable advancements, it continues to be one of the most challenging issues for experimentalists. An approach for geolocating that has proved effective is leveraging geolocating hints in PTR records associated with network devices. We argue that Large Language Models (LLMs), rather than humans, are better equipped to identify patterns in DNS PTR records, and significantly scale the coverage of tools like Hoiho. We introduce an approach that leverages LLMs to classify PTR records, and generate regular expressions for these classes, and hint-to-location mapping. We present preliminary results showing the applicability of using LLMs as a scalable approach to leverage PTR records for infrastructure geolocation. 
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