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Austin Murdock

dblp:208/4251 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2017
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer networks
1 paper
Network measurement and analytics · 100%
Network and information security
1 paper
Network security · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Network measurement and analytics › active measurement
internet-wide scanning
0.312017
Target generation for internet-wide IPv6 scanning · Internet Measurement Conference 2017
Network measurement and analytics › internet measurement
IPv6 target generation
0.312017
Target generation for internet-wide IPv6 scanning · Internet Measurement Conference 2017
Network security › attack strategy
network reconnaissance
0.112017
Target generation for internet-wide IPv6 scanning · Internet Measurement Conference 2017

Methods — techniques the papers use, named apart from their topics

algorithmic address generation · 0.6
YearPublicationVenuePosition
2017 Target generation for internet-wide IPv6 scanning
abstract
Fast IPv4 scanning has enabled researchers to answer a wealth of new security and measurement questions. However, while increased network speeds and computational power have enabled comprehensive scans of the IPv4 address space, a brute-force approach does not scale to IPv6. Systems are limited to scanning a small fraction of the IPv6 address space and require an algorithmic approach to determine a small set of candidate addresses to probe. In this paper, we first explore the considerations that guide designing such algorithms. We introduce a new approach that identifies dense address space regions from a set of known "seed" addresses and generates a set of candidates to scan. We compare our algorithm 6Gen against Entropy/IP---the current state of the art---finding that we can recover between 1--8 times as many addresses for the five candidate datasets considered in the prior work. However, during our analysis, we uncover widespread IP aliasing in IPv6 networks. We discuss its effect on target generation and explore preliminary approaches for detecting aliased regions.
Austin Murdock, Frank Li 0001, Paul Bramsen, Zakir Durumeric, Vern Paxson
Internet Measurement Conference1