Abstract
We report on the use of novel mathematical methods in hypergraph analytics over a large quantity of DNS data. Hypergraphs generalize graphs, as used in network science, to better model complex multiway relations in cyber data. Specifically, casting DNS data from Georgia Tech’s ActiveDNS repository as hypergraphs allows us to fully represent the interactions between collections of domains and IP addresses. To facilitate large-scale analytics, we fielded an analytical pipeline of two capabilities: HyperNetX (HNX) is a Python package for the exploration and visualization of hypergraphs; while on the backend, the Chapel HyperGraph Library (CHGL) is a library for high performance hypergraph analytics written in the exascale programming language Chapel. CHGL was used to process gigascale DNS data, performing compute-intensive calculations for data reduction and segmentation. Identified portions are then sent to HNX for both exploratory analysis and knowledge discovery targeting known tactics, techniques, and procedures.
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Notes
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\(\mathcal {H}\) can also be represented as a bipartite graph on the disjoint union \(V \sqcup \mathcal {E}\), with each component a distinct part.
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Acknowledgements
This work was partially funded by a US Department of Energy Computational Science Graduate Fellowship (grant DE-SC0020347).
This work was also partially funded under the High Performance Data Analytics (HPDA) program at the Department of Energy’s Pacific Northwest National Laboratory. Pacific Northwest National Laboratory is operated by Battelle Memorial Institute under Contract DE-ACO6-76RL01830.
Special thanks to William Nickless for helpful conversations surrounding the DNS analysis and interpretation.
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Joslyn, C.A. et al. (2020). Hypergraph Analytics of Domain Name System Relationships. In: Kamiński, B., Prałat, P., Szufel, P. (eds) Algorithms and Models for the Web Graph. WAW 2020. Lecture Notes in Computer Science(), vol 12091. Springer, Cham. https://doi.org/10.1007/978-3-030-48478-1_1
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