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arXiv · 2312.00023

Hypergraph Topological Features for Autoencoder-Based Intrusion Detection for Cybersecurity Data

Bill Kay·Sinan G. Aksoy·Molly Baird·Daniel M. Best·Helen Jenne·Cliff Joslyn·Christopher Potvin·Gregory Henselman-Petrusek·Garret Seppala·Stephen J. Young·Emilie Purvine
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Abstract

In this position paper, we argue that when hypergraphs are used to capture multi-way local relations of data, their resulting topological features describe global behaviour. Consequently, these features capture complex correlations that can then serve as high fidelity inputs to autoencoder-driven anomaly detection pipelines. We propose two such potential pipelines for cybersecurity data, one that uses an autoencoder directly to determine network intrusions, and one that de-noises input data for a persistent homology system, PHANTOM. We provide heuristic justification for the use of the methods described therein for an intrusion detection pipeline for cyber data. We conclude by showing a small example over synthetic cyber attack data.

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Bill Kay, Sinan G. Aksoy, Molly Baird, Daniel M. Best, Helen Jenne, Cliff Joslyn, Christopher Potvin, Gregory Henselman-Petrusek, Garret Seppala, Stephen J. Young, Emilie Purvine. 2023-11-09. Hypergraph Topological Features for Autoencoder-Based Intrusion Detection for Cybersecurity Data. https://arxiv.org/abs/2312.00023

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