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.
@article{arxiv.2312.00023,
title = {Hypergraph Topological Features for Autoencoder-Based Intrusion Detection for Cybersecurity Data},
author = {Bill Kay and Sinan G. Aksoy and Molly Baird and Daniel M. Best and Helen Jenne and Cliff Joslyn and Christopher Potvin and Gregory Henselman-Petrusek and Garret Seppala and Stephen J. Young and Emilie Purvine},
journal= {arXiv preprint arXiv:2312.00023},
year = {2023}
}