  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.
