2023/11/09 by Bill Kay, Sinan G. Aksoy, Kay, Bill +19
Biochemistry, Genetics and Molecular Biology · Computer Science · Physics and Astronomy · #55N31 #Bioinformatics and Genomic Networks #Complex Network Analysis Techniques #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Topological and Geometric Data Analysis
paper · pdf · doi:10.48550/arxiv.2312.00023
openalex publication_date 2023/11/09 · openalex created_date 2023/12/05 · openalex updated_date 2026/07/28
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.