parHSOM: A novel parallel Hierarchical Self-Organizing Map implementation
Rebekah Lane, Logan Cummins, Andy Perkins, George Trawick, Ioana Banicescu, Sudip Mittal

TL;DR
This paper introduces parHSOM, a parallel Hierarchical Self-Organizing Map architecture that significantly reduces training time for intrusion detection systems without sacrificing performance.
Contribution
The work presents a novel parallel HSOM architecture that accelerates training on large datasets, enabling more efficient cybersecurity intrusion detection.
Findings
parHSOM trains faster than sequential HSOM across multiple datasets
Performance of parHSOM is comparable to traditional HSOM
Provides a platform for further parallel HSOM research
Abstract
The digital age has completely transformed the way that information is processed and stored, which makes cybersecurity a crucial field of research. Cybersecurity contains many different domains, but this work focuses on Intrusion Detection Systems (IDSs). Within the literature, Hierarchical Self-Organizing Maps (HSOMs) have been used to create trustworthy, explainable, and AI-based IDSs. However, HSOMs are trained sequentially, which means that training HSOMs on large datasets is slow. This work presents a novel parallel HSOM architecture, called parHSOM. The purpose of this research is to investigate the effect that parallel computation has on the HSOM training time. parHSOM is tested on two different testbeds, four different output grid sizes, and five different cybersecurity datasets. Performance metrics collected from these experiments show that parHSOM consistently trains faster…
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