parHSOM: A novel parallel Hierarchical Self-Organizing Map implementation

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 than the Sequential HSOM algorithm without any significant loss in performance. Additionally, this work provides a platform for further investigation into parallel HSOM implementations.