提出并行分层自组织映射，显著加速网络安全入侵检测模型训练。
parHSOM: A novel parallel Hierarchical Self-Organizing Map implementation
- 采用并行计算重构分层自组织映射结构，突破传统串行训练瓶颈。
- 在5个数据集上测试，训练速度提升明显，准确率损失可忽略。
- 适合需要快速部署高可解释性AI安全系统的研究人员与工程师。
数字时代彻底改变了信息处理与存储方式，使网络安全成为关键研究领域。本文聚焦入侵检测系统（IDS），针对已有分层自组织映射（HSOM）模型训练依赖串行过程、难以应对大规模数据的问题，提出一种新型并行分层自组织映射（parHSOM）。该方法在两个测试平台、四种输出网格尺寸及五个网络安全数据集上进行了验证。实验结果表明，parHSOM在保持性能几乎不变的前提下，显著缩短了训练时间。本研究还为后续并行化HSOM实现提供了可扩展的框架支持。
原文摘要 · Abstract (English)
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.
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