用MTF增强的Transformer提升稀疏数据下的网络入侵检测精度
Time Series Based Network Intrusion Detection using MTF-Aided Transformer
- 将马尔可夫转移场与Transformer结合，捕捉时间序列依赖关系
- 在InSDN数据集上优于基线模型，尤其在数据稀缺时表现更优
- 适合资源受限的SDN场景，兼具高精度与高效训练推理
本文提出一种基于马尔可夫转移场（MTF）增强的Transformer模型，用于软件定义网络（SDN）中的时间序列分类。该模型融合了MTF对时间依赖性的建模能力与Transformer的复杂模式识别优势。在InSDN数据集上的实验表明，该模型在数据受限环境下显著优于基线分类方法，且性能随MTF与Transformer组件协同作用而提升。同时，该方法保持了具有竞争力的训练与推理速度，适用于真实SDN场景。研究结果验证了MTF增强Transformer在稀疏数据条件下解决时间序列分类挑战的潜力，为可靠、可扩展的网络分析提供了可行路径。
原文摘要 · Abstract (English)
This paper introduces a novel approach to time series classification using a Markov Transition Field (MTF)-aided Transformer model, specifically designed for Software-Defined Networks (SDNs). The proposed model integrates the temporal dependency modeling strengths of MTFs with the sophisticated pattern recognition capabilities of Transformer architectures. We evaluate the model's performance using the InSDN dataset, demonstrating that our model outperforms baseline classification models, particularly in data-constrained environments commonly encountered in SDN applications. We also highlight the relationship between the MTF and Transformer components, which leads to better performance, even with limited data. Furthermore, our approach achieves competitive training and inference times, making it an efficient solution for real-world SDN applications. These findings establish the potential of MTF-aided Transformers to address the challenges of time series classification in SDNs, offering a promising path for reliable and scalable analysis in scenarios with sparse data.
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