Few-shot network intrusion detection method based on multi-domain fusion and cross-attention.
Deep learning methods have achieved remarkable progress in network intrusion detection. However, their performance often deteriorates significantly in real-world scenarios characterized by limited attack samples and substantial domain shifts. To address this challenge, we propose a novel few-shot in...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2025-01-01
|
| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0327161 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Deep learning methods have achieved remarkable progress in network intrusion detection. However, their performance often deteriorates significantly in real-world scenarios characterized by limited attack samples and substantial domain shifts. To address this challenge, we propose a novel few-shot intrusion detection method that integrates multi-domain feature fusion with a bidirectional cross-attention mechanism. Specifically, the method adopts a dual-branch feature extractor to jointly capture spatial and frequency domain characteristics of network traffic. The frequency domain features are obtained via two-dimensional discrete cosine transform (2D-DCT), which helps to highlight the spectral structure and improve feature discriminability. To bridge the semantic gap between support and query samples under few-shot conditions, we design a dual-domain bidirectional cross-attention module that enables deep, task-specific alignment across spatial and frequency domains. Additionally, we introduce a hierarchical feature encoding module based on a modified Mamba architecture, which leverages state space modeling to capture long-range dependencies and temporal patterns in traffic sequences. Extensive experiments on two benchmark datasets, CICIDS2017 and CICIDS2018, demonstrate that the proposed method achieves accuracy of 99.03% and 98.64% under the 10-shot setting, outperforming state-of-the-art methods. Moreover, the method exhibits strong cross-domain generalization, achieving over 95.13% accuracy in cross-domain scenarios, thereby proving its robustness and practical applicability in real-world, dynamic network environments. |
|---|---|
| ISSN: | 1932-6203 |