Few-shot network intrusion detection method based on multi-domain fusion and cross-attention Article Swipe
Congyuan Xu
,
Donghui Li
,
Zihao Liu
,
Jun Yang
,
Qiang Shen
,
Ning Tong
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0327161
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0327161
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.
Related Topics
Concepts
Computer science
Artificial intelligence
Pattern recognition (psychology)
Inference
Frequency domain
Robustness (evolution)
Discrete cosine transform
Feature (linguistics)
Benchmark (surveying)
Feature extraction
Intrusion detection system
Domain (mathematical analysis)
Data mining
Computer vision
Image (mathematics)
Mathematics
Geodesy
Mathematical analysis
Geography
Biochemistry
Philosophy
Linguistics
Gene
Chemistry
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1371/journal.pone.0327161
- OA Status
- gold
- Cited By
- 1
- References
- 51
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4411948373
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4411948373Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1371/journal.pone.0327161Digital Object Identifier
- Title
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Few-shot network intrusion detection method based on multi-domain fusion and cross-attentionWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
-
2025-07-02Full publication date if available
- Authors
-
Congyuan Xu, Donghui Li, Zihao Liu, Jun Yang, Qiang Shen, Ning TongList of authors in order
- Landing page
-
https://doi.org/10.1371/journal.pone.0327161Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1371/journal.pone.0327161Direct OA link when available
- Concepts
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Computer science, Artificial intelligence, Pattern recognition (psychology), Inference, Frequency domain, Robustness (evolution), Discrete cosine transform, Feature (linguistics), Benchmark (surveying), Feature extraction, Intrusion detection system, Domain (mathematical analysis), Data mining, Computer vision, Image (mathematics), Mathematics, Geodesy, Mathematical analysis, Geography, Biochemistry, Philosophy, Linguistics, Gene, ChemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
- References (count)
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51Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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