Explainable few-shot learning with dynamic prototypes for distributed fiber-optic intrusion detection Article Swipe

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Xing Hu , Huiliang Shang , Haima Yang , Dawei Zhang · YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.1364/oe.580802

Reliable intrusion detection is critical for modern infrastructure security, yet it faces two fundamental challenges: scarcity of labeled samples and lack of model interpretability. Distributed optical fiber vibration sensing (DVS) systems are promising for perimeter security but perform poorly when only a few intrusion samples are available. Most deep models also lack transparency and trustworthiness. To address these issues, we propose an explainable dual-branch feature fusion dynamic class center prototypical network (DBFF-DC-ProtoNet). The framework employs a lightweight dual-branch 1-D ResNet to extract complementary temporal and time–frequency representations from raw signals and discrete wavelet transform (DWT) features, which are fused to form more discriminative class prototypes. A dynamic class center update strategy with a novel loss function is further introduced to enhance intra-class compactness and inter-class separability in few-shot conditions. In addition, an explainability module integrates prototype-based class activation mapping (Proto-CAM) and case-based reasoning, offering both fine-grained attribution of key signal segments and intuitive retrieval of similar historical cases. Extensive experiments on a self-collected dataset and a public benchmark confirm the effectiveness of our approach, achieving 97.22% and 98.33% accuracy under the 5-shot setting. These results demonstrate that DBFF-DC-ProtoNet effectively bridges few-shot learning with interpretability, providing a practical and trustworthy solution for DVS-based intrusion detection.

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article

Language

en

Landing Page

https://doi.org/10.1364/oe.580802

OA Status

gold

References

29

OpenAlex ID

https://openalex.org/W4416304396

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OpenAlex ID

https://openalex.org/W4416304396

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DOI

https://doi.org/10.1364/oe.580802

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Title

Explainable few-shot learning with dynamic prototypes for distributed fiber-optic intrusion detection

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article

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Language

en

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Publication year

2025

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2025-11-18

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Authors

Xing Hu, Huiliang Shang, Haima Yang, Dawei Zhang

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https://doi.org/10.1364/oe.580802

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Yes

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gold

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https://doi.org/10.1364/oe.580802

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0

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29

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