Aprendizaje explicable de pocos ejemplos con prototipos dinámicos para la detección distribuida de intrusiones por...

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.