FED-GEM-CN: A federated dual-CNN architecture with contrastive cross-attention for maritime radar intrusion detection
The escalating complexity of maritime operations and the integration of advanced radar systems have heightened the susceptibility of maritime infrastructures to sophisticated cyber intrusions. Ensuring resilient and privacy-preserving intrusion detection in such environments necessitates innovative...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-09-01
|
| Series: | Array |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590005625000839 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | The escalating complexity of maritime operations and the integration of advanced radar systems have heightened the susceptibility of maritime infrastructures to sophisticated cyber intrusions. Ensuring resilient and privacy-preserving intrusion detection in such environments necessitates innovative solutions capable of learning from distributed, heterogeneous data sources without compromising sensitive information. This study introduces FED-GEM-CN, a novel federated learning framework designed explicitly for maritime radar intrusion detection. The proposed architecture integrates dual parallel convolutional neural network (CNN) pipelines to independently process network and radar modality features, which are subsequently fused via a multi-head cross-attention mechanism to capture intricate inter-modal dependencies. To enhance feature discriminability, a supervised contrastive learning paradigm is incorporated, while a gradient episodic memory (GEM) buffer strategically retains challenging instances to bolster model robustness against hard-to-detect intrusions. Operating under a federated learning scheme, FED-GEM-CN facilitates collaborative model optimization across distributed radar nodes, preserving data locality and mitigating privacy risks inherent in centralized approaches. Experimental evaluations conducted on a comprehensive real-world maritime radar dataset reveal that FED-GEM-CN achieves superior performance, attaining an overall accuracy exceeding 99 % and macro F1-scores above 0.97 across federated rounds, with convergence typically observed within 15 communication iterations. These findings substantiate the efficacy of the proposed system in delivering robust, energy-efficient, and privacy-aware intrusion detection tailored to the constraints of maritime radar networks. The approach underscores a significant advancement toward deploying intelligent, distributed cybersecurity solutions within critical maritime infrastructures. |
|---|---|
| ISSN: | 2590-0056 |