Learning in Multiple Spaces: Few-Shot Network Attack Detection with Metric-Fused Prototypical Networks
This addresses network intrusion detection for cybersecurity, but it appears incremental as it builds on existing few-shot learning techniques.
The paper tackles the problem of detecting emerging network attacks with limited data by proposing a Multi-Space Prototypical Learning framework, which outperforms traditional methods in detecting low-profile and novel attacks.
Network intrusion detection systems face significant challenges in identifying emerging attack patterns, especially when limited data samples are available. To address this, we propose a novel Multi-Space Prototypical Learning (MSPL) framework tailored for few-shot attack detection. The framework operates across multiple metric spaces-Euclidean, Cosine, Chebyshev, and Wasserstein distances-integrated through a constrained weighting scheme to enhance embedding robustness and improve pattern recognition. By leveraging Polyak-averaged prototype generation, the framework stabilizes the learning process and effectively adapts to rare and zero-day attacks. Additionally, an episodic training paradigm ensures balanced representation across diverse attack classes, enabling robust generalization. Experimental results on benchmark datasets demonstrate that MSPL outperforms traditional approaches in detecting low-profile and novel attack types, establishing it as a robust solution for zero-day attack detection.