Learning in Multiple Spaces: Few-Shot Network Attack Detection with Metric-Fused Prototypical Networks

Autor:	Martinez-Lopez, Fernando, Santana, Lesther, Rahouti, Mohamed
Rok vydání:	2024
Předmět:	Computer Science - Cryptography and Security Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: The AAAI-25 Workshop on Artificial Intelligence for Cyber Security (AICS)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2501.00050 Zobrazit plný text záznamu View this record from Arxiv