Cargando…
Few-Shot network intrusion detection based on prototypical capsule network with attention mechanism
Network intrusion detection plays a crucial role in ensuring network security by distinguishing malicious attacks from normal network traffic. However, imbalanced data affects the performance of intrusion detection system. This paper utilizes few-shot learning to solve the data imbalance problem cau...
Autores principales: | Sun, Handi, Wan, Liang, Liu, Mengying, Wang, Bo |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10118191/ https://www.ncbi.nlm.nih.gov/pubmed/37079539 http://dx.doi.org/10.1371/journal.pone.0284632 |
Ejemplares similares
-
A Few-Shot Learning-Based Siamese Capsule Network for Intrusion Detection with Imbalanced Training Data
por: Wang, Zu-Min, et al.
Publicado: (2021) -
CLIP-Driven Prototype Network for Few-Shot Semantic Segmentation
por: Guo, Shi-Cheng, et al.
Publicado: (2023) -
Few-shot segmentation with duplex network and attention augmented module
por: Zeng, Sifu, et al.
Publicado: (2023) -
Knowledge-enhanced prototypical network with class cluster loss for few-shot relation classification
por: Liu, Tao, et al.
Publicado: (2023) -
High-Accuracy Maize Disease Detection Based on Attention Generative Adversarial Network and Few-Shot Learning
por: Song, Yihong, et al.
Publicado: (2023)