Cargando…
Malicious traffic detection combined deep neural network with hierarchical attention mechanism
Given the gradual intensification of the current network security situation, malicious attack traffic is flooding the entire network environment, and the current malicious traffic detection model is insufficient in detection efficiency and detection performance. This paper proposes a data processing...
Autores principales: | Liu, Xiaoyang, Liu, Jiamiao |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8196150/ https://www.ncbi.nlm.nih.gov/pubmed/34117338 http://dx.doi.org/10.1038/s41598-021-91805-z |
Ejemplares similares
-
Malicious Network Traffic Detection Based on Deep Neural Networks and Association Analysis
por: Gao, Minghui, et al.
Publicado: (2020) -
Malicious traffic detection on sampled network flow data with novelty-detection-based models
por: Campazas-Vega, Adrián, et al.
Publicado: (2023) -
A Framework for Malicious Traffic Detection in IoT Healthcare Environment
por: Hussain, Faisal, et al.
Publicado: (2021) -
Malicious Traffic Identification with Self-Supervised Contrastive Learning
por: Yang, Jin, et al.
Publicado: (2023) -
A deep neural network for the classification of epileptic seizures using hierarchical attention mechanism
por: Chirasani, Sateesh Kumar Reddy, et al.
Publicado: (2022)