Malicious traffic detection combined deep neural network with hierarchical attention mechanism
Abstract 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 p...
Guardado en:
Autores principales: | Xiaoyang Liu, Jiamiao Liu |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/4b64aa99e423495497fddadaf0a1dfd6 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Intrusion Detection System Based on Fast Hierarchical Deep Convolutional Neural Network
por: Robson V. Mendonca, et al.
Publicado: (2021) -
Deep neural network for detecting arbitrary precision peptide features through attention based segmentation
por: Fatema Tuz Zohora, et al.
Publicado: (2021) -
DroidEnsemble: Detecting Android Malicious Applications With Ensemble of String and Structural Static Features
por: Wei Wang, et al.
Publicado: (2018) -
Salient Object Detection Using Recurrent Guidance Network With Hierarchical Attention Features
por: Shanmei Lu, et al.
Publicado: (2020) -
Hierarchical Spatiotemporal Electroencephalogram Feature Learning and Emotion Recognition With Attention-Based Antagonism Neural Network
por: Pengwei Zhang, et al.
Publicado: (2021)