Network Intrusion Detection Algorithm Combined with Group Convolution Network and Snapshot Ensemble
In order to adapt to the rapid development of network technology and network security detection in different scenarios, the generalization ability of the classifier needs to be further improved and has the ability to detect unknown attacks. However, the generalization ability of a single classifier...
Main Authors: | Aili Wang, Wenya Wang, Huaming Zhou, Jian Zhang |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-09-01
|
Series: | Symmetry |
Subjects: | |
Online Access: | https://www.mdpi.com/2073-8994/13/10/1814 |
Similar Items
-
dropCyclic: Snapshot Ensemble Convolutional Neural Network Based on a New Learning Rate Schedule for Land Use Classification
by: Sangdaow Noppitak, et al.
Published: (2022-01-01) -
CSK-CNN: Network Intrusion Detection Model Based on Two-Layer Convolution Neural Network for Handling Imbalanced Dataset
by: Jiaming Song, et al.
Published: (2023-02-01) -
An Efficient Two-Stage Network Intrusion Detection System in the Internet of Things
by: Hongpo Zhang, et al.
Published: (2023-01-01) -
Improving Computer-Aided Cervical Cells Classification Using Transfer Learning Based Snapshot Ensemble
by: Wen Chen, et al.
Published: (2020-10-01) -
Deep Hyperspectral Shots: Deep Snap Smooth Wavelet Convolutional Neural Network Shots Ensemble for Hyperspectral Image Classification
by: Farhan Ullah, et al.
Published: (2024-01-01)