scholarly journals A Novel Intrusion Detection Model for a Massive Network Using Convolutional Neural Networks

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 50850-50859 ◽  
Author(s):  
Kehe Wu ◽  
Zuge Chen ◽  
Wei Li
Keyword(s):  
Neural Networks ◽  
Intrusion Detection ◽  
Convolutional Neural Networks ◽  
Detection Model

scholarly journals An Intrusion Detection Model Based on Feature Reduction and Convolutional Neural Networks

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 42210-42219 ◽  
Author(s):  
Yihan Xiao ◽  
Cheng Xing ◽  
Taining Zhang ◽  
Zhongkai Zhao
Keyword(s):  
Neural Networks ◽  
Intrusion Detection ◽  
Convolutional Neural Networks ◽  
Feature Reduction ◽  
Detection Model ◽  
Model Based

A Neural Networks Pruning and Data Fusion Based Intrusion Detection Model

2014 ◽  
Vol 651-653 ◽  
pp. 1772-1775
Author(s):  
Wei Gong
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Intrusion Detection ◽  
Data Fusion ◽  
Input Data ◽  
The Other ◽  
Pruning Algorithm ◽  
Detection Model ◽  
Process Ability

The abilities of summarization, learning and self-fitting and inner-parallel computing make artificial neural networks suitable for intrusion detection. On the other hand, data fusion based IDS has been used to solve the problem of distorting rate and failing-to-report rate and improve its performance. However, multi-sensor input-data makes the IDS lose its efficiency. The research of neural network based data fusion IDS tries to combine the strong process ability of neural network with the advantages of data fusion IDS. A neural network is designed to realize the data fusion and intrusion analysis and Pruning algorithm of neural networks is used for filtering information from multi-sensors. In the process of intrusion analysis pruning algorithm of neural networks is used for filtering information from multi-sensors so as to increase its performance and save the bandwidth of networks.

Cascaded hybrid intrusion detection model based on SOM and RBF neural networks

2019 ◽  
Vol 32 (21) ◽  
Author(s):  
Muder Almiani ◽  
Alia AbuGhazleh ◽  
Amer Al‐Rahayfeh ◽  
Abdul Razaque
Keyword(s):  
Neural Networks ◽  
Intrusion Detection ◽  
Rbf Neural Networks ◽  
Detection Model ◽  
Model Based
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