scholarly journals An Effective Cost-Sensitive Convolutional Neural Network for Network Traffic Classification

2021 ◽  
Author(s):  
Mhd Saeed Sharif ◽  
Mina Moein
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Traffic ◽  
Traffic Classification ◽  
Network Traffic Classification ◽  
Effective Cost

scholarly journals Robot Communication: Network Traffic Classification Based on Deep Neural Network

2021 ◽  
Vol 15 ◽  
Author(s):  
Mengmeng Ge ◽  
Xiangzhan Yu ◽  
Likun Liu
Keyword(s):  
Neural Network ◽  
Network Traffic ◽  
Short Term Memory ◽  
Traffic Classification ◽  
Classification Framework ◽  
Network Traffic Classification ◽  
Scalar Input ◽  
Memory Network ◽  
Encrypted Traffic ◽  
Robot Communication

With the rapid popularization of robots, the risks brought by robot communication have also attracted the attention of researchers. Because current traffic classification methods based on plaintext cannot classify encrypted traffic, other methods based on statistical analysis require manual extraction of features. This paper proposes (i) a traffic classification framework based on a capsule neural network. This method has a multilayer neural network that can automatically learn the characteristics of the data stream. It uses capsule vectors instead of a single scalar input to effectively classify encrypted network traffic. (ii) For different network structures, a classification network structure combining convolution neural network and long short-term memory network is proposed. This structure has the characteristics of learning network traffic time and space characteristics. Experimental results show that the network model can classify encrypted traffic and does not require manual feature extraction. And on the basis of the previous tool, the recognition accuracy rate has increased by 8%

scholarly journals Prospects for the Application of Reinforcement Learning to Network Traffic Classification Tasks

2021 ◽  
Vol 2096 (1) ◽  
pp. 012175
Author(s):  
G D Asyaev
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Reinforcement Learning ◽  
Network Traffic ◽  
Classification Task ◽  
Traffic Classification ◽  
Data Set ◽  
Basic Principles ◽  
Network Traffic Classification ◽  
Classification Tasks

Abstract The basic principles and methods of reinforcement learning are reviewed. The problems and approaches for applying a model based on reinforcement learning in the framework of attack prevention are described. The model is built and the hyperparameters of machine learning for the task of classifying network traffic are selected, and its performance on the test data set is evaluated by such quality metrics as accuracy and completeness. The dataset used to implement an agent for selecting the optimal defense strategy for a particular attack has been finalized. Developed an algorithm for using a reinforcement learning neural network for the traffic classification task. A table of rules and rewards for the problem is generated. An agent has been developed and trained to interact with the system. We describe the application of reinforcement learning to the traffic classification task.

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