A systematic approach of feature selection for encrypted network traffic classification

2018 ◽  
Author(s):  
Donald McGaughey ◽  
Trevor Semeniuk ◽  
Ron Smith ◽  
Scott Knight
Keyword(s):  
Feature Selection ◽  
Network Traffic ◽  
Systematic Approach ◽  
Traffic Classification ◽  
Network Traffic Classification ◽  
Selection For

scholarly journals Edge Computing Intelligence Using Robust Feature Selection for Network Traffic Classification in Internet-of-Things

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 224059-224070
Author(s):  
Bushra Mohammed ◽  
Mosab Hamdan ◽  
Joseph Stephen Bassi ◽  
Haitham A. Jamil ◽  
Suleman Khan ◽  
...  
Keyword(s):  
Feature Selection ◽  
Internet Of Things ◽  
Network Traffic ◽  
Edge Computing ◽  
Traffic Classification ◽  
Network Traffic Classification ◽  
Selection For

A Novel Feature Selection Method Based on Principal Component Analysis for Network Traffic Classification

2014 ◽  
Vol 989-994 ◽  
pp. 4510-4513
Author(s):  
Hong Zhi Wang ◽  
Jian Ping Zhang ◽  
Zun Yi Shang
Keyword(s):  
Feature Selection ◽  
Network Traffic ◽  
Feature Selection Method ◽  
Principal Component ◽  
Selection Method ◽  
Performance Comparison ◽  
Traffic Classification ◽  
Network Traffic Classification ◽  
Validation Experiment ◽  
Pca Method

In network traffic classification, by conventional PCA method, more features still exist due to uniform contribution rates for most of features. To overcome this problem, in this paper, a novel feature selection method is proposed to reduce data dimension of network traffic. A contribution rate of various features in each component is calculated by a new weight criterion. A maxima-order principle is proposed to determine feature selection. Based on three multi-class classification methods, performance comparison is conducted by actual traffic data with 10-fold cross-validation. Experiment shows that the proposed method has higher classification accuracy than conventional PCA method.

scholarly journals An Improved Network Traffic Classification Model Based on a Support Vector Machine

Symmetry ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 301 ◽  
Author(s):  
Jie Cao ◽  
Da Wang ◽  
Zhaoyang Qu ◽  
Hongyu Sun ◽  
Bin Li ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Network Traffic ◽  
Feature Selection Method ◽  
Selection Method ◽  
Classification Model ◽  
Support Vector ◽  
Traffic Classification ◽  
Generalization Ability ◽  
Network Traffic Classification

Network traffic classification based on machine learning is an important branch of pattern recognition in computer science. It is a key technology for dynamic intelligent network management and enhanced network controllability. However, the traffic classification methods still facing severe challenges: The optimal set of features is difficult to determine. The classification method is highly dependent on the effective characteristic combination. Meanwhile, it is also important to balance the experience risk and generalization ability of the classifier. In this paper, an improved network traffic classification model based on a support vector machine is proposed. First, a filter-wrapper hybrid feature selection method is proposed to solve the false deletion of combined features caused by a traditional feature selection method. Second, to balance the empirical risk and generalization ability of support vector machine (SVM) traffic classification model, an improved parameter optimization algorithm is proposed. The algorithm can dynamically adjust the quadratic search area, reduce the density of quadratic mesh generation, improve the search efficiency of the algorithm, and prevent the over-fitting while optimizing the parameters. The experiments show that the improved traffic classification model achieves higher classification accuracy, lower dimension and shorter elapsed time and performs significantly better than traditional SVM and the other three typical supervised ML algorithms.

Optimizing Feature Selection for Efficient Encrypted Traffic Classification: A Systematic Approach

IEEE Network ◽  
2020 ◽  
Vol 34 (4) ◽  
pp. 20-27
Author(s):  
Meng Shen ◽  
Yiting Liu ◽  
Liehuang Zhu ◽  
Ke Xu ◽  
Xiaojiang Du ◽  
...  
Keyword(s):  
Feature Selection ◽  
Systematic Approach ◽  
Traffic Classification ◽  
Selection For ◽  
Encrypted Traffic
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