scholarly journals Analysis of Various Network Traffic Classification Techniques

2021 ◽  
Vol 4 (1) ◽  
pp. 261-270
Author(s):  
Shivam Puri ◽  
Sukhpreet Kaur
Keyword(s):  
Social Networking ◽  
Network Traffic ◽  
Web Pages ◽  
Traffic Classification ◽  
Research Papers ◽  
Daily Lives ◽  
Classification Techniques ◽  
Physical Systems ◽  
Network Traffic Classification ◽  
Financial Transactions

There are several interconnected entities present within the networked data for which the generation of inferences is important. For instance, hyperlinks are used to interconnect the web pages, calls are used to link the phone accounts, and references are used to connect the research papers and so on. Almost every existing application includes networks within it. The daily lives of individuals include social networking, making financial transactions, generating networks that show physical systems and so on. The manner in which the nodes present within the system influence each other can be known through this research. On the basis of observed attributed of an object within the system, another attributed is predicted using new model. The various network traffic classification techniques are reviewed in terms of certain parameters.

scholarly journals A Survey of Network Traffic Classification

2020 ◽  
Vol 32 (6) ◽  
pp. 137-154
Author(s):  
Aleksandr Igorevich Getman ◽  
Maria Kirillovna Ikonnikova
Keyword(s):  
Machine Learning ◽  
Network Traffic ◽  
Learning Algorithms ◽  
Research Area ◽  
Machine Learning Algorithms ◽  
Traffic Classification ◽  
Research Papers ◽  
Advantages And Disadvantages ◽  
Network Traffic Classification ◽  
Insight Into

This survey is dedicated to the task of network traffic classification, particularly to the use of machine learning algorithms in this task. The survey begins with the description of the task, its variations and possible uses in real-world problems. It then proceeds to the description of the methods used historically to solve this task, their limitations and evolution of traffic making machine learning the main way to solve the problem. Then the most popular machine learning algorithms used in this task are described, with the examples of research papers, providing the insight into their advantages and disadvantages in relation to this field. The task of feature selection is discussed, followed by the more global problem of acquiring the suitable dataset to use in the research; some examples of such popular datasets and their descriptions are provided. The paper concludes with the outline of the current problems in this research area to be solved.

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