Machine Learning Algorithm in Network Traffic Classification
Syifa Maliah Rachmawati, Dong‐Seong Kim, Jae‐Min Lee
Abstract
Network traffic classification plays an important role in various network functions such as network security issues and network management. In addition to port-based and payload-based approaches, the classical machine learning approaches have been studied for past decades, but there are some limitations, namely time-consuming, frequent features updates, and the accuracy has decreased due to the rise of internet traffic, especially encrypted traffic. Deep learning comes with the ability of automatic feature learning, some studies try to apply it and reported better accuracy. This survey paper introduces the emerging research and general framework for deep learning-based methods for traffic classification. We present commonly used deep learning methods and their application in traffic classification tasks.