The Application of Deep Learning for Network Traffic Classification
DOI:
https://doi.org/10.54097/hset.v39i.6689Keywords:
Network Traffic Classification; Deep Learning; SAE; CNN; LSTM.Abstract
The classification, detection, and analysis of routine network traffic has been a hot topic for businesses and research institutions due to the proliferation of Internet of Things devices and the explosive development of networks. Traditional methods for categorizing network traffic primarily employ common machine learning algorithms e.g., decision trees and plain Bayes algorithms, but as deep learning technology advances, more and more traffic classifications are being successfully applied. This study examines existing deep learning-based network traffic classification techniques and focuses on the categorization of computer network traffic. Firstly, the research background of the topic is introduced, and then the traffic classification based on deep learning is mainly described, which includes traffic classification based on Stacked Autoencoder, traffic classification based on Convolutional Neural Network and traffic classification based on Recurrent Neural Networks. Following investigation, this paper comes to the conclusion that Long Short-Term Memory and Convolutional Neural Network models are the best deep learning models for traffic classification, with three-dimensional Convolutional Neural Network outperforming the others.
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