A Survey on Application of LSTM as a Deep Learning Approach in Traffic Classification for SDN

Rai, Prerna; Sarma, Hiren Kumar Deva

doi:10.1007/978-981-19-5090-2_16

Cited by 1 publication

(2 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the rapid advancement of network technology, the research on encrypted data has increased notably. In [2], a typical LSTM algorithm for SDN networks is summarized. The utilization of graph convolutional networks (GCN) for encrypted traffic classification is discussed in [7].…”

Section: Related Workmentioning

confidence: 99%

“…The proliferation of web applications and services has resulted in a continuous expansion of flow kinds, creating a need for diverse traffic classification approaches in the field of network research [1] . The development of a high-precision traffic classification system is crucial for analyzing the network flows from various perspectives, gaining accurate insights into current network information, effectively managing network resources, providing improved network services and so on [2][3] . However, current methods of network traffic classification encounter several challenges, detailed as follows:…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Effective Real-time Traffic Classification Method Using Convolutional Neural Network

Yang,

Wang,

Feng

et al. 2023

Preprint

View full text Add to dashboard Cite

Network traffic classification has been as a research hots pot in network studies. However, previous research has predominantly focused on coarse-grained classification, neglecting fine-grained classification among network flows. As the increasing demand for personalized network services, fine-grained classification of network flows research has become imminently. This study discusses the task of fine-grained classification mainly, specifically for chat flows. We proposed a Convolutional Neural Network (CNN)-based method for fine-grained real-time classification of chat flows. Firstly, we pre-process the five-tuple data, analysis the probabilistic feature values about the protocols by first-order Markov chains, then using the features as input data of CNN model. Secondly, we propose an improved adaptive step method to optimize the training CNN model; Additionally, we combining the bagging algorithm with the CNN model to improve its classification performance. To validate the effectiveness of our proposed method, we conducted experiments using two different chat flows from the ISCX database. The experiment results show that the proposed classification method effectively improved the fine-grained traffic classification results. It increases the classification accuracy of non-VPN chat flows from 76.7% and 80.8% to 88.8% and that of VPN chat flows from 91.0% and 93.6% to 97.9%.

show abstract

Section: Related Workmentioning

confidence: 99%