Paper Info
Title
A Traffic Classification Method Based on Packet Transport Layer Payload by Ensemble Learning
Abstract
Network traffic classification is an important research topic for computer network, such as QoS detection and admission monitoring. Traditional classification methods, such as port-based and DPI(deep packet inspect)-based, are out-of-date due to the computational expensiveness and inaccuracy. In this paper, we propose a novel traffic classification approach based on packet transport layer payload by ensemble learning. We use three kinds of base neural networks to form a strong classifier. Each model is trained separately and the final prediction result is decided by weight voting. The raw traffic data are reshaped into the format of sequence and matrix as the input, which avoids the TCP stream feature selection and extraction process. Our approach is applicable to both TCP and UDP, which means that it doesn't require a distinction between transport layer protocols. The experiment results show that our approach can reach the high accuracy of 96.38%, and is better than the state-of-the-art methods based on the same dataset. Besides, our proposed model can select packet samples randomly avoiding tracing the whole stream and the model works well even there's packet loss and disorder.
Year
DOI
Venue
2019
10.1109/ISCC47284.2019.8969702
2019 IEEE Symposium on Computers and Communications (ISCC)
Keywords
Field
DocType
traffic classification,feature auto-extraction,ensemble learning,CNN,LSTM
Traffic classification,Data mining,Computer science,Network packet,Quality of service,Computer network,Packet loss,Transport layer,Artificial neural network,Ensemble learning,Payload
Conference
ISSN
ISBN
Citations 
1530-1346
978-1-7281-3000-2
1
PageRank 
References 
Authors
0.36
4
4
Name
Order
Citations
PageRank
Luyang Xu110.36
Xu Zhou28318.25
Yongmao Ren38510.27
Yifang Qin483.28