@Article{cmc.2020.09802,
AUTHOR = {Mo Chen, Xiaojuan Wang, Mingshu He, Lei Jin, Khalid Javeed, Xiaojun Wang},
TITLE = {A Network Traffic Classification Model Based on Metric Learning},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {64},
YEAR = {2020},
NUMBER = {2},
PAGES = {941--959},
URL = {http://www.techscience.com/cmc/v64n2/39338},
ISSN = {1546-2226},
ABSTRACT = {Attacks on websites and network servers are among the most critical threats in 
network security. Network behavior identification is one of the most effective ways to 
identify malicious network intrusions. Analyzing abnormal network traffic patterns and 
traffic classification based on labeled network traffic data are among the most effective 
approaches for network behavior identification. Traditional methods for network traffic 
classification utilize algorithms such as Naive Bayes, Decision Tree and XGBoost. 
However, network traffic classification, which is required for network behavior 
identification, generally suffers from the problem of low accuracy even with the recently 
proposed deep learning models. To improve network traffic classification accuracy thus 
improving network intrusion detection rate, this paper proposes a new network traffic 
classification model, called ArcMargin, which incorporates metric learning into a
convolutional neural network (CNN) to make the CNN model more discriminative.
ArcMargin maps network traffic samples from the same category more closely while 
samples from different categories are mapped as far apart as possible. The metric learning 
regularization feature is called additive angular margin loss, and it is embedded in the 
object function of traditional CNN models. The proposed ArcMargin model is validated 
with three datasets and is compared with several other related algorithms. According to a 
set of classification indicators, the ArcMargin model is proofed to have better 
performances in both network traffic classification tasks and open-set tasks. Moreover, in
open-set tasks, the ArcMargin model can cluster unknown data classes that do not exist in 
the previous training dataset.},
DOI = {10.32604/cmc.2020.09802}
}