Open Access
ARTICLE
A Network Traffic Classification Model Based on Metric Learning
Mo Chen1, Xiaojuan Wang1, *, Mingshu He1, Lei Jin1, Khalid Javeed2, Xiaojun Wang3
1 Beijing University of Posts and Telecommunications, Beijing, 100876, China.
2 Department of Computer Engineering, Bahria University, Islamabad, Pakistan.
3 School of Electronic Engineering, Dublin City University, Dublin, Ireland.
* Corresponding Author: Xiaojuan Wang. Email: .
Computers, Materials & Continua 2020, 64(2), 941-959. https://doi.org/10.32604/cmc.2020.09802
Received 19 January 2020; Accepted 11 April 2020; Issue published 10 June 2020
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.
Keywords
Cite This Article
M. Chen, X. Wang, M. He, L. Jin, K. Javeed
et al., "A network traffic classification model based on metric learning,"
Computers, Materials & Continua, vol. 64, no.2, pp. 941–959, 2020.
Citations