Online Traffic Classification Based on Co-training Method

2012 ◽  
Author(s):  
Jinghua Yan ◽  
Xiaochun Yun ◽  
Zhigang Wu ◽  
Hao Luo ◽  
Shuzhuang Zhang ◽  
...  
Keyword(s):  
Traffic Classification ◽  
Training Method ◽  
Online Traffic

scholarly journals Selection of Online Network Traffic Discriminators for on-the-Fly Traffic Classification

2021 ◽  
Vol 20 (38) ◽  
pp. 65-85
Author(s):  
Angela María Vargas Arcila ◽  
Juan Carlos Corrales Muñoz ◽  
Alvaro Rendon Gallon ◽  
Araceli Sanchis
Keyword(s):  
Network Traffic ◽  
Domain Knowledge ◽  
Selection Process ◽  
Quality Analysis ◽  
Traffic Classification ◽  
Knowledge Based ◽  
Online Analysis Processing ◽  
Manual Selection ◽  
Online Traffic ◽  
Selection Of

There are several techniques to select a set of traffic features for traffic classification. However, most studies ignore the domain knowledge where traffic analysis or classification is performed and do not consider the always moving information carried in the networks. This paper describes a selection process of online network-traffic discriminators. We obtained 24 traffic features that can be processed on the fly and propose them as a base attribute set for future domain-aware online analysis, processing, or classification. For the selection of a set of traffic discriminators, and to avoid the inconveniences mentioned, we carried out three steps. The first step is a context knowledge-based manual selection of traffic features that meet the condition of being obtained on the fly from the flow. The second step is focused on the quality analysis of previously selected attributes to ensure the relevance of each one when performing a traffic classification. In the third step, the implementation of several incremental learning algorithms verified the usefulness of such attributes in online traffic classification processes. 

scholarly journals CBD: A Deep-Learning-Based Scheme for Encrypted Traffic Classification with a General Pre-Training Method

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8231
Author(s):  
Xinyi Hu ◽  
Chunxiang Gu ◽  
Yihang Chen ◽  
Fushan Wei
Keyword(s):  
Traffic Classification ◽  
Traffic Data ◽  
Training Method ◽  
Proposed Model ◽  
Public Dataset ◽  
Basic Characteristics ◽  
Encrypted Traffic ◽  
Closed Environment ◽  
Better Than

With the rapid increase in encrypted traffic in the network environment and the increasing proportion of encrypted traffic, the study of encrypted traffic classification has become increasingly important as a part of traffic analysis. At present, in a closed environment, the classification of encrypted traffic has been fully studied, but these classification models are often only for labeled data and difficult to apply in real environments. To solve these problems, we propose a transferable model called CBD with generalization abilities for encrypted traffic classification in real environments. The overall structure of CBD can be generally described as a of one-dimension CNN and the encoder of Transformer. The model can be pre-trained with unlabeled data to understand the basic characteristics of encrypted traffic data, and be transferred to other datasets to complete the classification of encrypted traffic from the packet level and the flow level. The performance of the proposed model was evaluated on a public dataset. The results showed that the performance of the CBD model was better than the baseline methods, and the pre-training method can improve the classification ability of the model.

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