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E- conference s e r I e s premium best q uality guaran tee serviceBog'liq Italy Econference Series Nov 2023 package
Proceedings of International Educators Conference
Hosted online from Rome, Italy.
Date: 25
th
Nov., 2023
ISSN: 2835-396X Website: econferenceseries.com
85
selection and extraction in traditional methods and has higher accuracy
and flexibility.
4.
Network traffic classification platforms: Researchers have developed
a number of network traffic classification platforms, such as OpenDPI,
L7-filter, and DPI-LIB, to facilitate and accelerate traffic classification
research. These platforms provide convenient traffic classification tools
and datasets, which can help researchers to conduct traffic classification
research more quickly.
Proposed solution.
Deep learning has been proven to be very effective in network traffic
classification tasks. However, the increasing diversity of network traffic
and encrypted traffic demands continuous improvement and optimization
of these models. In order to further improve the classification accuracy
of encrypted and network application traffic, this paper proposes the use
of multiple deep learning models to enhance network traffic classification.
The paper also considers aspects such as dataset, feature selection, model
optimization, and model fusion. The paper emphasizes the importance of
dataset quality and diversity and the need to collect valid data. For
different types of traffic, appropriate end-to-end representation learning
methods should be used. Using various model optimization techniques,
such as adaptive learning rate, dropout, and batch normalization, can
improve model performance. In addition, using multiple model fusion
methods, such as voting, weighted averaging, and stacking, can further
improve model performance. The comprehensive use of these technologies
and methods can effectively improve the accuracy and generalization
ability of network traffic classification, especially in the area of encrypted
and network application traffic classification. In this section, a deep
learning-based spatiotemporal correlation network flow classification model
is proposed. The model combines the advantages of convolutional neural
network (CNN) and long short-term memory (LSTM). The model
framework is shown in Fig. 1.
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