CLSTM-MT (a Combination of 2-Conv CNN and BiLSTM Under the Mean Teacher Collaborative Learning Framework): Encryption Traffic Classification Based on CLSTM (a Combination of 2-Conv CNN and BiLSTM) and Mean Teacher Collaborative Learning

CLSTM-MT (a Combination of 2-Conv CNN and BiLSTM Under the Mean Teacher Collaborative Learning Framework): Encryption Traffic Classification Based on CLSTM (a Combination of 2-Conv CNN and BiLSTM) and Mean Teacher Collaborative Learning

The identification and classification of network traffic are crucial for maintaining network security, optimizing network management, and ensuring reliable service quality. These functions help prevent malicious activities, such as network attacks and illegal intrusions, while supporting the efficie...

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Bibliographic Details
Main Authors: Xiaozong Qiu, Guohua Yan, Lihua Yin
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:Applied Sciences
Subjects:
encrypted traffic classification
convolutional neural network (CNN)
Bidirectional Long Short-Term Memory (BiLSTM)
semi-supervised learning
deep learning
Online Access:https://www.mdpi.com/2076-3417/15/9/5089
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