A two-dimensional convolutional gated recurrent unit based method for classifying malicious encrypted traffic-Reference-Cited by-同舟云学术

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A two-dimensional convolutional gated recurrent unit based method for classifying malicious encrypted traffic

Published:2023-05-26 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE 3rd International Conference on Electronic Technology, Communication and Information (ICETCI)
language:
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Author:

Jianhua Chen¹,Zhaohui Liu²,Xin Deng¹,Xinyue Lu³

Affiliation:

1. University of South China,School of Computer Science,Hengyang,China

2. University of South China,College of Innovation and Entrepreneurship,Hengyang,China

3. Northeastern University at Qinhuangdao,School of Computer and Communication Engineering,Qinhuangdao,China

Funder

Chinese Academy of Sciences

Research and Development

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10176329/10176364/10176820.pdf?arnumber=10176820

Reference22 articles.

1. HAST-IDS: Learning Hierarchical Spatial-Temporal Features Using Deep Neural Networks to Improve Intrusion Detection

2. Malware traffic classification using convolutional neural network for representation learning

3. Malicious encrypted traffic identification based on hierarchical spatio-temporal features and multi-headed attention[J];jiang;Computer Engineering,2021

4. CLD-Net: A Network Combining CNN and LSTM for Internet Encrypted Traffic Classification

5. Malicious encrypted traffic detection based on LSTM recurrent neural network[J];zou;Computer Applications and Software,2020

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