Encrypted Network Traffic Classification Using Deep Learning Method-Reference-Cited by-同舟云学术

Encrypted Network Traffic Classification Using Deep Learning Method

Published:2022-05-11 Issue: Volume: Page:
ISSN:
Container-title:2022 8th International Conference on Web Research (ICWR)
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Author:

Banihashemi Seyedeh Bahareh¹,Akhtarkavan Ehsan¹

Affiliation:

1. Khatam University,Department of Computer engineering and information technology,Iran,Tehran

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9786062/9786222/09786247.pdf?arnumber=9786247

Reference20 articles.

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

2. Seq2Img: A sequence-to-image based approach towards IP traffic classification using convolutional neural networks

3. End-to-end encrypted traffic classification with onedimensional convolution neural networks;wang;2017 IEEE International Conference on Intelligence and Security Informatics (ISI) ISI,0

4. TLS/SSL Encrypted Traffic Classification with Autoencoder and Convolutional Neural Network

5. Large-scale mobile app identification using deep learning;rezaei;IEEE Access,2019

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1. No Pictures, Please: Using eXplainable Artificial Intelligence to Demystify CNNs for Encrypted Network Packet Classification;Applied Sciences;2024-06-24

2. F3l: an automated and secure function-level low-overhead labeled encrypted traffic dataset construction method for IM in Android;Cybersecurity;2024-01-01

3. Remaining Useful Life Prediction for Lithium-Ion Batteries With a Hybrid Model Based on TCN-GRU-DNN and Dual Attention Mechanism;IEEE Transactions on Transportation Electrification;2023-09

4. Deep Learning Based Network Traffic Analysis Using Modified Hybrid Methodology Comparing with SVM to Improve Accuracy;2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF);2023-01-05

5. A Traffic Classification Method for Internet Application Services Based on DNN;2022 International Conference on Electrical, Computer and Energy Technologies (ICECET);2022-07-20