CBS: A Deep Learning Approach for Encrypted Traffic Classification With Mixed Spatio-Temporal and Statistical Features-Reference-Cited by-同舟云学术

CBS: A Deep Learning Approach for Encrypted Traffic Classification With Mixed Spatio-Temporal and Statistical Features

Published:2023 Issue: Volume:11 Page:141674-141702
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Seydali Mehdi¹^ORCID,Khunjush Farshad¹^ORCID,Akbari Behzad²,Dogani Javad¹

Affiliation:

1. Department of Computer Science and Engineering and Information Technology, School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran

2. Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10359509.pdf?arnumber=10359509

Reference81 articles.

1. Data Transformation Schemes for CNN-Based Network Traffic Analysis: A Survey

2. Encrypted Network Traffic Classification Using Deep and Parallel Network-in-Network Models

3. MATEC: A lightweight neural network for online encrypted traffic classification

4. Encrypted Traffic Classification with a Convolutional Long Short-Term Memory Neural Network

5. Network traffic classification: Techniques, datasets, and challenges

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