Tabular-to-Image Transformations for the Classification of Anonymous Network Traffic Using Deep Residual Networks-Reference-Cited by-同舟云学术

Tabular-to-Image Transformations for the Classification of Anonymous Network Traffic Using Deep Residual Networks

Published:2023 Issue: Volume:11 Page:113100-113113
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Briner Nathan¹^ORCID,Cullen Drake¹^ORCID,Halladay James¹^ORCID,Miller Darrin¹,Primeau Riley¹,Avila Abraham¹^ORCID,Basnet Ram¹^ORCID,Doleck Tenzin²^ORCID

Affiliation:

1. Department of Computer Science and Engineering, Colorado Mesa University (CMU), Grand Junction, CO, USA

2. Faculty of Education, Simon Fraser University, Burnaby, BC, Canada

Funder

State of Colorado through funds appropriated for Cybersecurity Law Dubbed “Cyber Coding Cryptology for State Records.”

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/ielx7/6287639/10005208/10278393.pdf?arnumber=10278393

Reference43 articles.

1. DarknetSec: A novel self-attentive deep learning method for darknet traffic classification and application identification

2. Deep Residual Learning for Image Recognition

3. Converting tabular data into images for deep learning with convolutional neural networks

4. XGBoost: Its genealogy, its architectural features, and its innovation;suginoo,2022

5. Dark Web Analytics: A Comparative Study of Feature Selection and Prediction Algorithms

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