DNN-CTGAN

Based on the research paper: Enhancing Intrusion Detection through Deep Learning and Generative Adversarial Network (Link to the Research Article is included)

• The repository will include all materials, images, code, and information regarding the paper.

Table of Content

• Getting Started
  • Datasets
  • Prerequisites
• Download and install code
• Authors
• References

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

Datasets

Tools

• Anaconda (Python v3.9.18)
• Jupyter Notebook (v7.0.8)

Prerequisites

• scikit-learn (v1.2.2)
• matplotlib (v3.7.4)
• numpy (v1.23.5)
• pandas (v2.0.3)
• keras (v2.12.0)
• tensorflow (v2.12.0)
• ydata-synthetic (v1.3.1)
• pickle (v0.7.5)
• seaborn (v0.13.2)

Download and install code

• Retrieve the code

git clone https://github.com/LeoMartinezTAMUK/Network_Intrusion_DNN-CTGAN.git

Authors

Leo Martinez III, Md Habibur Rahman, Avdesh Mishra, Mais Nijim, Ayush Goyal and David Hicks.

For any issue please contact Avdesh Mishra, avdesh.mishra@tamuk.edu or myself at leo.martinez@students.tamuk.edu

References

L. Martinez, M. H. Rahman, A. Mishra, M. Nijim, A. Goyal and D. Hicks, "Enhancing Intrusion Detection Through Deep Learning and Generative Adversarial Network," 2024 4th Interdisciplinary Conference on Electrics and Computer (INTCEC), Chicago, IL, USA, 2024, pp. 1-6, doi: 10.1109/INTCEC61833.2024.10602926