Network Flow Classification Using Decision Trees

Authors: G. Xu
Date: April 10, 2024

This project focuses on classifying network flow data into various categories using Decision Trees. It involves training and testing a Decision Tree model on network flow datasets and evaluating its performance using key metrics.

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Project Structure

• Cicflowmeter: Processes .pcap files into .csv format.
• Dataset_kali: Contains the dataset and the trained model specific to our dataset.
• Dos_attack_code: Source code for generating DoS attack traffic.
• image: Contains test result visualizations.
• main.py: Main script for training and testing the Decision Tree model.
• train.py: Script for training the Decision Tree model.
• test.py: Script for testing the trained Decision Tree model.
• decision_tree_model.pkl: Pre-trained Decision Tree model on flows_benign_and_DoS.csv.
• model_our_dataset.pkl: Pre-trained Decision Tree model on dataset_kali.csv.
• flows_benign_and_DoS.csv: CSV file containing the dataset for training the model.
• test.csv: CSV file containing the dataset for testing the model.
• README.md: Provides an overview of the project.

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Dependencies

Ensure the following dependencies are installed:

• Python 3.x
• NumPy
• scikit-learn
• matplotlib

Contributions

We welcome contributions! Whether you have feature suggestions or bug reports, please submit an issue or pull request to this repository.