Anti-flowguard : Practically disproving the claim made in Flowguard paper

In this repository, we have provided the replication package to disprove the claim made by the FlowGuard: An Intelligent Edge Defense Mechanism Against IoT DDoS Attacks. This project aims to train and evaluate several machine learning models using a given dataset. The primary focus is on classification tasks using different classifiers such as Decision Tree, Naive Bayes, and Random Forest. Additionally, the project includes feature importance analysis and visualization.

Requirements

• Python 3.8+
• Required libraries are listed in the requirements.txt file.

Installation

1. Clone the repository:

   git clone https://github.com/SmartSecLab/anti-flowguard.git
   cd anti-flowguard

2. Create a virtual environment and activate it:

   python3 -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Install the dependencies:

   pip install -r requirements.txt

Configuration

The configuration file config.yaml should be placed in the root directory. It should include the following keys:

• data: Path to the dataset (CSV file).
• split: Dictionary containing the test_size for train-test split.

Usage

Ensure that the configuration file config.yaml is correctly set up. Run the script:

python main.py

Classifiers

The script includes the following classifiers:

Decision Tree Classifier
Naive Bayes Classifier
Random Forest Classifier
LSTM Classifier

Outputs

classification_report.txt: Contains the classification report for the evaluated models.
figure/feature_importances.png: Visualization of feature importances.

Contributing

Contributions are welcome! Please open an issue or submit a pull request for any improvements or fixes.