https://github.com/mohammadshabazuddin/fedsecguard-federated_learning_techniques_for_advanced_malware_detection

The project focuses on utilizing federated machine learning to enhance the detection of malware in Internet of Things (IoT) devices. The code includes experiments simulating various configurations where clients collaboratively train deep learning models for malware detection without sharing raw data.
https://github.com/mohammadshabazuddin/fedsecguard-federated_learning_techniques_for_advanced_malware_detection

deep-learning federated-learning iot mechine-learning pycharm python3

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The project focuses on utilizing federated machine learning to enhance the detection of malware in Internet of Things (IoT) devices. The code includes experiments simulating various configurations where clients collaboratively train deep learning models for malware detection without sharing raw data.

• Host: GitHub
• URL: https://github.com/mohammadshabazuddin/fedsecguard-federated_learning_techniques_for_advanced_malware_detection
• Owner: MohammadShabazuddin
• Created: 2023-04-19T23:41:05.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-05-30T19:12:49.000Z (7 months ago)
• Last Synced: 2024-05-30T22:12:28.152Z (7 months ago)
• Topics: deep-learning, federated-learning, iot, mechine-learning, pycharm, python3
• Language: Jupyter Notebook
• Homepage:
• Size: 12.5 MB
• Stars: 3
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
**Project Title: #FedSecGuard - Federated Learning Techniques for Advanced Malware Detection**

🔍 **Overview:**

The project focuses on utilizing federated machine learning to enhance the detection of malware in Internet of Things (IoT) devices. The code includes experiments simulating various configurations where clients collaboratively train deep learning models for malware detection without sharing raw data.

🛠️ **Setup Instructions:**

1. **Recommendations:**

   - Use Linux (preferably Ubuntu 20.04) for easier dataset handling.

   - Employ Python 3.9 for compatibility.

   - Consider using PyCharm for streamlined configuration handling.

2. **Repository Setup:**

   - Clone or download the repository to your machine.

3. **Data Acquisition:**

   - Manually create a `data/` folder in `fed_iot_guard/`.

   - Download and extract the N-BaIoT dataset into the `data/N-BaIoT/` folder.

   - On Linux, execute a command to download the dataset. If not on Linux, find an alternative method.

   - Use the `unar` command to extract files with .rar extension.

4. **Python Library Installation:**

   - Using PyCharm: Create a new Python interpreter, PyCharm will prompt to install requirements from `requirements.txt`.

   - Using virtualenv: Create a new virtual environment, activate it, and install requirements using `pip install -r requirements.txt`.

5. **Usage:**

   - Run experiments with different configurations by adjusting program arguments.

   - Examples include collaborative grid search for classifiers and federated training/testing for autoencoders.

   - Detailed instructions for PyCharm users and terminal commands are provided.

6. **Hyper-Parameters:**

   - Hyper-parameters are defined in the code; fixed parameters are in `constant_params`, varying ones in `varying_params`.

   - After running a grid search, check results using the provided Jupyter notebook `GS Results.ipynb`.

   - Copy the best hyper-parameters for each configuration into `configuration_params` lists.

7. **Modification and Code Structure:**

   - The code is designed for easy extension, organized into files for specific functionalities.

   - Explanation provided for each file's purpose, such as data handling, machine learning functions, and experiments.

8. **Context and Additional Information:**

   - The project uses federated learning to collaborate on model training without sharing raw data, enhancing malware detection for IoT devices.

   - Detailed insights into the structure, modifications, and usage are provided for better understanding.

📚 **References:**

- [N-BaIoT Dataset](https://archive.ics.uci.edu/ml/datasets/detection_of_IoT_botnet_attacks_N_BaIoT)

- [Related Publication](https://www.sciencedirect.com/science/article/pii/S1389128621005582)

  

## Contact

_Connect with me through various portals :_

  

    

    Social Media

    Username

    Link

  

  

    

    Email

    [email protected]

    Email

  

  

    

    LinkedIn

    Shabazuddin Mohammad

    LinkedIn

  

  

    

    Instagram

    shabaz_uddin

    Instagram

  

  

    

    Facebook

    Shabaz

    Facebook

  

  

    

    Twitter

    shabazuddin786

    Twitter

  

I'm always open to collaboration and new opportunities! Feel free to reach out and connect with me. 🌟

🚀 **Note:** The project requires careful attention to setup details and may involve lengthy execution times for experiments. Adjust parameters as needed for your environment.