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DefendX:

An AI-powered cybersecurity solution designed to enhance the security of edge devices by detecting unusual login activities, background-running malware, and abnormal traffic at routers.

Features

  • Unusual Login Activity Detection: - Monitor and flag suspicious login attempts on edge devices.
  • Malware Detection: - Identify and block malware processes running in the background.
  • Router-Level Traffic Monitoring: - Analyze and detect unusual traffic patterns to prevent unauthorized access.

Project Structure

  • ..data.py file: 
     Code for generating synthetic data used to train the model.
  • ..model.py: 
    Code for creating and training AI models using Llama 3.1 and Phi-3.
  • ..app.py: 
    Frontend implementation using Streamlit for user interaction.

How to Get Started

To get started with this project, follow the steps below:

  • Clone the Repository

    git clone https://github.com/ayesha-asad07/edge-runners-hackathon.git
cd edge-runners-hackathon

  • Install Requirements

    • Make sure you have Python 3.x installed.

    • Then install the required dependencies:

      pip install -r requirements.txt

  • After installing, you can run the application:

     streamlit run app.py

Technologies Used

  • Streamlit
  • Llama 3.1:
  • Phi-3 Models:

Regards

A special thanks to all the contributors and mentors who guided the development of this project during the Edge-Runners Hackathon. Your support is invaluable!