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!