This project implements a quantum machine learning model for anomaly detection in network traffic using TensorFlow Quantum and Cirq. It demonstrates a Variational Quantum Classifier (VQC) for binary classification of network traffic as normal or anomalous.

TensorFlow Quantum is not supported natively on Apple Silicon (M1/M2/M3) Macs.

• If you are using an Apple Silicon Mac, you will not be able to install TensorFlow Quantum directly via pip.
• The recommended way to run this project is via Google Colab, which provides a compatible environment and free access to GPUs.

1. Open Google Colab:

   • Go to https://colab.research.google.com/

2. Upload the Project Files:

   • Click on the folder icon in the left sidebar.
   • Click the upload icon and upload main.py and requirements.txt from this repository.

3. Install Dependencies:

   • At the top of your Colab notebook, run the following cell to install all required packages:

     !pip install -r requirements.txt

4. Run the Main Script:

   • In a new cell, run:

     !python main.py

5. (Optional) Edit and Experiment:

   • You can edit main.py directly in Colab or upload new versions as needed.

• main.py — Main script containing the quantum anomaly detection pipeline.
• requirements.txt — List of required Python packages.

• The script will generate a synthetic sample dataset (CICIDS2017_sample.csv) if not present. Request are required https://www.yorku.ca/research/bccc/ucs-technical/cybersecurity-datasets-cds/ https://www.unb.ca/cic/datasets/ids-2017.html

MIT