Thales, Zetane and Polytechnique Mtl | Anomaly detection in network data

The aim of the project is to design and develop a software framework for network anomaly detection using different quantum machine learning models, including qGANs, kernel methods and quantum reservoirs.

The 3-year project is a collaboration between Thales, Zetane Systems, Polytechnique Montréal and the Université de Sherbrooke.

Publications

1. Kalfon, Benjamin, et al. "Successive data injection in conditional quantum GAN applied to time series anomaly detection." IET Quantum Communication, (2024).
2. Vieloszynski, Alexis, et al. "LatentQGAN: A Hybrid QGAN with Classical Convolutional Autoencoder." 2024 IEEE 10th World Forum on Internet of Things (WF-IoT). IEEE, (2024).
3. Aaraba, Abdallah, et al. "QuaCK-TSF: Quantum-Classical Kernelized Time Series Forecasting." 2024 IEEE International Conference on Quantum Computing and Engineering (QCE). Vol. 1. IEEE, (2024).