GANomaly for anomaly detection (w multiple GPU support)

版本 1.0.1 (11.9 KB) 作者: Da Huang

MATLAB implementation of the GANomaly network architecture for anomaly detection problems.

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更新时间 2024/6/30

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The code package comes with an example that does the follow: 1. loads data from file, 2. construct GANomaly network, 3. train the GANomaly network, and 4. use trained GANomaly for anomaly detection.

The code comes with multiple GPU support.

The example in the main.m can run on the MNIST datset for readiness check, or on your own dataset.

Condition of use: plese cite the following paper

D. Huang and A. Al-Hourani, "Physical Layer Spoof Detection and Authentication for IoT Devices Using Deep Learning Methods," in IEEE Transactions on Machine Learning in Communications and Networking, vol. 2, pp. 841-854, 2024, doi: 10.1109/TMLCN.2024.3417806.

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P.S.:

1. The MNIST dataset needs to be downloaded and put in the same folder. The dataset can be found in the following link:

https://www.kaggle.com/datasets/hojjatk/mnist-dataset

2. The helper function for processing the MNIST dataset is modified based on the following MATLAB example:

https://www.mathworks.com/help/releases/R2019b/deeplearning/examples/train-a-variational-autoencoder-vae-to-generate-images.html

3. GANomaly is implemented based on the following Github project:

https://github.com/samet-akcay/ganomaly

引用格式

Huang, Da, and Akram Al-Hourani. “Physical Layer Spoof Detection and Authentication for IoT Devices Using Deep Learning Methods.” IEEE Transactions on Machine Learning in Communications and Networking, vol. 2, Institute of Electrical and Electronics Engineers (IEEE), 2024, pp. 841–54, doi:10.1109/tmlcn.2024.3417806.

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MATLAB 版本兼容性

创建方式 R2023b

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版本	已发布	发行说明
1.0.1	2024/6/30	Update linked publication	下载
1.0.0	2024/2/13		下载

MLA	Huang, Da, and Akram Al-Hourani. “Physical Layer Spoof Detection and Authentication for IoT Devices Using Deep Learning Methods.” IEEE Transactions on Machine Learning in Communications and Networking, vol. 2, Institute of Electrical and Electronics Engineers (IEEE), 2024, pp. 841–54, doi:10.1109/tmlcn.2024.3417806.
APA	Huang, D., & Al-Hourani, A. (2024). Physical Layer Spoof Detection and Authentication for IoT Devices Using Deep Learning Methods. IEEE Transactions on Machine Learning in Communications and Networking, 2, 841–854. Institute of Electrical and Electronics Engineers (IEEE). Retrieved from http://dx.doi.org/10.1109/TMLCN.2024.3417806
BibTeX	@article{Huang_2024, title={Physical Layer Spoof Detection and Authentication for IoT Devices Using Deep Learning Methods}, volume={2}, ISSN={2831-316X}, url={http://dx.doi.org/10.1109/TMLCN.2024.3417806}, DOI={10.1109/tmlcn.2024.3417806}, journal={IEEE Transactions on Machine Learning in Communications and Networking}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Huang, Da and Al-Hourani, Akram}, year={2024}, pages={841–854} }

GANomaly for anomaly detection (w multiple GPU support)