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المؤلفAliya, Tabassum
المؤلفErbad, Aiman
المؤلفLebda, Wadha
المؤلفMohamed, Amr
المؤلفGuizani, Mohsen
تاريخ الإتاحة2023-05-21T09:17:53Z
تاريخ النشر2022-06-18
اسم المنشورComputer Communications
المعرّفhttp://dx.doi.org/10.1016/j.comcom.2022.06.015
الاقتباسTabassum, A., Erbad, A., Lebda, W., Mohamed, A., & Guizani, M. (2022). Fedgan-ids: Privacy-preserving ids using gan and federated learning. Computer Communications, 192, 299-310.
الرقم المعياري الدولي للكتاب0140-3664
معرّف المصادر الموحدhttps://www.sciencedirect.com/science/article/pii/S0140366422002171
معرّف المصادر الموحدhttp://hdl.handle.net/10576/43122
الملخصFederated Learning (FL) is a promising distributed training model that aims to minimize the data sharing to enhance privacy and performance. FL requires sufficient and diverse training data to build efficient models. Lack of data balance as seen in rare classes affects the model accuracy. Generative Adversarial Networks (GAN) are remarkable in data augmentation to balance the available training data. In this article, we propose a novel Federated Deep Learning (DL) Intrusion Detection System (IDS) using GAN, named FEDGAN-IDS, to detect cyber threats in smart Internet of Things (IoT) systems; smarthomes, smart e-healthcare systems and smart cities. We distribute the GAN network over IoT devices to act as a classifier and train using augmented local data. We compare the convergence and accuracy of our model with other federated intrusion detection models. Extensive experiments with multiple datasets demonstrates the effectiveness of the proposed FEDGAN-IDS. The model performs better and converges earlier than the state-of-the-art standalone IDS.
راعي المشروعThis publication was made possible by NPRP grant 7-1469-1-273 from the Qatar National Research Fund (a member of Qatar Foundation).
اللغةen
الناشرElsevier
الموضوعDeep Learning (DL)
Federated Learning (FL)
Generative Adversarial Network (GAN)
Internet of Things (IoT)
Intrusion Detection System (IDS)
العنوانFEDGAN-IDS: Privacy-preserving IDS using GAN and Federated Learning
النوعArticle
الصفحات299-310
رقم المجلد192
dc.accessType Open Access


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