Federated Learning-Based Intrusion Detection System for IoT Environments with Locally Adapted Model-Reference-Cited by-同舟云学术

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Federated Learning-Based Intrusion Detection System for IoT Environments with Locally Adapted Model

Published:2023-07 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE 10th International Conference on Cyber Security and Cloud Computing (CSCloud)/2023 IEEE 9th International Conference on Edge Computing and Scalable Cloud (EdgeCom)
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Author:

Roy Souradip¹,Li Juan¹,Bai Yan²

Affiliation:

1. North Dakota State University,Department of Computer Science,Fargo,USA

2. University of Washington Tacoma,School of Engineering and Technology,Tacoma,USA

Funder

National Science Foundation

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10195194/10195273/10195366.pdf?arnumber=10195366

Reference28 articles.

1. Intrusion Detection System Using Deep Neural Network for In-Vehicle Network Security

2. Distributed attack detection scheme using deep learning approach for Internet of Things

3. A Host-Based Anomaly Detection Framework Using XGBoost and LSTM for IoT Devices

4. Transfer Learning Approach for Botnet Detection Based on Recurrent Variational Autoencoder

5. A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. FedIoTect: Federated Machine Learning for Collaborative Internet of Things Threat Detection;2024-02-19

2. A CNN-Based Adaptive Federated Learning Approach for Communication Jamming Recognition;Electronics;2023-08-13

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