Federated-LSTM based Network Intrusion Detection Method for Intelligent Connected Vehicles-Reference-Cited by-同舟云学术

Federated-LSTM based Network Intrusion Detection Method for Intelligent Connected Vehicles

Published:2022-05-16 Issue: Volume: Page:
ISSN:
Container-title:ICC 2022 - IEEE International Conference on Communications
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Author:

Yu Tianqi¹,Hua Guodong²,Wang Huaisheng¹,Yang Jianfeng¹,Hu Jianling¹

Affiliation:

1. Soochow University,School of Electronic and Information Engineering,Suzhou,China,215006

2. Jiangsu Smart Travel Future Automobile Research Institute,Nanjing,China,211111

Funder

Natural Science Foundation of Jiangsu Province

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9837954/9838246/09838655.pdf?arnumber=9838655

Reference17 articles.

1. A Distributed Anomaly Detection System for In-Vehicle Network Using HTM

2. Mobile Edge Assisted Literal Multi-Dimensional Anomaly Detection of In-Vehicle Network Using LSTM

3. ID Sequence Analysis for Intrusion Detection in the CAN bus using Long Short Term Memory Networks

4. Federated Learning With Blockchain for Autonomous Vehicles: Analysis and Design Challenges

5. A Hierarchical Blockchain-Enabled Federated Learning Algorithm for Knowledge Sharing in Internet of Vehicles;chai;IEEE Trans Intell Transp Syst,2020

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