FL-IDS: Federated Learning-Based Intrusion Detection System Using Edge Devices for Transportation IoT-Reference-Cited by-同舟云学术

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FL-IDS: Federated Learning-Based Intrusion Detection System Using Edge Devices for Transportation IoT

Published:2024 Issue: Volume:12 Page:52215-52226
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Bhavsar Mansi H.¹^ORCID,Bekele Yohannes B.¹^ORCID,Roy Kaushik²^ORCID,Kelly John C.¹,Limbrick Daniel¹

Affiliation:

1. Department of Electrical and Computer Engineering, North Carolina Agricultural and Technical State University, Greensboro, NC, USA

2. Computer Science Department, North Carolina Agricultural and Technical State University, Greensboro, NC, USA

Funder

Cisco Systems

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6287639/10380310/10495036.pdf?arnumber=10495036

Reference61 articles.

1. Identifying Intrusion Attempts on Connected and Autonomous Vehicles: A Survey

2. Survey on cyber security of CAV

3. Machine-Learning-Enabled Cooperative Perception for Connected Autonomous Vehicles: Challenges and Opportunities

4. Shallow and deep networks intrusion detection system: A taxonomy and survey;Hodo;arXiv:1701.02145,2017

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. An Intelligent Attack Detection Framework for the Internet of Autonomous Vehicles with Imbalanced Car Hacking Data;World Electric Vehicle Journal;2024-08-08

2. A Hybrid Feature Selection Model for Anomaly-Based Intrusion Detection in IoT Networks;2024 International Telecommunications Conference (ITC-Egypt);2024-07-22

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