Federated Graph Neural Network for Fast Anomaly Detection in Controller Area Networks-Reference-Cited by-同舟云学术

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Federated Graph Neural Network for Fast Anomaly Detection in Controller Area Networks

Published:2023 Issue: Volume:18 Page:1566-1579
ISSN:1556-6013
Container-title:IEEE Transactions on Information Forensics and Security
language:
Short-container-title:IEEE Trans.Inform.Forensic Secur.

Author:

Zhang Hengrun¹^ORCID,Zeng Kai²^ORCID,Lin Shuai³

Affiliation:

1. Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, China

2. Department of Electrical and Computer Engineering, George Mason University, Fairfax, VA, USA

3. School of Economics and Management, Shanghai Institute of Technology, Shanghai, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Safety, Risk, Reliability and Quality

Link

http://xplorestaging.ieee.org/ielx7/10206/9970396/10026810.pdf?arnumber=10026810

Reference59 articles.

1. Cyber-Security for the Controller Area Network (CAN) Communication Protocol

2. – vatiCAN – Vetted, Authenticated CAN Bus

3. CAN-Bus Attack Detection With Deep Learning

4. Byzantine-Resilient Secure Federated Learning

5. CANnolo: An Anomaly Detection System Based on LSTM Autoencoders for Controller Area Network

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3. Private and utility enhanced intrusion detection based on attack behavior analysis with local differential privacy on IoV;Computer Networks;2024-08

4. Reconfigurable CAN Intrusion Detection and Response System;Electronics;2024-07-07

5. SIDiLDNG: A similarity-based intrusion detection system using improved Levenshtein Distance and N-gram for CAN;Computers & Security;2024-07

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