Normal and Malicious Sliding Windows Similarity Analysis Method for Fast and Accurate IDS Against DoS Attacks on In-Vehicle Networks-Reference-Cited by-同舟云学术

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Normal and Malicious Sliding Windows Similarity Analysis Method for Fast and Accurate IDS Against DoS Attacks on In-Vehicle Networks

Published:2020 Issue: Volume:8 Page:42422-42435
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Ohira Shuji^ORCID,Desta Araya Kibrom^ORCID,Arai Ismail^ORCID,Inoue Hiroyuki^ORCID,Fujikawa Kazutoshi^ORCID

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science

Link

http://xplorestaging.ieee.org/ielx7/6287639/8948470/09007444.pdf?arnumber=9007444

Cited by 17 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. FIVADMI: A Framework for In-Vehicle Anomaly Detection by Monitoring and Isolation;Future Internet;2024-08-08

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

3. Essential Technics of Cybersecurity for Intelligent Connected Vehicles: Comprehensive Review and Perspective;IEEE Internet of Things Journal;2023-12-15

4. Development research of operating system for mega-constellation in China;SCIENTIA SINICA Informationis;2023-12-01

5. A survey of deep learning-based intrusion detection in automotive applications;Expert Systems with Applications;2023-07

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