Collaborative Learning for Cyberattack Detection in Blockchain Networks-Reference-Cited by-同舟云学术

Collaborative Learning for Cyberattack Detection in Blockchain Networks

Published:2024-07 Issue:7 Volume:54 Page:3920-3933
ISSN:2168-2216
Container-title:IEEE Transactions on Systems, Man, and Cybernetics: Systems
language:
Short-container-title:IEEE Trans. Syst. Man Cybern, Syst.

Author:

Khoa Tran Viet¹^ORCID,Son Do Hai²^ORCID,Hoang Dinh Thai¹^ORCID,Trung Nguyen Linh³,Quynh Tran Thi Thuy³,Nguyen Diep N.¹^ORCID,Ha Nguyen Viet³,Dutkiewicz Eryk¹^ORCID

Affiliation:

1. School of Electrical and Data Engineering, University of Technology Sydney, Sydney, NSW, Australia

2. Advanced Institute of Engineering and Technology, University of Engineering and Technology, Vietnam National University, Hanoi, Vietnam

3. Advanced Institute of Engineering and Technology, the Faculty of Electronics and Telecommunications, and the Insitute of Artificial Intelligence, University of Engineering and Technology, Vietnam National University, Hanoi, Vietnam

Funder

NICT https://www.nict.go.jp/en/index.html through the ASEAN IVO https://www.nict.go.jp/en/asean_ivo/index.html Project “Cyber-Attack Detection and Information Security for Industry 4.0;”

Australian Research Council through the DECRA Project

Joint Technology and Innovation Research Centre—a Partnership between the University of Technology Sydney (UTS) and the University of Engineering and Technology, Vietnam National University, Hanoi

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6221021/10559914/10490138.pdf?arnumber=10490138

Reference41 articles.

1. Bitcoin: A peer-to-peer electronic cash system;Nakamoto

2. Applications of Blockchains in the Internet of Things: A Comprehensive Survey

3. A Survey of Blockchain Technology Applied to Smart Cities: Research Issues and Challenges

4. Interoperability and Synchronization Management of Blockchain-Based Decentralized e-Health Systems

5. Blockchain and Cryptocurrencies: Model, Techniques, and Applications