A Comparative Study of Lightweight Machine Learning Techniques for Cyber-Attacks Detection in Blockchain-Enabled Industrial Supply Chain-Reference-Cited by-同舟云学术

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A Comparative Study of Lightweight Machine Learning Techniques for Cyber-Attacks Detection in Blockchain-Enabled Industrial Supply Chain

Published:2024 Issue: Volume:12 Page:102481-102491
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Ismail Shereen¹^ORCID,Dandan Salah²^ORCID,Dawoud Diana W.³^ORCID,Reza Hassan²^ORCID

Affiliation:

1. Merit Network Inc., University of Michigan, Ann Arbor, MI, USA

2. School of Electrical Engineering and Computer Science, University of North Dakota, Grand Forks, ND, USA

3. College of Engineering and Information Technology, University of Dubai, Dubai, United Arab Emirates

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/6287639/10380310/10606226.pdf?arnumber=10606226

Reference47 articles.

1. Impact of COVID-19 on IoT adoption in healthcare, smart homes, smart buildings, smart cities, transportation and industrial IoT;Umair;Sensors,2021

2. Security Challenges of Blockchain-Based Supply Chain Systems

3. Toward an Intelligent Blockchain IoT-Enabled Fish Supply Chain: A Review and Conceptual Framework

4. Backstabber’s Knife Collection: A Review of Open Source Software Supply Chain Attacks

5. Cybersecurity Threats to and Cyberattacks on Critical Infrastructure: a Legal Perspective

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