Predicting Device Anomalous Condition in a Collaborated Industrial Environment-Reference-Cited by-同舟云学术

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Predicting Device Anomalous Condition in a Collaborated Industrial Environment

Published:2024-01 Issue:1 Volume:20 Page:390-398
ISSN:1551-3203
Container-title:IEEE Transactions on Industrial Informatics
language:
Short-container-title:IEEE Trans. Ind. Inf.

Author:

Alvi Unaiza¹^ORCID,Malik Asad Waqar¹^ORCID,Rahman Anis Ur²^ORCID,Khattak Muazzam A. Khan³,Khan Samee U.⁴^ORCID

Affiliation:

1. National University of Sciences and Technology (NUST), Islamabad, Pakistan

2. Department of Biological and Environmental Sciences, University of Jyväskylä, Jyväskylä, Finland

3. Quaid-i-Azam University, Islamabad, Pakistan

4. Mississippi State University, Mississippi State, MS, USA

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Information Systems,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/9424/10355676/10086701.pdf?arnumber=10086701

Reference14 articles.

1. Classification of Attack Types for Intrusion Detection Systems Using a Machine Learning Algorithm

2. Microscopic Traffic Simulation using SUMO

3. Blockchain-Enabled Adaptive-Learning-Based Resource-Sharing Framework for IIoT Environment

4. Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects

5. CorrAUC: A Malicious Bot-IoT Traffic Detection Method in IoT Network Using Machine-Learning Techniques

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