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A Hybrid Machine Learning-based Data-Centric Cybersecurity Detection in the 5G-Enabled IoT
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  • Lingcheng ZENG,
  • Yunzhu AN,
  • Heng ZHOU,
  • Qifeng LUO,
  • Yuede LIN,
  • Bing DAI
Lingcheng ZENG
Zhongshan Power Supply Bureau of Guangdong Power Grid Co Ltd
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Yunzhu AN
Shandong University of Technology

Corresponding Author:anyunzhu2006@163.com

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Heng ZHOU
Zhongshan Power Supply Bureau of Guangdong Power Grid Co Ltd
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Qifeng LUO
Zhongshan Power Supply Bureau of Guangdong Power Grid Co Ltd
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Yuede LIN
Zhongshan Power Supply Bureau of Guangdong Power Grid Co Ltd
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Bing DAI
Zhongshan Power Supply Bureau of Guangdong Power Grid Co Ltd
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Abstract

By increasing smart objects as high-potential computing devices and 5G technology, the Internet of Things (IoT) has emerging technology to provide a data-centric infrastructure for detecting safe device-to-device communications by supporting security and privacy issues. As 5G-enabled communication technology, Vehicle-to-Vehicle (V2V) communication provides a wirelessly exchange information connection between smart vehicles, intelligent devices and cloud-edge computing environment. This connection should be established with a safe and secured run-time protection system to avoid many critical anomalies and misbehavior problems. Detecting run-time malicious transformations with data-centric misbehaving reactions is a main challenge for autonomous vehicle communications with 5G-enabled communication technology. This paper provides a hybrid Genetic Algorithm-based Ensemble Bagged Trees (GA-EBT) algorithm for a data-centric misbehavior detection approach to support the V2V communications against malicious and misbehavior transactions. For evaluation of the proposed algorithm, four real test-cases are applied for messaging injection attacks in the V2V environments with compare to the state-of-the-art machine learning algorithms. The experimental results show that the proposed hybrid approach can achieve to optimal high rate accuracy factor with 99.999, precision and recall factors with 100% and F1-Score factor with 100% to detect unexpected cyber-attacks for the V2V communications in the IoT environment.
Submitted to Security and Privacy
05 Jun 2024Reviewer(s) Assigned
23 Jun 2024Review(s) Completed, Editorial Evaluation Pending
01 Sep 20241st Revision Received
04 Sep 2024Assigned to Editor
04 Sep 2024Submission Checks Completed
04 Sep 2024Review(s) Completed, Editorial Evaluation Pending
04 Sep 2024Reviewer(s) Assigned
14 Sep 2024Editorial Decision: Accept