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Malicious Vehicle Detection Using Layer-Based Paradigm and the Internet of Things

Deep learning algorithms have a wide range of applications, including cancer diagnosis, face and speech recognition, object recognition, etc. It is critical to protect these models since any changes to them can result in serious losses in a variety of ways. This article proposes the consortium block...

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Autores principales: Razaque, Abdul, Bektemyssova, Gulnara, Yoo, Joon, Alotaibi, Aziz, Ali, Mohsin, Amsaad, Fathi, Amanzholova, Saule, Alshammari, Majid
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10386004/
https://www.ncbi.nlm.nih.gov/pubmed/37514847
http://dx.doi.org/10.3390/s23146554
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author Razaque, Abdul
Bektemyssova, Gulnara
Yoo, Joon
Alotaibi, Aziz
Ali, Mohsin
Amsaad, Fathi
Amanzholova, Saule
Alshammari, Majid
author_facet Razaque, Abdul
Bektemyssova, Gulnara
Yoo, Joon
Alotaibi, Aziz
Ali, Mohsin
Amsaad, Fathi
Amanzholova, Saule
Alshammari, Majid
author_sort Razaque, Abdul
collection PubMed
description Deep learning algorithms have a wide range of applications, including cancer diagnosis, face and speech recognition, object recognition, etc. It is critical to protect these models since any changes to them can result in serious losses in a variety of ways. This article proposes the consortium blockchain-enabled conventional neural network (CBCNN), a four-layered paradigm for detecting malicious vehicles. Layer-1 is a convolutional neural network-enabled Internet-of-Things (IoT) model for the vehicle; Layer-2 is a spatial pyramid polling layer for the vehicle; Layer-3 is a fully connected layer for the vehicle; and Layer-4 is a consortium blockchain for the vehicle. The first three layers accurately identify the vehicles, while the final layer prevents any malicious attempts. The primary goal of the four-layered paradigm is to successfully identify malicious vehicles and mitigate the potential risks they pose using multi-label classification. Furthermore, the proposed CBCNN approach is employed to ensure tamper-proof protection against a parameter manipulation attack. The consortium blockchain employs a proof-of-luck mechanism, allowing vehicles to save energy while delivering accurate information about the vehicle’s nature to the “vehicle management system.” C++ coding is employed to implement the approach, and the ns-3.34 platform is used for simulation. The ns3-ai module is specifically utilized to detect anomalies in the Internet of Vehicles (IoVs). Finally, a comparative analysis is conducted between the proposed CBCNN approach and state-of-the-art methods. The results confirm that the proposed CBCNN approach outperforms competing methods in terms of malicious label detection, average accuracy, loss ratio, and cost reduction.
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spelling pubmed-103860042023-07-30 Malicious Vehicle Detection Using Layer-Based Paradigm and the Internet of Things Razaque, Abdul Bektemyssova, Gulnara Yoo, Joon Alotaibi, Aziz Ali, Mohsin Amsaad, Fathi Amanzholova, Saule Alshammari, Majid Sensors (Basel) Article Deep learning algorithms have a wide range of applications, including cancer diagnosis, face and speech recognition, object recognition, etc. It is critical to protect these models since any changes to them can result in serious losses in a variety of ways. This article proposes the consortium blockchain-enabled conventional neural network (CBCNN), a four-layered paradigm for detecting malicious vehicles. Layer-1 is a convolutional neural network-enabled Internet-of-Things (IoT) model for the vehicle; Layer-2 is a spatial pyramid polling layer for the vehicle; Layer-3 is a fully connected layer for the vehicle; and Layer-4 is a consortium blockchain for the vehicle. The first three layers accurately identify the vehicles, while the final layer prevents any malicious attempts. The primary goal of the four-layered paradigm is to successfully identify malicious vehicles and mitigate the potential risks they pose using multi-label classification. Furthermore, the proposed CBCNN approach is employed to ensure tamper-proof protection against a parameter manipulation attack. The consortium blockchain employs a proof-of-luck mechanism, allowing vehicles to save energy while delivering accurate information about the vehicle’s nature to the “vehicle management system.” C++ coding is employed to implement the approach, and the ns-3.34 platform is used for simulation. The ns3-ai module is specifically utilized to detect anomalies in the Internet of Vehicles (IoVs). Finally, a comparative analysis is conducted between the proposed CBCNN approach and state-of-the-art methods. The results confirm that the proposed CBCNN approach outperforms competing methods in terms of malicious label detection, average accuracy, loss ratio, and cost reduction. MDPI 2023-07-20 /pmc/articles/PMC10386004/ /pubmed/37514847 http://dx.doi.org/10.3390/s23146554 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Razaque, Abdul
Bektemyssova, Gulnara
Yoo, Joon
Alotaibi, Aziz
Ali, Mohsin
Amsaad, Fathi
Amanzholova, Saule
Alshammari, Majid
Malicious Vehicle Detection Using Layer-Based Paradigm and the Internet of Things
title Malicious Vehicle Detection Using Layer-Based Paradigm and the Internet of Things
title_full Malicious Vehicle Detection Using Layer-Based Paradigm and the Internet of Things
title_fullStr Malicious Vehicle Detection Using Layer-Based Paradigm and the Internet of Things
title_full_unstemmed Malicious Vehicle Detection Using Layer-Based Paradigm and the Internet of Things
title_short Malicious Vehicle Detection Using Layer-Based Paradigm and the Internet of Things
title_sort malicious vehicle detection using layer-based paradigm and the internet of things
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10386004/
https://www.ncbi.nlm.nih.gov/pubmed/37514847
http://dx.doi.org/10.3390/s23146554
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