Malicious Vehicle Detection Using Layer-Based Paradigm and the Internet of Things
Razaque A. Bektemyssova G. Yoo J. Alotaibi A. Ali M. Amsaad F. Amanzholova S. Alshammari M.
July 2023Multidisciplinary Digital Publishing Institute (MDPI)
Sensors
2023#23Issue 14
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.
consortium blockchain technology , conventional neural networks , deep learning , IoT devices , malicious vehicle detection , multi-label
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School of Computing, Gachon University, Seongnam-si, 13120, South Korea
Department of Computer Engineering, International Information Technology University, Almaty, 050000, Kazakhstan
Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia
Computer Science Department, Wright State University, Fairborn, 45435, OH, United States
Department of Cybersecurity, International Information Technology University, Almaty, 050000, Kazakhstan
Department of Information Technology, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia
School of Computing
Department of Computer Engineering
Department of Computer Science
Computer Science Department
Department of Cybersecurity
Department of Information Technology
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