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

Abstract

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.

A Novel Efficient Dynamic Throttling Strategy for Blockchain-Based Intrusion Detection Systems in 6G-Enabled VSNs

“…The framework gathers information from four main layers, namely, the IoT layer, network layer, fog layer, and cloud layer to monitor and analyse the network traffic among IoT devices. The authors in [ 23 , 24 ] proposed blockchain-based solutions to detect malicious vehicles and IoT devices. The main difference between our work and the work proposed in [ 23 ] is that in the latter case, the malicious behaviour of the vehicles is detected using machine learning (neural networks), while in our case we focus on the lateral movement among the endpoints installed inside the vehicles.…”

“…The authors in [ 23 , 24 ] proposed blockchain-based solutions to detect malicious vehicles and IoT devices. The main difference between our work and the work proposed in [ 23 ] is that in the latter case, the malicious behaviour of the vehicles is detected using machine learning (neural networks), while in our case we focus on the lateral movement among the endpoints installed inside the vehicles. The authors in [ 24 ] addressed the problem of indoor navigation and proposed a new secure communication approach based on blockchain, which is different from the objective of our paper.…”