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

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Summary
This summary is machine-generated.

This study introduces a consortium blockchain-enabled conventional neural network (CBCNN) to detect malicious vehicles. The CBCNN model enhances security and reduces costs in the Internet of Vehicles (IoVs).

Keywords:
IoT devicesconsortium blockchain technologyconventional neural networksdeep learningmalicious vehicle detectionmulti-label

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Area of Science:

  • Artificial Intelligence and Machine Learning
  • Cybersecurity in Transportation
  • Blockchain Technology Applications

Background:

  • Deep learning models are vital across many fields but vulnerable to malicious attacks.
  • Protecting AI models, especially in critical applications like vehicle detection, is essential.
  • Existing methods may lack robust security against parameter manipulation in vehicle networks.

Purpose of the Study:

  • To propose a novel four-layered paradigm, the consortium blockchain-enabled conventional neural network (CBCNN), for detecting malicious vehicles.
  • To ensure tamper-proof protection against parameter manipulation attacks within the Internet of Vehicles (IoVs).
  • To enhance the security and reliability of vehicle management systems through multi-label classification and blockchain integration.

Main Methods:

  • Developed a four-layered CBCNN model comprising convolutional neural network (CNN)-IoT, spatial pyramid pooling, fully connected layers, and a consortium blockchain.
  • Implemented a proof-of-luck mechanism within the consortium blockchain to optimize energy consumption for vehicles.
  • Utilized C++ for implementation and the ns-3.34 platform with the ns3-ai module for simulation and anomaly detection in IoVs.

Main Results:

  • The CBCNN approach demonstrated superior performance in malicious label detection compared to state-of-the-art methods.
  • Achieved higher average accuracy and a reduced loss ratio in identifying malicious vehicles.
  • Showcased significant cost reduction benefits while ensuring tamper-proof protection of the AI model.

Conclusions:

  • The proposed CBCNN model effectively identifies malicious vehicles and mitigates associated risks in IoVs.
  • Consortium blockchain integration provides robust, tamper-proof security for deep learning models in vehicular systems.
  • The CBCNN approach offers an energy-efficient and cost-effective solution for securing the Internet of Vehicles.