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Published on: September 8, 2023
Quantum-resistant hybrid encryption framework for secure and intelligent Vehicle-to-Vehicle communication using deep
Tawfiq Hasanin1, Zahyah H Alharbi2, Majdy M Eltahir3
1Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.
Scientific Reports
|May 6, 2026
Summary
A new method enhances vehicular communication security using Quantum-Resistant Hybrid Encryption for Secure Vehicle-to-Vehicle Communication Using Deep Representation Learning (QRHEV2V-DRL). This approach achieves 99.62% accuracy in detecting malicious attacks in vehicle networks.
Area of Science:
- Computer Science
- Cybersecurity
- Network Engineering
Background:
- Vehicular communication, particularly vehicle-to-vehicle (V2V), is crucial for modern applications but faces security challenges due to high mobility and network dynamics.
- Vehicular Ad Hoc Networks (VANETs) are susceptible to malicious attacks like fabrication attacks, leading to jamming and increased network delays.
- Existing security measures struggle to provide long-term data protection against evolving cyber threats in dynamic V2V environments.
Purpose of the Study:
- To propose a novel Quantum-Resistant Hybrid Encryption for Secure Vehicle-to-Vehicle Communication Using Deep Representation Learning (QRHEV2V-DRL) method.
- To enhance the security and efficiency of V2V communication networks against sophisticated cyber threats.
- To ensure long-term data security and integrity in vehicular communication systems.
Main Methods:
- Cluster formation using the Kernel Fuzzy C-Means (KFCM) model to segment the network based on communication patterns.
- Attack detection and classification using min-max normalization and a stacked sparse autoencoder (SSAE) model.
- Secure data transmission via a quantum-resistant hybrid encryption (QRHE) model to the cloud.
Main Results:
- The QRHEV2V-DRL method demonstrated superior performance in attack detection and classification.
- Achieved a high accuracy of 99.62% on the ToN-IoT dataset, outperforming existing models.
- Successfully implemented a multi-stage approach for robust V2V communication security.
Conclusions:
- The QRHEV2V-DRL method offers a significant advancement in securing V2V communication networks.
- The proposed approach provides effective protection against malicious attacks and ensures data confidentiality.
- This study highlights the potential of deep representation learning and quantum-resistant encryption for future vehicular security.
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