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<p>Vehicular Ad Hoc Networks (VANETs) are critical for transportation safety and efficiency but face serious security challenges due to limitations of traditional cryptography and emerging cyber threats. This paper proposes a novel reconciliation-free Physical Layer Key Generation (PKG) framework based on dynamic Received Signal Strength (RSS) measurements and Triplet Networks (TN). Unlike conventional PKG schemes, the proposed approach eliminates reconciliation overhead while maintaining high entropy and strong&#xa0;resistance to eavesdropping. Realistic RSS is generated by integrating SUMO-based vehicular mobility with NS-3 network simulations, capturing spatiotemporal channel dynamics. The TN architecture extracts reciprocal channel features through hierarchical convolutional layers and optimized embedding spaces for secure key extraction. Experimental results show near-zero bit error rates between legitimate vehicles, high key&#xa0;entropy (0.69 bits/bit), and complete failure of eavesdroppers to reconstruct keys. Among 3,200 generated keys, 74.1% achieved perfect agreement without reconciliation, while eavesdroppers achieved 0% success. The proposed framework demonstrates a scalable, and robust security solution for VANET communications.</p>

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Reconciliation-free physical layer key generation for VANETs using triplet networks

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月2日

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  • Ahmedalmansour Abuobieda,
  • Weibin Zhang,
  • Ibraheem Abdelazeem,
  • Abdeldime Mohamedsalih,
  • Mohamed Abdalwohab

摘要

Vehicular Ad Hoc Networks (VANETs) are critical for transportation safety and efficiency but face serious security challenges due to limitations of traditional cryptography and emerging cyber threats. This paper proposes a novel reconciliation-free Physical Layer Key Generation (PKG) framework based on dynamic Received Signal Strength (RSS) measurements and Triplet Networks (TN). Unlike conventional PKG schemes, the proposed approach eliminates reconciliation overhead while maintaining high entropy and strong resistance to eavesdropping. Realistic RSS is generated by integrating SUMO-based vehicular mobility with NS-3 network simulations, capturing spatiotemporal channel dynamics. The TN architecture extracts reciprocal channel features through hierarchical convolutional layers and optimized embedding spaces for secure key extraction. Experimental results show near-zero bit error rates between legitimate vehicles, high key entropy (0.69 bits/bit), and complete failure of eavesdroppers to reconstruct keys. Among 3,200 generated keys, 74.1% achieved perfect agreement without reconciliation, while eavesdroppers achieved 0% success. The proposed framework demonstrates a scalable, and robust security solution for VANET communications.