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Vehicle-to-Everything (V2X) communication is foundational for next-generation intelligent transportation systems. However, the reliance on wireless communication exposes vehicles to significant cybersecurity threats. This paper proposes a novel framework for detecting physical layer spoofing attacks in Cellular V2X (C-V2X) networks by leveraging the flexibility of Software-Defined Radio (SDR) and the pattern recognition capabilities of Deep Learning (DL). The proposed approach is based on a graph-based representation of the signal and a Graph Neural Network (GNN) that classifies these graphs. Evaluation through simulation demonstrates that the proposed SDR-DL approach achieves high detection accuracy and robustness.

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Leveraging Software-Defined Radio and Deep Learning for Enhanced Physical Layer Spoofing Detection in C-V2X Communications

  • 会议论文

  • 发表日期：

    2026年

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  • Danilo Greco,
  • Giovanni Gaggero

摘要

Vehicle-to-Everything (V2X) communication is foundational for next-generation intelligent transportation systems. However, the reliance on wireless communication exposes vehicles to significant cybersecurity threats. This paper proposes a novel framework for detecting physical layer spoofing attacks in Cellular V2X (C-V2X) networks by leveraging the flexibility of Software-Defined Radio (SDR) and the pattern recognition capabilities of Deep Learning (DL). The proposed approach is based on a graph-based representation of the signal and a Graph Neural Network (GNN) that classifies these graphs. Evaluation through simulation demonstrates that the proposed SDR-DL approach achieves high detection accuracy and robustness.