<ul><li>A new deep learning-based technique for detecting jammers in 5G Connected and Automated Vehicle (CAV) networks has been developed.</li><li>The technique focuses on the Synchronization Signal Block (SSB) and leverages RF domain features to improve network robustness.</li><li>By extracting PSS correlation and energy per null resource elements (EPNRE) characteristics, the method distinguishes between normal and jammed signals with high precision.</li><li>The proposed technique achieves a 96.4% detection rate at extra low jamming power, specifically with SJNR between 15 to 30 dB.</li></ul>

DT-DDNN: A Physical Layer Security Attack Detector in 5G RF Domain for CAVs

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