  The Synchronization Signal Block (SSB) is a fundamental component of the 5G
New Radio (NR) air interface, crucial for the initial access procedure of
Connected and Automated Vehicles (CAVs), and serves several key purposes in the
network's operation. However, due to the predictable nature of SSB
transmission, including the Primary and Secondary Synchronization Signals (PSS
and SSS), jamming attacks are critical threats. These attacks, which can be
executed without requiring high power or complex equipment, pose substantial
risks to the 5G network, particularly as a result of the unencrypted
transmission of control signals. Leveraging RF domain knowledge, this work
presents a novel deep learning-based technique for detecting jammers in CAV
networks. Unlike the existing jamming detection algorithms that mostly rely on
network parameters, we introduce a double-threshold deep learning jamming
detector by focusing on the SSB. The detection method is focused on RF domain
features and improves the robustness of the network without requiring
integration with the pre-existing network infrastructure. By integrating a
preprocessing block to extract PSS correlation and energy per null resource
elements (EPNRE) characteristics, our method distinguishes between normal and
jammed received signals with high precision. Additionally, by incorporating of
Discrete Wavelet Transform (DWT), the efficacy of training and detection are
optimized. A double-threshold double Deep Neural Network (DT-DDNN) is also
introduced to the architecture complemented by a deep cascade learning model to
increase the sensitivity of the model to variations of signal-to-jamming noise
ratio (SJNR). Results show that the proposed method achieves 96.4% detection
rate in extra low jamming power, i.e., SJNR between 15 to 30 dB. Further,
performance of DT-DDNN is validated by analyzing real 5G signals obtained from
a practical testbed.
