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

Abstract

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. Leveraging radio frequency (RF) domain knowledge, this work presents a novel deep learning-based technique focusing on SSB for detecting jammers in CAV networks without changing pre-existing infrastructure. By integrating a preprocessing block that extracts 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 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 which outperforms the single threshold DNN design with 86.0% detection rate and unprocessed IQ sample DNN design with 83.2% detection rate. Ultimately, the performance of DT-DDNN is validated through the analysis of real 5G signals obtained from a practical testbed, demonstrating a strong alignment with the simulation results.