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

arXiv: https://arxiv.org/abs/2403.02645 · HTML: https://mlsanity.com/paper/2403.02645/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 78 references that are in the map.
- First posted on arXiv 5 Mar 2024.

## Builds on (most cited first, 14 of 14)

- [A systematic study of the class imbalance problem in convolutional neural networks](https://mlsanity.com/paper/1710.05381/) (2017)
- [Recent Advances in Convolutional Neural Networks](https://mlsanity.com/paper/1512.07108/) (2015)
- [Revisiting Locally Supervised Learning: an Alternative to End-to-end Training](https://mlsanity.com/paper/2101.10832/) (2021)
- [Jamming Attacks and Anti-Jamming Strategies in Wireless Networks: A Comprehensive Survey](https://mlsanity.com/paper/2101.00292/) (2021)
- [Understanding CNN Fragility When Learning With Imbalanced Data](https://mlsanity.com/paper/2210.09465/) (2022)
- [Multi-stage Jamming Attacks Detection using Deep Learning Combined with Kernelized Support Vector Machine in 5G Cloud Radio Access Networks](https://mlsanity.com/paper/2004.06077/) (2020)
- [5G NR Jamming, Spoofing, and Sniffing: Threat Assessment and Mitigation](https://mlsanity.com/paper/1803.03845/) (2018)
- [Smart Jamming Attacks in 5G New Radio: A Review](https://mlsanity.com/paper/2009.05531/) (2020)
- [Bayesian Inference-assisted Machine Learning for Near Real-Time Jamming Detection and Classification in 5G New Radio (NR)](https://mlsanity.com/paper/2304.13660/) (2023)
- [Deep Attention Recognition for Attack Identification in 5G UAV scenarios: Novel Architecture and End-to-End Evaluation](https://mlsanity.com/paper/2303.12947/) (2023)
- [Federated Learning with Dual Attention for Robust Modulation Classification under Attacks](https://mlsanity.com/paper/2401.11039/) (2024)
- [Jamming Detection With Subcarrier Blanking for 5G and Beyond in Industry 4.0 Scenarios](https://mlsanity.com/paper/2106.07970/) (2021)
- [Anonymous Jamming Detection in 5G with Bayesian Network Model Based Inference Analysis](https://mlsanity.com/paper/2311.17097/) (2023)
- [Bypassing a Reactive Jammer via NOMA-Based Transmissions in Critical Missions](https://mlsanity.com/paper/2401.10387/) (2024)

## Built on by (most cited first, 2 of 2)

- [CWGAN-GP Augmented CAE for Jamming Detection in 5G-NR in Non-IID Datasets](https://mlsanity.com/paper/2506.15075/) (2025)
- [Explainable and Hardware-Efficient Jamming Detection for 5G Networks Using the Convolutional Tsetlin Machine](https://mlsanity.com/paper/2603.07336/) (2026)

## Abstract (arXiv)

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.