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

2024-03-05

arXiv.org

AI Summary

In 5G NR, the Synchronization Signal Block (SSB) is vulnerable to low-power RF jamming attacks due to its predictable structure and unencrypted control channels. To address this, we propose an end-to-end, real-time RF-domain detection method that requires no modifications to existing network infrastructure. Our approach introduces a novel Dual-Threshold Dual Deep Neural Network (DT-DDNN) integrated with a deep cascaded learning architecture. It jointly exploits three complementary features: PSS correlation, Energy Per Non-Resource Element (EPNRE), and Discrete Wavelet Transform (DWT) coefficients—enabling robust detection under low SINR conditions (15–30 dB) while maintaining high sensitivity. Evaluated on a real-world 5G testbed, the method achieves a detection rate of 96.4%, demonstrating strong practical deployability. This work advances RF-layer security for 5G NR by enabling lightweight, architecture-agnostic, and real-time jamming detection without compromising system compatibility or operational overhead.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Intelligent Robots: State Estimation
• Application Domains: Internet of Things, Sensor Networks & Smart Cities

Application Category

• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

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. 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.

Problem

• Detects jamming attacks in 5G CAV networks using RF features
• Improves SSB-based jammer detection with deep learning and double-threshold
• Enhances robustness without modifying existing 5G infrastructure

Innovation

• Deep learning detects 5G jammers using RF features
• Double-threshold DNN enhances jammer detection sensitivity
• Wavelet transform optimizes signal analysis and training