PCA-Featured Transformer for Jamming Detection in 5G UAV Networks

• URL: http://arxiv.org/abs/2412.15312v1
• Date: Thu, 19 Dec 2024 16:13:04 GMT
• Title: PCA-Featured Transformer for Jamming Detection in 5G UAV Networks
• Authors: Joseanne Viana, Hamed Farkhari, Pedro Sebastiao, Victor P Gil Jimenez, Lester Ho,
• Abstract summary: Jamming attacks pose a threat to Unmanned Aerial Vehicle (UAV) wireless communication systems. Current detection approaches struggle with sophisticated artificial intelligence (AI) jamming techniques. We present a novel transformer-based deep learning framework for jamming detection.
• Score: 0.559239450391449
• License:
• Abstract: Jamming attacks pose a threat to Unmanned Aerial Vehicle (UAV) wireless communication systems, potentially disrupting essential services and compromising network reliability. Current detection approaches struggle with sophisticated artificial intelligence (AI) jamming techniques that adapt their patterns while existing machine learning solutions often require extensive feature engineering and fail to capture complex temporal dependencies in attack signatures. Furthermore, 5G networks using either Time Division Duplex (TDD) or Frequency Division Duplex (FDD) methods can face service degradation from intentional interference sources. To address these challenges, we present a novel transformer-based deep learning framework for jamming detection with Principal Component Analysis (PCA) added features. Our architecture leverages the transformer's self-attention mechanism to capture complex temporal dependencies and spatial correlations in wireless signal characteristics, enabling more robust jamming detection techniques. The U-shaped model incorporates a modified transformer encoder that processes signal features including received signal strength indicator (RSSI) and signal-to-noise ratio (SINR) measurements, alongside a specialized positional encoding scheme that accounts for the periodic nature of wireless signals. In addition, we propose a batch size scheduler and implement chunking techniques to optimize training convergence for time series data. These advancements contribute to achieving up to a ten times improvement in training speed within the advanced U-shaped encoder-decoder model introduced. Simulation results demonstrate that our approach achieves a detection accuracy of 90.33 \% in Line-of-Sight (LoS) and 84.35 % in non-Line-of-Sight (NLoS) and outperforms machine learning methods and existing deep learning solutions such as the XGBoost (XGB) classifier in approximately 4%.

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