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

TL;DR

This paper presents a novel U-shaped transformer architecture with PCA feature refinement and entropy regularization for robust jamming detection in 5G UAV networks, significantly improving detection accuracy and training efficiency.

Contribution

It introduces a new transformer-based model with PCA and entropy-based regularization tailored for wireless security, enhancing detection of AI-powered jamming attacks in 5G UAV networks.

Findings

Achieves 85.06% detection rate in NLoS scenarios.

Up to tenfold training speed improvement.

Effective handling of temporal wireless signal patterns.

Abstract

Unmanned Aerial Vehicles (UAVs) face significant security risks from jamming attacks, which can compromise network functionality. Traditional detection methods often fall short when confronting AI-powered jamming that dynamically modifies its behavior, while contemporary machine learning approaches frequently demand substantial feature engineering and struggle with temporal patterns in attack signatures. The vulnerability extends to 5G networks employing Time Division Duplex (TDD) or Frequency Division Duplex (FDD), where service quality may deteriorate due to deliberate interference. We introduce a novel U-shaped transformer architecture that leverages Principal Component Analysis (PCA) to refine feature representations for improved wireless security. The training process is regularized by incorporating the output entropy uncertainty into the loss function, a mechanism inspired by the Soft Actor-Critic (SAC) algorithm in Reinforcement Learning (RL) to enable robust jamming detection techniques. The architecture features a modified transformer encoder specially designed to process critical wireless signal features, including Received Signal Strength Indicator (RSSI) and Signal-to- Interference-plus-Noise Ratio (SINR) measurements. We complement this with a custom positional encoding mechanism that specifically accounts for the inherent periodicity of wireless signals,enabling a more accurate representation of temporal signal patterns. In addition, we propose a batch size scheduler and implement chunking techniques to optimize convergence for time series data. These advancements contribute to up to a ten times improvement in training speed within the advanced U-shaped encoder-decoder transformer model introduced in this study. Experimental evaluations demonstrate the effectiveness of our entropy-based approach, achieving detection rates of 85.06% in NLoS scenarios.