A Highly Interpretable Framework for Generic Low-Cost UAV Attack Detection

Shihao Wu

doi:10.1109/JSEN.2023.3244831

A Highly Interpretable Framework for Generic Low-Cost UAV Attack Detection

2023-04-01

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AI

DOI：10.1109/JSEN.2023.3244831

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The increasing prevalence of cyber-attacks on unmanned aerial vehicles (UAVs) has led to research on effective detection methods. However, current approaches often lack transferability and interoperability, which limits their effectiveness. This study proposes a CNN-BiLSTM-Attention (CBA) model for efficient attack detection using real-time UAV sensor data. Additionally, the SHapley Additive exPlanations (SHAP) method is used to improve the interpretability of the model. The proposed approach is tested on real attack scenarios, including denial-of-service (DoS) attacks and global positioning system (GPS) spoofing attacks, and demonstrates both effectiveness and interpretability.

Keywords:

Feature extraction

Global Positioning System

Autonomous aerial vehicles

Sensors

Security

Predictive models

Sensor phenomena and characterization

Attack detection

deep learning

unmanned aerial vehicles (UAVs)

A Highly Interpretable Framework for Generic Low-Cost UAV Attack Detection

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