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Ensemble Machine Learning Models Utilizing a Hybrid Recursive Feature Elimination (RFE) Technique for Detecting GPS

Raghad Al-Syouf1, Omar Y Aljarrah1, Raed Bani-Hani1

  • 1Department of Network Engineering and Security, Jordan University of Science and Technology, Irbid 22110, Jordan.

Sensors (Basel, Switzerland)
|April 26, 2025
PubMed
Summary

Unmanned Aerial Vehicles (UAVs) face GPS spoofing threats. A new hybrid Recursive Feature Elimination (RFE) method with ensemble learning improves intrusion detection efficiency and accuracy for drones.

Keywords:
GPS spoofingUAVscyber-attacksensemble modelsintrusion detection system (IDS)machine learning (ML)

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Area of Science:

  • Cybersecurity
  • Aerospace Engineering
  • Machine Learning

Background:

  • Unmanned Aerial Vehicles (UAVs) rely on external data, making them vulnerable to GPS jamming and spoofing attacks.
  • Intrusion Detection Systems (IDS) are crucial for UAV security, with machine learning (ML) offering high detection rates for cyber threats.
  • Existing ML-based IDSs in UAVs face challenges with computational efficiency and processing time.

Purpose of the Study:

  • To propose a hybrid Recursive Feature Elimination (RFE) technique combined with Spearman Correlation Analysis (SCA) for efficient GPS spoofing detection in UAVs.
  • To evaluate the performance of ensemble learning classifiers (bagging, boosting, stacking, voting) using the proposed feature selection method.
  • To enhance both the accuracy and computational efficiency of intrusion detection systems for UAVs.

Main Methods:

  • A hybrid feature selection approach combining Recursive Feature Elimination (RFE) and Spearman Correlation Analysis (SCA) was developed.
  • Ensemble learning models, including bagging, boosting, stacking, and voting classifiers, were implemented for GPS spoofing detection.
  • The proposed methods were evaluated on two benchmark datasets: a GPS spoofing dataset and a UAV location GPS spoofing dataset.

Main Results:

  • The proposed ensemble models demonstrated a strong balance between detection accuracy and processing efficiency.
  • The bagging classifier achieved the highest accuracy (99.50%) on the GPS spoofing dataset with a low processing time (0.029 s).
  • On the UAV location GPS spoofing dataset, the bagging classifier achieved 99.16% accuracy and 0.002 s processing time, outperforming other ML models.

Conclusions:

  • The hybrid RFE-based feature selection methodology significantly improves the efficiency and efficacy of GPS spoofing detection in UAVs.
  • Ensemble learning classifiers, particularly the bagging classifier, show excellent performance in terms of accuracy and speed for UAV intrusion detection.
  • The proposed approach outperforms conventional feature selection techniques in detecting GPS spoofing attacks on UAVs.