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Global Positioning System (GPS) technology has revolutionized navigation and positioning, but its accuracy is often compromised by various errors. These errors, stemming from environmental, satellite, and receiver-related factors, require careful mitigation to ensure reliable performance across applications.Atmospheric ErrorsGPS signals travel through the Earth’s ionosphere and troposphere, introducing delays which affect accuracy. The ionosphere is strongly influenced by charged particles,...
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Spoofing attack recognition for GNSS-based train positioning using a BO-LightGBM method.

Jiaqi Bi1, Jiang Liu1,2, Baigen Cai1

  • 1School of Automation and Intelligence, Beijing Jiaotong University, Beijing, China.

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Summary
This summary is machine-generated.

This study introduces a new method for detecting Global Navigation Satellite System (GNSS) spoofing attacks in train positioning systems (TPS). The developed Bayesian optimization-LightGBM model effectively identifies spoofing, enhancing train safety and operational integrity.

Keywords:
Railwaymachine learningrecognition modelspoofing attacktrain positioning system

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Area of Science:

  • * Focuses on cybersecurity and robust positioning systems for intelligent transportation.
  • * Integrates machine learning for real-time threat detection in critical infrastructure.

Background:

  • * Trustworthy train positioning is essential for operational control, with Global Navigation Satellite System (GNSS) being a key technology.
  • * GNSS spoofing attacks pose a significant threat to the reliability of train positioning systems (TPS).
  • * Existing research often overlooks the impact and recognition of spoofing attacks within GNSS-enabled TPS.

Purpose of the Study:

  • * To develop an effective spoofing recognition solution for active security protection in train positioning systems.
  • * To address the gap in current research by considering the influence and recognition of GNSS spoofing attacks.
  • * To enhance the trustworthiness of GNSS-based train positioning.

Main Methods:

  • * Designed novel features incorporating GNSS observation indicators and odometer-enabled parameters to reflect spoofing effects.
  • * Proposed a Bayesian optimization-light gradient boosting machine (BO-LightGBM) for spoofing recognition.
  • * Utilized a dedicated GNSS spoofing test platform and SimSAFE tool for data collection across various attack modes.

Main Results:

  • * The BO-LightGBM model demonstrated superior performance in recognizing GNSS spoofing attacks.
  • * The proposed solution showed adaptability to different spoofing attack scenarios.
  • * Comparative analysis confirmed the model's advantages over existing state-of-the-art strategies.

Conclusions:

  • * The developed BO-LightGBM approach offers an effective means for detecting GNSS spoofing in TPS.
  • * The method provides a robust solution for active security protection in train navigation.
  • * This research contributes to enhancing the reliability and security of GNSS-dependent transportation systems.