Satellite Fingerprinting Methods for GNSS Spoofing Detection.

Area of Science:

• Navigation Systems and Signal Processing
• Cybersecurity and Signal Authentication

Background:

• Global Navigation Satellite Systems (GNSS) are critical for many operations, but vulnerable to spoofing attacks.
• Existing spoofing detection methods can be circumvented by advanced attacks like secure code estimation and replay (SCER).

Purpose of the Study:

• To analyze and benchmark satellite fingerprinting methods for GNSS spoofing detection.
• To introduce and evaluate a novel fingerprinting metric based on satellite instrumental delay.

Main Methods:

• Performance evaluation of existing fingerprinting methods (Gaussian properties, energy, in-phase symbol dispersion) and a new method based on satellite instrumental delay.
• Utilized real GPS and Galileo signals generated and recorded in a DLR advanced GNSS simulation facility.
• Developed a new jamming and spoofing complementary detection technique using fingerprinting and machine learning.

Main Results:

• Proposed fingerprinting methods demonstrate improved detection accuracy compared to existing techniques.
• The new metric based on satellite instrumental delay shows significant potential for spoofing detection.
• Fingerprinting methods can be combined with other techniques to enhance overall detection system performance.

Conclusions:

• Satellite fingerprinting, particularly using instrumental delay, offers a promising approach for robust GNSS spoofing detection.
• The developed complementary detection technique enhances resilience against sophisticated spoofing and jamming attacks.
• Simple, effective metrics can significantly improve the security of GNSS-dependent systems.