Gps signals blocked blindly using learning and local data
GhostLock: Learning-Assisted Blind GPS Signal Nullification Without Victim-Side Information
Cryptography and Security
Summary
GPS signals are important for navigation but can be attacked to stop them from working. The researchers show a new way to block GPS signals without needing information from the victim's device. They use a smart system that learns and works with only an approximate location and local GPS signal data. Their system, GhostLock, successfully makes GPS signals disappear on a software radio setup, proving that such blind attacks are possible and need new protections.
What this means in practice
- •For security teams: Identify and mitigate new GPS signal nullification attacks without victim-side information.
- •For radio system developers: Implement learning-based blind signal cancellation techniques demonstrated by GhostLock for testing GPS resilience.
Authors
Khalil Ahmad, Omar Ansari, Mohsin Kamal, Asif Iqbal, Muhammad Naveed Aman
Abstract
The Global Positioning System (GPS) provides critical positioning, navigation, and timing (PNT) services for numerous safety- and mission-critical applications, making it an attractive target for physical-layer attacks. While GPS spoofing and jamming have been extensively studied, GPS Signal Nullification (GSN)-a physical-layer attack that suppresses authentic satellite signals through destructive interference prior to acquisition-remains largely unexplored. Existing wireless signal nullification techniques target conventional communication systems and typically require victim-side information, such as channel estimates or synchronization parameters, making them impractical for GPS. Furthermore, many GPS spoofing defenses assume authentic satellite signals remain available as trusted references, an assumption invalidated by GSN. To address this gap, we present GhostLock, a learning-assisted blind GSN framework requiring only an approximate target location estimate. GhostLock integrates a Blind Adaptive Filter (BAF) with a deep learning-based synchronization module to generate and align an anti-signal using only the attacker's locally received GPS signal, eliminating the need for victim-side observations. We implement GhostLock on a real-time software-defined radio (SDR) platform using GPS-SDR-SIM and GNU Radio. Experimental results demonstrate successful suppression of GPS signal acquisition, establishing the feasibility of blind GSN and motivating new detection and mitigation techniques for this emerging GPS threat model.