Few-Shot RF Fingerprinting for ADS-B Spoofing Detection

The patent replaces complex radar-based physical authentication systems with a machine learning-based signal fingerprinting system. Instead of using expensive radar hardware to verify aircraft authenticity, the invention uses computational algorithms that analyze subtle characteristics of ADS-B signals to identify spoofing attempts, thereby maintaining authentication reliability while dramatically reducing system complexity and cost.

The patent introduces signal fingerprinting as an intermediary layer between the ADS-B message content and the authentication decision. Rather than directly verifying aircraft identity through complex radar interaction, the system extracts unique fingerprint features from the signal characteristics themselves, creating a simplified verification mechanism that maintains reliability without requiring complex radar systems.

The patent applies few-shot learning techniques that require only a small subset of training data (a few examples per class) rather than large datasets. This partial action approach maintains classification accuracy by focusing on the most critical fingerprint features while significantly reducing the quantity of training data needed compared to traditional machine learning methods.

The patent transforms the learning problem by changing the parameter space from requiring large amounts of data to operating effectively with minimal data. By using prototypical networks and few-shot learning architectures, the system achieves accurate classification with only a few training samples per aircraft type, fundamentally altering the data requirement parameter from thousands of samples to just a few.