Detecting GPS Interference Using Automatic Dependent Surveillance-Broadcast Data

This paper investigates the detection of Global Positioning System (GPS) interference during the Dallas Fort Worth (DFW) event from 17 to 19 October 2022, utilizing various machine learning (ML) models. The study examines the effectiveness of several ML models, including neural networks (NN), tree-based models, regression-based models, Bayesian classifiers, distance-based models, and stochastic classifiers, in identifying GPS interference. A simulated training signature was created with 180,000 data points, of which 25,792 were modeled as positive samples indicating GPS interference. Preliminary results reveal that the Multi-Layer Perceptron (MLP) model outperformed others, achieving a 99.8% True Positive Rate (TPR). Additionally, permutation feature importance was utilized to understand how model feature prioritization impacts the detection outcomes. Given the increasing frequency of GPS interference, these findings underscore the critical importance of ML techniques in detecting GPS interference patterns in Automatic Dependent Surveillance-Broadcast (ADS-B) data.