US 12,445,850 B2
Systems and methods to detect GPS spoofing attacks
Xin Tian, Germantown, MD (US); Zhengyang Fan, Germantown, MD (US); Khanh Pham, Albuquerque, NM (US); Erik Blasch, Arlington, VA (US); Sixiao Wei, Germantown, MD (US); Dan Shen, Germantown, MD (US); and Genshe Chen, Germantown, MD (US)
Assigned to INTELLIGENT FUSION TECHNOLOGY, INC., Germantown, MD (US)
Filed by Intelligent Fusion Technology, Inc., Germantown, MD (US)
Filed on Dec. 27, 2023, as Appl. No. 18/397,610.
Prior Publication US 2025/0220432 A1, Jul. 3, 2025
Int. Cl. H04L 29/06 (2006.01); H04W 12/121 (2021.01)
CPC H04W 12/121 (2021.01) 11 Claims
OG exemplary drawing
 
1. A method for detecting a global positioning system (GPS) spoofing attack, comprising:
providing a trained deep learning (DL) model based on a neural network;
feeding a GPS signal into the trained DL model;
using asymmetric Shapley values (ASVs) to calculate a plurality of feature contributions;
using the ASVs to assign a non-uniform distribution over an ordering of a plurality of features;
obtaining a plurality of causal structures among the plurality of features;
applying the ASVs to causal Shapley additive explanation to obtain a Shapley attribution;
incorporating the Shapley attribution and the plurality of causal structures;
using Shapley additive explanation (SHAP) to obtain a reason behind signal classification;
detecting the GPS spoofing attack by running the trained DL model and using the plurality of causal structures, the non-uniform distribution, the plurality of feature contributions, the Shapley attribution, the reason behind the signal classification, and incorporation of the Shapley attribution and the plurality of causal structures;
wherein the neural network includes three hidden layers that are followed by a rectified linear unit (ReLU) activation function or a hyperbolic tangent (Tanh) activation function;
using a Bayesian structural causal model (SCM) to construct a graphical representation of a causal relationship among the plurality of features.