An Approach of Linear Regression‐Based UAV GPS Spoofing Detection

Abstract

A prominent security threat to unmanned aerial vehicle (UAV) is to capture it by GPS spoofing, in which the attacker manipulates the GPS signal of the UAV to capture it. This paper introduces an anti-spoofing model to mitigate the impact of GPS spoofing attack on UAV mission security. In this model, linear regression (LR) is used to predict and model the optimal route of UAV to its destination. On this basis, a countermeasure mechanism is proposed to reduce the impact of GPS spoofing attack. Confrontation is based on the progressive detection mechanism of the model. In order to better ensure the flight security of UAV, the model provides more than one detection scheme for spoofing signal to improve the sensitivity of UAV to deception signal detection. For better proving the proposed LR anti-spoofing model, a dynamic Stackelberg game is formulated to simulate the interaction between GPS spoofer and UAV. In particular, for GPS spoofer, it is worth mentioning that for the scenario that the UAV is cheated by GPS spoofing signal in the mission environment of the designated route is simulated in the experiment. In particular, UAV with the LR anti-spoofing model, as the leader in this game, dynamically adjusts its response strategy according to the deception’s attack strategy when upon detection of GPS spoofer’s attack. The simulation results show that the method can effectively enhance the ability of UAV to resist GPS spoofing without increasing the hardware cost of the UAV and is easy to implement. Furthermore, we also try to use long short-term memory (LSTM) network in the trajectory prediction module of the model. The experimental results show that the LR anti-spoofing model proposed is far better than that of LSTM in terms of prediction accuracy.

Dynamic Selection Techniques for Detecting GPS Spoofing Attacks on UAVs

“…• Uses a dataset with only 5 features and very limited samples. Linear regression-based and long short term memory [14] • Works effectively in a case of UAV flying along the specified rout…”

“…In [14], the authors proposed an anti-spoofing model that used linear regression to predict and model the optimal UAV route to its destination and used Long Short-Term Memory in the trajectory prediction. The model provides more than one detection scheme for GPS spoofing signals to improve UAV flight security and sensitivity to deception signal detection.…”

A Survey on UAV Network for Secure Communication and Attack Detection: A focus on Q-learning, Blockchain, IRS and mmWave Technologies

“…Network infiltration attack [31] Gaussian Naive Bayes Stochastic Gradient Descent K-means GPS Spoofing attack [32] Linear regression anti-spoofing model Quadrotor jMAVSim…”

Enhanced Machine Learning Ensemble Approach for Securing Small Unmanned Aerial Vehicles From GPS Spoofing Attacks

“…In [26], a counter-spoofing model was introduced, employing linear regression for optimal UAV route prediction and long short-term memory (LSTM) for trajectory prediction. The model incorporates multiple identification schemes for GPS spoofing signals, enhancing UAV flight sensitivity and safety to deceptive signal detection.…”