An Approach of Linear Regression-Based UAV GPS Spoofing Detection

Lianxiao Meng,Wu Yang,Gaigai Tang,Long Zhang,Lin Yang,Feng Yang,Shuangyin Ren,Xiaojie Wang

doi:10.1155/2021/5517500

Lianxiao Meng, Wu Yang + Show 6 more

Open Access

https://doi.org/10.1155/2021/5517500

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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.

Highlights

With the progress of science and technology and the continuous reduction of manufacturing costs, unmanned aerial vehicle (UAV) has entered the industrial production and people’s daily life from the military field
The flight trajectory prediction model of UAV is built by fitting the flight log of UAV with linear regression (LR) model, and the prediction accuracy is relatively high among all the methods
In order to determine the performance of the long short-term memory (LSTM)-KF defense model, the root mean square error is used to evaluate the fitting performance of the model

Summary

Introduction

With the progress of science and technology and the continuous reduction of manufacturing costs, UAV has entered the industrial production and people’s daily life from the military field. While UAV brings all kinds of convenience to our production and life, the security problems it faces are being gradually exposing. The common attacks on UAV mainly include the attacks on UAV sensors, UAV network, radio interference and hijacking, and GPS spoofing [4]. In these attacks, GPS spoofing is regarded as one of the most urgent threats, because it is practical and can be executed against UAV [5,6,7]. GPS spoofing refers to the following: in order to mislead the GPS navigation and positioning signal in the designated area, GPS attacker transmits pseudonavigation signal which cannot be effectively detected under the concealment condition because of its certain similarity with the real GPS signal, and user can get the false positioning, speed, and time

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Results

Conclusion