Performance Analysis of Direct GPS Spoofing Detection Method with AHRS/Accelerometer

Abstract

The global positioning system (GPS) is an essential technology that provides positioning capabilities and is used in various applications such as navigation, surveying, mapping, robot simultaneous localization and mapping (SLAM), location-based service (LBS), etc. However, the GPS is known to be vulnerable to intentional attacks such as spoofing because of its simple signal structure. In this study, a direct method is proposed for GPS spoofing detection, using Attitude and Heading Reference System (AHRS) accelerometer and analyzing the detection performance with corresponding probability density functions (PDFs). The difference in the acceleration between the GPS receiver and the accelerometer is used to detect spoofing. The magnitude of the acceleration error may be used as a decision variable. Additionally, using the magnitude of the north (or east) component of the acceleration error as another decision variable is proposed, which shows better performance in some conditions. The performance of the two decision variables is compared by calculating the probability of spoofing detection and the detectable minimum spoofing acceleration (DMSA), given a pre-defined false alarm probability and a pre-defined detection probability. It turns out that both decision variables need to be used together to obtain the best spoofing detection performance.Sensors 2020, 20, 954 2 of 22 (MLE) [11]. Prior studies [7,12] summarize various spoofing detection techniques, among which low complexity spoofing detection techniques include C/N 0 monitoring, absolute power monitoring, power variation versus receiver movement, code and phase rate consistency check, and GPS clock consistency check, while high complexity spoofing detection techniques include Direction Of Arrival (DOA) comparison [14] and consistency check with other solutions [15][16][17][18].Among the effective high complexity spoofing detection techniques mentioned in the prior study [12], the DOA comparison approach uses the DOA measurements to assess the direction of the spoofing source and mitigates the interference by placing the spatial null in the antenna reception pattern [14]. The consistency check approach uses the inertial measurement unit (IMU) [15][16][17][18]. The relative platform trajectory estimated by the GNSS receiver is compared to the relative trajectory developed from the IMU measurement, specifically gyro output, to detect spoofing [15]. In References [16,17], a tightly coupled GPS / inertial navigation system (INS) is used to detect spoofing by incorporating fault detection concepts and Kalman filter, respectively, based on receiver autonomous integrity monitoring (RAIM). In [18], an accelerometer is used to detect spoofing by comparison of acceleration between the GPS receiver and the IMU. However, no prior research has been done on using the IMU and the acceleration error which is expressed with exact probability density function.In this study, a direct GPS spoofing detection method is proposed which uses attitude and heading reference system (AHRS)...

A Self-Diagnosis Method for Detecting UAV Cyber Attacks Based on Analysis of Parameter Changes

“…Unlike previous work [ 23 , 24 , 25 , 26 ], we do not need to know which behavior is considered normal, or to compose many training datasets. It is enough for the UAV system to obtain one value and it does not need to be compared with a normal value, as in other approaches [ 8 , 9 , 10 , 11 , 12 ].…”

“…Kwon and Shim proposed a method for direct detection of GPS spoofing attacks using a positioning system based on an accelerometer [ 26 ]. They conducted a performance analysis using probability density functions.…”

Dynamic Selection Techniques for Detecting GPS Spoofing Attacks on UAVs

“…Several studies have been performed on GPS spoofing detection and mitigation methods. For instance, the authors of [7] proposed a GPS spoofing detection method that depends on the acceleration error calculated by estimating the acceleration from the GPS receiver and the acceleration measured from the IMU. In [9], the authors used IMU measurements (angle, velocity, and acceleration) and GPS data (longitude and latitude) in a two-step method that applies the XGBoost model and a Genetic Algorithm, to detect GPS-spoofing attacks.…”

“…The accelerator is also sensitive to temperature, which makes it difficult for it to perform properly in different environmental situations of UAVs. Moreover, Gyroscope is a sensor that does not measure linear motion in any direction or any static angle of orientation [7]. Therefore, as these two sensors are the two main components of INS and IMU-based techniques, and based on their drawbacks, they cannot be used in detecting GPS spoofing attacks on UAVs [8].…”

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“…Several methods currently exist for the measurement of antiGPS spoofing that are mainly focusing on GPS navigation signals analysis [5]- [8], GPS navigation message authentication [9]- [12], Inertial Navigation System (INS) based spoofing detection [13]- [15], and Mobile Positioning System (MPS) based spoofing detection [16]- [18]. The GPS navigation signals analysis detects the spoofed signal by estimating and comparing the Direction of Arrival (DoA) of the GPS signal.…”