Research on Detection of Spoofing Signal with Small Delay Based on KNN

Li, Junzhi; Li, Wanqing; He, Shaofeng; Dai, Zhiqiang; Fu, Qun

doi:10.1109/icet49382.2020.9119515

Cited by 4 publications

(2 citation statements)

References 2 publications

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“…The main idea for applying this method is that the input must be highly specific to the object that needs to be classified. Prior studies have demonstrated the application of machine learning techniques in detecting spoofing attacks, showing promising outcomes [17][18][19][20]. Nonetheless, these methods require data from the acquisition stage, which is feasible with softwaredefined receivers but not with standard commercial ones.…”

Section: Introductionmentioning

confidence: 99%

A method to build feature descriptor for GNSS spoofing detection by carrier phase double difference measurement

Dinh,

Huu,

Van

et al. 2024

Meas. Sci. Technol.

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GNSS spoofing is a type of attack that aims to deceive GNSS receivers by transmitting fake signals that imitate the authentic ones. To detect such attacks, a possible solution is to calculate the double difference (DD) of carrier phase measurements between two antennas of two separated receivers. This DD measurement represents the angle of arrival (AoA) information of the signal. In the case of authentic signals, each signal comes from a different satellite, so the AoA values are different for each satellite. However, in the case of a spoofing attack, all the counterfeit signals from the same broadcaster come from the same direction, and thus the AoA values are the same for all DD measurements. Sum of Squares (SoS) detector and Dispersion of Double Difference (D3) detector are known as simple but effective methods for spoofing detection. However, these detectors are not robust in some special regions where cycle slips are large and vary rapidly. This paper proposes a new method for detecting GNSS spoofing by using carrier phase double difference measurements to generate a feature vector as a descriptor for each satellite in a set of observed satellites. The feature vectors are formed by statistical parameters of a series of consecutive double difference values computed for each reference satellite. Our proposed feature descriptor has been evaluated on a dataset containing both real and fake signals using an SVM classifier. The study shows that the proposed feature descriptor is highly effective in detecting spoofing satellite signals.

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Section: Introductionmentioning

confidence: 99%

A method to build feature descriptor for GNSS spoofing detection by carrier phase double difference measurement

Dinh,

Huu,

Van

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…Method ( 5) is a technology to analyze navigation data through machine learning and identify deceptive interference. At present, common detection models include Convolutional Neural Network [21], K-nearest neighbor [22], Support Vector Machine [23], Multi-layer Perceptron Neural Network [24], Probabilistic Neural Network [25], etc. This method has a higher detection rate than traditional spoofing detection technology based on signal characteristics.…”

Section: Introductionmentioning

confidence: 99%

Detection and Orientation of GNSS Spoofing Based on Positioning Solutions of Three Receivers

Chen

Cheng

et al. 2023

IEEE Access

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Spoofing poses a serious security threat to the satellite navigation system. Most of the current spoofing detection methods are based on the information in the receiver-solving process. Applying the above spoofing detection algorithm on the receiver requires a redesign of the original receiver, which limits the application of the deceptive detection method on the ex-factory receiver. In the case of single-station spoofing, the positions of the successfully spoofed multiple receivers are the same according to the deception signal, but the clock offset is different. The single-difference clock offset is positively correlated with the distance between the two receivers. Based on the above principles, this paper proposes a spoofing detection and spoofing direction method based on the positioning solutions of three receivers. The observation value of the baseline length is calculated from the position information in the positioning solution, and spoofing detection is realized by hypothesis testing. The clocks of the three receivers are synchronized. The Adaptive Delta Gradient Descent(ADGD) method is adopted to calculate the spoofing direction according to the single-difference clock offset in the positioning solution. The direction angle is optimized through the Interacting Multiple Model based on the volume Kalman Filter. The relationship between several parameters and the direction accuracy of spoofing is analyzed through simulation tests. Experimental results show that this method can effectively detect and orient spoofing. In addition, the detection and orientation method of spoofing has the advantage of fast response speed, which can realize fast spoofing detection and direction finding in a deception environment.

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Small Delay GNSS Forwarding Spoofing Detection in a Multipath Environment Based on Convolutional Neural Network

Jin,

Cui,

Yan

et al. 2024

IEEE Sensors J.

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Research on Detection of Spoofing Signal with Small Delay Based on KNN

Cited by 4 publications

References 2 publications

A method to build feature descriptor for GNSS spoofing detection by carrier phase double difference measurement

A method to build feature descriptor for GNSS spoofing detection by carrier phase double difference measurement

Detection and Orientation of GNSS Spoofing Based on Positioning Solutions of Three Receivers

Small Delay GNSS Forwarding Spoofing Detection in a Multipath Environment Based on Convolutional Neural Network

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