GNSS Spoofing Detection Techniques by Cellular Network Cross-check in Smartphones

Formaggio, F; Ceccato, S; Basana, Francesco; Laurenti, Nicola; Tomasin, Stefano

doi:10.33012/2019.17076

Cited by 10 publications

(4 citation statements)

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“…As to the advent of the crowdsourcing technique, GNSS spoofing detection by a mobile phone was proposed in [33][34][35], where the reported GNSS positions or times are cross-checked with the reported cellular network positions or times. A spoofing alarm will be raised if these two reports of positions or times are dramatically different.…”

Section: Related Workmentioning

confidence: 99%

“…The large number and the wide spread of the receivers lead to the crowdsourcing method as an attractive approach for GNSS jammer monitoring and localization in a relatively large area [30][31][32], because it avoids the requirement of deploying dedicated devices to monitor the interference, thus greatly reducing the deployment costs of the system. The representative ones are the mobile phone raw measurements integrity method [33], the cross-checking between GNSS and cellular network measurements [34], and the generalized likelihood-ratio test algorithm based on multiple receivers' double differential pseudoranges (DP-GLRT) [39]. The crowdsourcing method can help authority organizations to find interference more efficiently and protect the GNSS usage for the public from spoofing harms, This paper presents a new spoofing detection method by exploiting the spatial diversity of the double differential pseudoranges of crowdsourcing receivers, which is named as double differential pseudorange spatial distribution (D 2 PS).…”

Section: Introductionmentioning

confidence: 99%

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School of Mechanical Engineering, Shanghai Jiao Tong University, 200240, Shanghai, China

Zhuang

Huang

et al. 2019

Mathematical Biosciences and Engineering

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The crystallization kinetics and melting behavior of nylon 10,10 in neat nylon 10,10 and in nylon 10,10 -montmorillonite (MMT) nanocomposites were systematically investigated by differential scanning calorimetry. The crystallization kinetics results show that the addition of MMT facilitated the crystallization of nylon 10,10 as a heterophase nucleating agent; however, when the content of MMT was high, the physical hindrance of MMT layers to the motion of nylon 10,10 chains retarded the crystallization of nylon 10,10, which was also confirmed by polarized optical microscopy. However, both nylon 10,10 and nylon 10,10 -MMT nanocomposites exhibited multiple melting be-havior under isothermal and nonisothermal crystallization conditions. The temperature of the lower melting peak (peak I) was independent of MMT content and almost remained constant; however, the temperature of the highest melting peak (peak II) decreased with increasing MMT content due to the physical hindrance of MMT layers to the motion of nylon 10,10 chains.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

School of Mechanical Engineering, Shanghai Jiao Tong University, 200240, Shanghai, China

Zhuang

Huang

et al. 2019

Mathematical Biosciences and Engineering

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“…The estimated position is then used to cross-check the consistency of the vehicle's GPS position. The authors in [33] used the position estimates obtained by the data relative to the neighboring cells in order to check the validity of the GPS position of a smartphone. Different from the aforementioned solutions, the work in [34] considered the use of information received from 5G network to recognize spoofed UAV's GPS positions reported to UTM.…”

Section: Mobile Cellular Network Based Approachesmentioning

confidence: 99%

Deep-Ensemble-Learning-Based GPS Spoofing Detection for Cellular-Connected UAVs

Dang

Benzaïd

Yang

et al. 2022

IEEE Internet Things J.

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Unmanned Aerial Vehicles (UAVs) are an emerging technology in the 5G and beyond systems with the promise of assisting cellular communications and supporting IoT deployment in remote and density areas. Safe and secure navigation is essential for UAV remote and autonomous deployment. Indeed, the open-source simulator can use commercial software-defined radio tools to generate fake GPS signals and spoof the UAV GPS receiver to calculate wrong locations, deviating from the planned trajectory. Fortunately, the existing mobile positioning system can provide additional navigation for cellular-connected UAVs and verify the UAV GPS locations for spoofing detection, but it needs at least three base stations at the same time. In this paper, we propose a novel deep ensemble learning-based, mobile network-assisted UAV monitoring and tracking system for cellular-connected UAV spoofing detection. The proposed method uses path losses between base stations and UAVs communication to indicate the UAV trajectory deviation caused by GPS spoofing. To increase the detection accuracy, three statistics methods are adopted to remove environmental impacts on path losses. In addition, deep ensemble learning methods are deployed on the edge cloud servers and use the multi-layer perceptron (MLP) neural networks to analyze path losses statistical features for making a final decision, which has no additional requirements and energy consumption on UAVs. The experimental results show the effectiveness of our method in detecting GPS spoofing, achieving above 97% accuracy rate under two BSs, while it can still achieve at least 83% accuracy under only one BS.

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“…The work in [16] exploited the Receive Signals Strength (RSS) from 2G base stations to estimate the vehicle position and cross-check the vehicle's GPS position. In [17], the authors used the data relative to the neighboring cells to verify the GPS position. The authors in [18] considered the use of 5G network to infer the trust area where the GPS position should be located and recognize spoofed UAV's GPS positions.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for GPS Spoofing Detection in Cellular-Enabled UAV Systems

Dang

Benzaïd

Yang

et al. 2021

2021 International Conference on Networking and Network Applications (NaNA)

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Cellular-based Unmanned Aerial Vehicle (UAV) systems are a promising paradigm to provide reliable and fast Beyond Visual Line of Sight (BVLoS) communication services for UAV operations. However, such systems are facing a serious GPS spoofing threat for UAV's position. To enable safe and secure UAV navigation BVLoS, this paper proposes a cellular network assisted UAV position monitoring and anti-GPS spoofing system, where deep learning approach is used to live detect spoofed GPS positions. Specifically, the proposed system introduces a MultiLayer Perceptron (MLP) model which is trained on the statistical properties of path loss measurements collected from nearby base stations to decide the authenticity of the GPS position.Experiment results indicate the accuracy rate of detecting GPS spoofing under our proposed approach is more than 93% with three base stations and it can also reach 80% with only one base station.

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GNSS Spoofing Detection Techniques by Cellular Network Cross-check in Smartphones

Cited by 10 publications

References 0 publications

School of Mechanical Engineering, Shanghai Jiao Tong University, 200240, Shanghai, China

School of Mechanical Engineering, Shanghai Jiao Tong University, 200240, Shanghai, China

Deep-Ensemble-Learning-Based GPS Spoofing Detection for Cellular-Connected UAVs

Deep Learning for GPS Spoofing Detection in Cellular-Enabled UAV Systems

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