SCER Spoofing Attacks on the Galileo Open Service and Machine Learning Techniques for End-User Protection

Gallardo, Francisco; Yuste, Antonio Pérez

doi:10.1109/access.2020.2992119

Cited by 31 publications

(10 citation statements)

References 12 publications

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“…This subsystem is taken for granted, as Galileo is already built and precedes the inception of the proposed system described in this paper. It was shown in [ 23 ] how the received Galileo signal (the one that will be used for the measurements) can be described in its Intermediate Frequency (IF) as:

…”

Section: System Descriptionmentioning

confidence: 99%

An Inexpensive Unmanned Aerial Vehicle-Based Tool for Mobile Network Output Analysis and Visualization

Buggiani

Ortega

Silva

et al. 2023

Sensors

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Usage of Unmanned Aerial Vehicles (UAVs) for different tasks is widespread, as UAVs are affordable, easy to manoeuvre and versatile enough to execute missions in a reliable manner. However, there are still fields where UAVs play a minimal role regardless of their possibilities. One of these application domains is mobile network testing and measurement. Currently, the procedures used to measure the main parameters of mobile networks in an area (such as power output or its distribution in a three-dimensional space) rely on a team of specialized people performing measurements with an array of tools. This procedure is significantly expensive, time consuming and the resulting outputs leave a higher degree of precision to be desired. An open-source UAV-based Cyber-Physical System is put forward that, by means of the Galileo satellite network, a Mobile Data Acquisition System and a Graphical User Interface, can quickly retrieve reliable data from mobile network signals in a three-dimensional space with high accuracy for its visualization and analysis. The UAV tested flew at 40.43 latitude and −3.65 longitude degrees as coordinates, with an altitude over sea level of around 600–800 m through more than 40 mobile network cells and signal power displayed between −75 and −113 decibels.

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…”

Section: System Descriptionmentioning

confidence: 99%

An Inexpensive Unmanned Aerial Vehicle-Based Tool for Mobile Network Output Analysis and Visualization

Buggiani

Ortega

Silva

et al. 2023

Sensors

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“…O'Driscoll and Fernandez-Hernandez (2020) help the receiver to re-encode the navigation message into symbols and compare the symbol error rates to avoid the forward estimation attack (Curran and O'Driscoll, 2016). Gallardo and Yuste (2020) analyzed the model of SCER spoofing attacks on the OSNMA and proposed a machine learning technique for its detection.…”

Section: Open Service Navigation Message Authentication For Galileomentioning

confidence: 99%

Authenticating GNSS civilian signals: a survey

Yuan

Tang

2023

Satell Navig

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Civilian services of Global Navigation Satellite System are threatened by spoofing attacks since it is hard to determine the authenticity of a navigation signal with a detailed structure open to the public. Signal authentication effectively protects the security of the signal by attaching unforgeable information to one or several elements of the signal. Receivers can verify the authenticity of the signal by extracting and validating this information. Developing good signal authentication schemes requires understanding possible spoofing modes, signal element specialty, and performance evaluation methods. This paper is an overview of navigation signal authentication, where the theories and reported approaches are described in detail. A design/performance matrix that demonstrates the advantages and defects of the signal element and its authentication design is summarized. Recommendations are proposed to improve the robustness, security, efficiency, and implementation hardness for future designs of navigation signal authentication.

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“…The authors in [37] proposed a GPS spoofing detection approach that leverages a supervised ML algorithm, specifically Support Vector Machine (SVM), to analyze the cross-correlation among the GPS signals of multiple GPS receivers. The work in [38] introduced RBF SVM, Ada Boost, Decision Trees, K-Nearest Neighbors, Random Forests methods for GPS signal's radio frequency interference analysis under the assumption that the attacker was not able to null the satellite signal. It is worth mentioning that the space weather changes diminish the features of raw GPS signals [39] and the attacker can broadcast the same frequency jamming GPS signals for blinding the GPS receiver [40], which can considerably hinder the effectiveness of the aforementioned approaches.…”

Section: E Machine Learning 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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SCER Spoofing Attacks on the Galileo Open Service and Machine Learning Techniques for End-User Protection

Abstract: This work was possible due to the agreement between DLR GfR mbH and the Technical University of Madrid for the development of an industrial PhD for researching SCER OS-NMA anti-spoofing protection techniques.

Cited by 31 publications

References 12 publications

An Inexpensive Unmanned Aerial Vehicle-Based Tool for Mobile Network Output Analysis and Visualization

An Inexpensive Unmanned Aerial Vehicle-Based Tool for Mobile Network Output Analysis and Visualization

Authenticating GNSS civilian signals: a survey

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

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