Detection of GPS Spoofing Attack on Unmanned Aerial Vehicle System

Chen, Liang; Miao, Meixia; Ma, Jianfeng; Yan, Hongyan; Zhang, Qun; Li, Xinghua; Li, Teng

doi:10.1007/978-3-030-30619-9_10

Cited by 19 publications

(10 citation statements)

References 22 publications

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“…Compared with our previous work, 30 we have made a detailed analysis and demonstration of the proposed scheme. In the case of UAVs formation, we also have made a further in-depth study on the positioning of the GCS, and proposed a positioning method based on the adaptive step iterative algorithm, and proved its effectiveness through the analysis of experimental data.…”

Section: Discussionmentioning

confidence: 99%

Detection of global positioning system spoofing attack on unmanned aerial vehicle system

Chen

Miao

et al. 2020

Concurrency and Computation

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Most of the existing global positioning system (GPS) spoofing detection schemes are vulnerable to the generative GPS spoofing attack, or require additional auxiliary equipment and extensive signal processing capabilities, leading to defects such as low real-time performance and large communication overhead which are not available for the unmanned aerial vehicle (UAV, also known as drone) system. Therefore, we propose a novel solution which employs information fusion based on the GPS receiver and inertial measurement unit. We use a real-time model of tracking and calculating to derive the current position of the drones which are then contrasted with the position information received by the receiver to verify whether the presence or absence of spoofing attack. Subsequent experimental work shows that, the proposed method can accurately detect the spoof within 8 seconds, with a detection rate (DR) of 98.6%. Compared with the existing schemes, the performance of real-time detecting is improved while the DR is ensured. Even in our worst-case, we detect the spoof within 28 seconds after the UAV system starts its mission.

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Section: Discussionmentioning

confidence: 99%

Detection of global positioning system spoofing attack on unmanned aerial vehicle system

Chen

Miao

et al. 2020

Concurrency and Computation

Self Cite

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“…In [17], the authors proposed a GPS spoofing-detection framework that needs minimal prior configuration and applies information fusion. The real-time detection scheme derives the current UAV location from IMU and compares it to the location information received by the GPS receiver to determine if the UAV system was experiencing a GPS spoofing attack.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Selection Techniques for Detecting GPS Spoofing Attacks on UAVs

Khoei

Ismail

Kaabouch

2022

Sensors

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Unmanned aerial vehicles are prone to several cyber-attacks, including Global Positioning System spoofing. Several techniques have been proposed for detecting such attacks. However, the recurrence and frequent Global Positioning System spoofing incidents show a need for effective security solutions to protect unmanned aerial vehicles. In this paper, we propose two dynamic selection techniques, Metric Optimized Dynamic selector and Weighted Metric Optimized Dynamic selector, which identify the most effective classifier for the detection of such attacks. We develop a one-stage ensemble feature selection method to identify and discard the correlated and low importance features from the dataset. We implement the proposed techniques using ten machine-learning models and compare their performance in terms of four evaluation metrics: accuracy, probability of detection, probability of false alarm, probability of misdetection, and processing time. The proposed techniques dynamically choose the classifier with the best results for detecting attacks. The results indicate that the proposed dynamic techniques outperform the existing ensemble models with an accuracy of 99.6%, a probability of detection of 98.9%, a probability of false alarm of 1.56%, a probability of misdetection of 1.09%, and a processing time of 1.24 s.

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“…The ML algorithms learn from the training data and improve themselves for achieving better results and high accuracy without any human intervention, which is a huge advantage. The ML algorithms can be deployed for detecting the presence of malicious drones in the network and can help in preventing the attacks such as man-in-the-middle attack [60] and spoofing attacks [61]. Such algorithms keep on improving with increasing experience, and provide better and more accurate results.…”

Section: Motivation For Using ML For Drone Communication Securitymentioning

confidence: 99%

Fast, Reliable, and Secure Drone Communication: A Comprehensive Survey

Hassija

Chamola

Agrawal

et al. 2021

IEEE Commun. Surv. Tutorials

148

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Drone security is currently a major topic of discussion among researchers and industrialists. Although there are multiple applications of drones, if the security challenges are not anticipated and required architectural changes are not made, the upcoming drone applications will not be able to serve their actual purpose. Therefore, in this paper, we present a detailed review of the security-critical drone applications, and security-related challenges in drone communication such as DoS attacks, Man-in-the-middle attacks, De-Authentication attacks, and so on. Furthermore, as part of solution architectures, the use of Blockchain, Software Defined Networks (SDN), Machine Learning, and Fog/Edge computing are discussed as these are the most emerging technologies. Drones are highly resourceconstrained devices and therefore it is not possible to deploy heavy security algorithms on board. Blockchain can be used to cryptographically store all the data that is sent to/from the drones, thereby saving it from tampering and eavesdropping. Various ML algorithms can be used to detect malicious drones in the network and to detect safe routes. Additionally, the SDN technology can be used to make the drone network reliable by allowing the controller to keep a close check on data traffic, and fog computing can be used to keep the computation capabilities closer to the drones without overloading them.

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Detection of GPS Spoofing Attack on Unmanned Aerial Vehicle System

Cited by 19 publications

References 22 publications

Detection of global positioning system spoofing attack on unmanned aerial vehicle system

Detection of global positioning system spoofing attack on unmanned aerial vehicle system

Dynamic Selection Techniques for Detecting GPS Spoofing Attacks on UAVs

Fast, Reliable, and Secure Drone Communication: A Comprehensive Survey

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