Abnormal Behavior Detection Scheme of UAV Using Recurrent Neural Networks

Xiao, Ke; Zhao, Jianyu; He, Yunhua; Li, Chaofei; Cheng, Wei

doi:10.1109/access.2019.2934188

Cited by 25 publications

(12 citation statements)

References 22 publications

(24 reference statements)

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“…The capability of ML to learn from historical data to unveil hidden patterns has driven the recent trend of using ML techniques for GPS spoofing detection in UAV environments. In this vein, different ML methods have been proposed to detect GPS spoofing either by classifying the spoofed GPS signal directly (e.g., [35]- [40]) or by verifying the GPS information supplementally (e.g., [41]- [44]). The GPS signal classification methods take advantage of the ML classifier techniques to discriminate the fake GPS signal from the actual GPS signal, while the GPS information verification methods make use of Manesh et al [35] exploited the received GPS signal characteristics, such as pseudo range, Doppler shift and signalto-noise ratio, to build a supervised neural network model for detecting GPS spoofing against unmanned aerial systems.…”

Section: E Machine Learning Based Approachesmentioning

confidence: 99%

“…Luckily, ML is a potential enabler for building MPSbased spoofing detection services that can perform well even in some worse cases. In [44], Xiao et al investigated the potential of recurrent neural networks in recognizing the deviation in the UAV's trajectory caused GPS spoofing. To this end, the DoA measurements of BSs' signals from the UAV equipment are analyzed by the proposed recurrent neural network models.…”

Section: E Machine Learning Based Approachesmentioning

confidence: 99%

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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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Section: E Machine Learning Based Approachesmentioning

confidence: 99%

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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“…El sistema se aplicó satisfactoriamente usando datos de sensores de orientación. Otro trabajo de detección de fallas considerando redes neuronales recurrente se propuso en Xiao et al (2019), donde se logró detectar interferencias en el sistema GPS que afectan la navegación del vehículo. En Yang et al (2020) se considera un enfoque híbrido para fallas en sensores basados en el análisis secuencial de residuos y máquinas de vectores de soporte.…”

Section: Introductionunclassified

Detección de fallas en vehículos aéreos no tripulados mediante señales de orientación y técnicas de aprendizaje de máquina

López‐Estrada

Méndez-López

Santos‐Ruiz

et al. 2021

Rev. iberoam. autom. inform. ind.

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<p>Este trabajo propone un esquema de detección y localización de fallas en los actuadores de un vehículo aéreo no tripulado (VANT) del tipo cuadrirrotor. Para ello, se considera un enfoque basado en datos haciendo uso de técnicas de aprendizaje de máquina. En este enfoque se construye un modelo implícito del sistema a través de la información proporcionada por los sensores del VANT. Primero, a través de un plataforma de vuelo de tipo giroscópica, se captan las vibraciones correspondientes a la orientación, posición angular y aceleración lineal cuando el vehículo se encuentra en vuelo estacionario en condiciones nominales. Estos datos se procesan mediante Análisis en Componentes Principales (PCA) para la extracción de características. Posteriormente, se induce una falla a los actuadores a través de un recorte en cada una de las hélices del VANT que ocasionan una reducción del empuje generado por los rotores. Estos datos se proyectan también al subespacio de componentes principales y se comparan con los datos nominales. Para discernir entre los datos nominales y los datos cuando el vehículo presenta falla, se emplea el estadístico T2 de Hotelling. Finalmente, el desarrollo se complementa con los algoritmos de clasificación de k-vecinos más cercanos (k-NN) y de máquina de vectores de soporte (SVM). Los resultados muestran una tasa de clasificación correcta del 89.6 % (k-NN) y 92.4 %(SVM) respectivamente para 423 conjuntos de datos de validación.</p>

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“…Today, UAVs have made significant progress in military and defense areas such as reconnaissance, surveillance, and security missions as well as in civilian areas such as urban planning, search, law enforcement, traffic monitoring, accident management, agricultural assessment, and entertainment. Environmental monitoring Photography, infrastructure monitoring and rescue operations are growing rapidly [1][2][3][4]. Because UAVs have security vulnerabilities, hackers exploit this security vulnerability.…”

Section: Introductionmentioning

confidence: 99%

Secure communication between UAVs using a method based on smart agents in unmanned aerial vehicles

Faraji-Biregani¹,

Fotohi

2020

J Supercomput

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Unmanned Aerial Vehicles (UAVs) can be deployed to monitor very large areas without the need for network infrastructure. UAVs communicate with each other during flight and exchange information with each other. However, such communication poses security challenges due to its dynamic topology. To solve these challenges, the proposed method uses two phases to counter malicious UAV attacks. In the first phase, we applied a number of rules and principles to detect malicious UAVs. In this phase, we try to identify and remove malicious UAVs according to the behavior of UAVs in the network in order to prevent sending fake information to the investigating UAVs. In the second phase, a mobile agent based on a three-step negotiation process is used to eliminate malicious UAVs. In this way, we use mobile agents to inform our normal neighbor UAVs so that they do not listen to the data generated by the malicious UAVs. Therefore, the mobile agent of each UAV uses reliable neighbors through a three-step negotiation process so that they do not listen to the traffic generated by the malicious UAVs. The NS-3 simulator was used to demonstrate the efficiency of the SAUAV method. The proposed method is more efficient than CST-UAS, CS-AVN, HVCR, and BSUM-based methods in detection rate, false positive rate, false negative rate, packet delivery rate, and residual energy.

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Abnormal Behavior Detection Scheme of UAV Using Recurrent Neural Networks

Cited by 25 publications

References 22 publications

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

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

Detección de fallas en vehículos aéreos no tripulados mediante señales de orientación y técnicas de aprendizaje de máquina

Secure communication between UAVs using a method based on smart agents in unmanned aerial vehicles

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