GPS Spoofing Detector with Adaptive Trustable Residence Area for Cellular based-UAVs

Dang, Yongchao; Benzaïd, Chafika; Shen, Yulong; Taleb, Tarik

doi:10.1109/globecom42002.2020.9348030

Cited by 15 publications

(18 citation statements)

References 12 publications

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“…The proposed method detects GPS spoofing attacks to some extent; however, the system experienced performance degradation during long attacks due to the interaction with the GPS sensor, especially with the Micro-Electro-Mechanical Systems sensors. In [24], a GPS spoofing detection method was proposed that leverages the uplink received signal strength measurements collected from base stations to identify the adaptive trustable residence area, which represents the trust region within which the UAV GPS position should be located to be classified as authentic or non-spoofed. In [3], the authors proposed a method for GPS spoofing attack detection based on a machine learning algorithm, Long Short-Term Memory, and compared the results to a method based on specifically designed UAV flight paths.…”

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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Section: Related Workmentioning

confidence: 99%

Dynamic Selection Techniques for Detecting GPS Spoofing Attacks on UAVs

Khoei

Ismail

Kaabouch

2022

Sensors

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“…Other studies suggested countermeasures that do not rely on GPS for navigation and instead rely on the cellular network; for example in [ 14 ], the authors presented “drive me not”, a GPS spoofing detection method that utilizes mobile cellular network infrastructure to validate the position received by the GPS infrastructure. Other research [ 15 ] utilized UpLink’s received signal strength (RSS) measurements for cross-position validation for GPS spoofing detection. In another study [ 16 ], the authors built a network of clustered ground base stations (BSs) that cooperatively serve a number of UAV-UEs.…”

Section: Related Workmentioning

confidence: 99%

Towards the Detection of GPS Spoofing Attacks against Drones by Analyzing Camera’s Video Stream

Davidovich

Nassi

Elovici

2022

Sensors

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A Global Positioning System (GPS) spoofing attack can be launched against any commercial GPS sensor in order to interfere with its navigation capabilities. These sensors are installed in a variety of devices and vehicles (e.g., cars, planes, cell phones, ships, UAVs, and more). In this study, we focus on micro UAVs (drones) for several reasons: (1) they are small and inexpensive, (2) they rely on a built-in camera, (3) they use GPS sensors, and (4) it is difficult to add external components to micro UAVs. We propose an innovative method, based on the video stream captured by a drone’s camera, for the real-time detection of GPS spoofing attacks targeting drones. The proposed method collects frames from the video stream and their location (GPS coordinates); by calculating the correlation between each frame, our method can detect GPS spoofing attacks on drones. We first analyze the performance of the suggested method in a controlled environment by conducting experiments on a flight simulator that we developed. Then, we analyze its performance in the real world using a DJI drone. Our method can provide different levels of security against GPS spoofing attacks, depending on the detection interval required; for example, it can provide a high level of security to a drone flying at altitudes of 50–100 m over an urban area at an average speed of 4 km/h in conditions of low ambient light; in this scenario, the proposed method can provide a level of security that detects any GPS spoofing attack in which the spoofed location is a distance of 1–4 m (an average of 2.5 m) from the real location.

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“…Those new standards allow the terrestrial cellular networks to provide identifying, locating, and tracking services for the UAS in order to enhance the security of the UAS operation. In this vein, the authors in [25] proposed a UAV tracking method, namely Adaptive Trustable Residence Area (ATRA), to detect the spoofed GPS position by leveraging up-link received signal strength indication. Regardless of the performance brought by the ATRA method, it requires at least three base stations at the same time.…”

Section: Introductionmentioning

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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GPS Spoofing Detector with Adaptive Trustable Residence Area for Cellular based-UAVs

Cited by 15 publications

References 12 publications

Dynamic Selection Techniques for Detecting GPS Spoofing Attacks on UAVs

Dynamic Selection Techniques for Detecting GPS Spoofing Attacks on UAVs

Towards the Detection of GPS Spoofing Attacks against Drones by Analyzing Camera’s Video Stream

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

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