Intelligent Detection Algorithm Against UAVs' GPS Spoofing Attack

Wang, Shenqing; Wang, Jiang; Su, Chunhua; Ma, Xinshu

doi:10.1109/icpads51040.2020.00058

Cited by 40 publications

(10 citation statements)

References 11 publications

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“…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. This method can detect attacks well when the flight trajectory is not complicated.…”

Section: Related Workmentioning

confidence: 99%

“…Long-Short Term Memory [3] • A comprehensive comparison with encryption-based detection techniques in terms of detection rate and time cost.…”

Section: Category Approach Advantages Limitationsmentioning

confidence: 99%

“…A number of techniques have been proposed to detect GPS spoofing attacks. These methods can be classified into three categories [3]: cryptography-based, signal processing methods, and external UAV characteristics. Cryptography techniques encrypt the GPS signals, which require a key to decrypt [4].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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%

“…Long-Short Term Memory [3] • A comprehensive comparison with encryption-based detection techniques in terms of detection rate and time cost.…”

Section: Category Approach Advantages Limitationsmentioning

confidence: 99%

See 1 more Smart Citation

Dynamic Selection Techniques for Detecting GPS Spoofing Attacks on UAVs

Khoei

Ismail

Kaabouch

2022

Sensors

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“…Recent studies have focused on data-driven and software-based GPS spoofing detections. Various machine learning approaches were exploited in global navigation satellite system (GNSS) use cases, including DNN [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 ]. However, the performance of the previous approaches was tested in simulation environments.…”

Section: Related Workmentioning

confidence: 99%

GPS Spoofing Detection Method for Small UAVs Using 1D Convolution Neural Network

Sung

Park

Kim

et al. 2022

Sensors

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The navigation of small unmanned aerial vehicles (UAVs), such as quadcopters, significantly relies on the global positioning system (GPS); however, UAVs are vulnerable to GPS spoofing attacks. GPS spoofing is an attempt to manipulate a GPS receiver by broadcasting manipulated signals. A commercial GPS simulator can cause a GPS-guided drone to deviate from its intended course by transmitting counterfeit GPS signals. Therefore, an anti-spoofing technique is essential to ensure the operational safety of UAVs. Various methods have been introduced to detect GPS spoofing; however, most methods require additional hardware. This may not be appropriate for small UAVs with limited capacity. This study proposes a deep learning-based anti-spoofing method equipped with 1D convolutional neural network. The proposed method is lightweight and power-efficient, enabling real-time detection on mobile platforms. Furthermore, the performance of our approach can be enhanced by increasing training data and adjusting the network architecture. We evaluated our algorithm on the embedded board of a drone in terms of power consumption and inference time. Compared to the support vector machine, the proposed method showed better performance in terms of precision, recall, and F-1 score. Flight test demonstrated our algorithm could successfully detect GPS spoofing attacks.

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“…Разумеется, на этом рисунке представлены не все наборы возможных последствий атак, а только их общая структура [3]. Тем не менее, эта схема ясно показывает, что практически любая атака, реализованная должным образом, может привести к катастрофическим последствиям не только для самого БПЛА, но и для человека и окружающей среды [4].…”

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Data Generation for Modeling Attacks on UAVs for the Purpose of Testing Intrusion Detection Systems

Basan¹,

Peskova²,

Silin³

et al. 2022

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Today, issues related to ensuring the safety of UAVs are very relevant. Researchers need to develop new protection methods to detect attacks in a timely manner and implement mitigation measures. The authors propose a new concept of attack detection "from inside" the UAV. The idea is to analyze the cyber-physical parameters of the UAV, which may indicate an attack, and its possible consequences. It was determined that to detect an attack and determine the consequences to which it can lead, it is necessary to control not only the initial parameters, but also the internal cyber-physical parameters of the UAV. This will allow predicting the possible consequences of an attack and taking emergency measures. A scheme of the impact of an attack on UAVs and the relationship with security incidents, built using an ontological approach, has been worked out. Two main essences of the UAV are considered - the physical and digital aspects of the UAV. Examples of chains of attacks leading to various consequences are also shown. In the review part, the analysis of methods and algorithms for detecting spoofing attacks using data generators is carried out, based on which conclusions are drawn about their advantages and disadvantages. Further, based on the experiments performed, the authors propose a method for assessing the quality of data and a method for generating anomalous data sets similar to real attack data, which can be used to develop and test methods for detecting and blocking attacks. The architecture of the experimental stand, which was used in the framework of full-scale simulation, is described. At this stand, designed to parse GPS spoofing attacks (GPS spoofing), several scenarios of a normal flight, and then several attack scenarios, were tested. Based on the results of the experiments, a method has been proposed that allows simulating the data corresponding to the attack with the required accuracy. A method for assessing the quality of fake data has also been proposed.

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Intelligent Detection Algorithm Against UAVs' GPS Spoofing Attack

Cited by 40 publications

References 11 publications

Dynamic Selection Techniques for Detecting GPS Spoofing Attacks on UAVs

Dynamic Selection Techniques for Detecting GPS Spoofing Attacks on UAVs

GPS Spoofing Detection Method for Small UAVs Using 1D Convolution Neural Network

Data Generation for Modeling Attacks on UAVs for the Purpose of Testing Intrusion Detection Systems

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