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.

/pdf/dynamic-selection-techniques-for-detecting-gps-spoofing-3c3w1sle.pdf

Dynamic Selection Techniques for Detecting GPS Spoofing Attacks on UAVs

Artificial intelligence for intrusion detection systems in Unmanned Aerial Vehicles

Machine Learning Modeling of GPS Features with Applications to UAV Location Spoofing Detection and Classification

The potential of indoor unmanned aerial vehicle (UAV) localization is paramount for diversified applications within large industrial sites, such as hangars, malls, warehouses, production lines, etc. In such real-time applications, autonomous UAV location is required constantly. This paper comprehensively reviews radio signal-based wireless technologies, machine learning (ML) algorithms and ranging techniques that are used for UAV indoor positioning systems. UAV indoor localization typically relies on vision-based techniques coupled with inertial sensing in indoor Global Positioning System (GPS)-denied situations, such as visual odometry or simultaneous localization and mapping employing 2D/3D cameras or laser rangefinders. This work critically reviews the research and systems related to mini-UAV localization in indoor environments. It also provides a guide and technical comparison perspective of different technologies, presenting their main advantages and disadvantages. Finally, it discusses various open issues and highlights future directions for UAV indoor localization.

/pdf/a-review-of-indoor-positioning-systems-for-uav-localization-3nd9mrgr.pdf

A Review of Indoor Positioning Systems for UAV Localization with Machine Learning Algorithms

To ensure that unmanned aerial vehicle (UAV) positioning is not affected by GPS spoofing signals, we propose PerDet, a perception-data-based UAV GPS spoofing detection approach utilizing machine learning algorithms. Based on the principle of the position estimation process and attitude estimation process, we choose the data gathered by the accelerometer, gyroscope, magnetometer, GPS and barometer as features. Although these sensors have different shortcomings, their variety makes sure that the selected perception data can compensate for each other. We collect the experimental data through real flights, which make PerDet more practical. Furthermore, we run various machine learning algorithms on our dataset and select the most effective classifier as the detector. Through the performance evaluation and comparison, we demonstrate that PerDet is better than existing methods and is an effective method with a detecting rate of 99.69%. For a fair comparison, we reproduce the existing method and run it on our dataset to compare the performance between this method and our PerDet approach.

/pdf/perdet-machine-learning-based-uav-gps-spoofing-detection-30cqcyx9.pdf

PerDet: Machine-Learning-Based UAV GPS Spoofing Detection Using Perception Data

Unmanned Aerial Vehicle (UAV) technology is more and more widely used in the field of civil and military information acquisition. GPS plays the most critical part of UAVs' navigation and positioning. However, since the communication channel of the GPS signals is open, attackers can disguise as real GPS signals to launch GPS spoofing attacks on civilian UAVs. At present, the detection schemes for GPS spoofing attacks can be divided into three categories respectively based on encryption and digital signatures, the characteristics of the GPS signal and various external characteristics of UAVs. However, there are some problems in these methods, such as low computing efficiency, difficulty in equipment upgrading, and limited application scenarios. To solve these problems, we propose a new GPS spoofing attack detection method based on Long Short-Term Memory (LSTM) which is a machine learning algorithm. In order to improve the detection ratio, after the machine learning algorithm, we let the UAVs fly according to the path of a specific shape to accurately detect GPS spoofing attacks. This is also the first time machine learning has been used to detect GPS spoofing attacks. According to our algorithm, we can detect GPS spoofing attacks accurately and quickly in a short time. This paper describes in detail the algorithm we proposed to resist GPS spoofing attacks, and the corresponding experiments are carried out in the simulation environment. The experimental results show that our method can quickly and accurately detect UAV GPS spoofing attacks without requiring upgrades to existing equipment.

Intelligent Detection Algorithm Against UAVs' GPS Spoofing Attack

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Intelligent Detection Algorithm Against UAVs' GPS Spoofing Attack