Abstract
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.
Highlights
The use of unmanned aerial vehicles (UAVs) in military and civilian applications has exponentially increased over the last decade
The third category is based on external UAV characteristics, such as speed and acceleration, that that can be measured by the sensors of the UAV flight control system, such as barometer, inertial measurement unit (IMU), and compass
Several types of tuning techniques have been proposed in the literature; Bayesian optimization has emerged as an effective approach, outperforming other techniques such as random search and grid search since grid search suffers from the curse of dimensionality and random search is not suitable for training complex models [33,34,35]
Summary
The use of unmanned aerial vehicles (UAVs) in military and civilian applications has exponentially increased over the last decade. 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. The third category is based on external UAV characteristics, such as speed and acceleration, that that can be measured by the sensors of the UAV flight control system, such as barometer, inertial measurement unit (IMU), and compass These GPS spoofing detection methods have some drawbacks that limit their application. A holistic solution that can interpret and perform better than any single conventional ML model in detecting GPS spoofing attacks is known as multiple classifier systems (MCS) In this technique, a pool of classifiers is competing to provide the best prediction for a data sample, and the final result belongs to the most efficient base classifier.
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