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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.

An implementation of methods for protecting unmanned aerial vehicles (UAVs) from spoofing attacks of the global positioning system (GPS) to ensure safe navigation is discussed in this paper. The Global Navigation Satellite System (GNSS) is widely used to locate UAVs and is by far the most popular navigation solution. This is due to the simplicity and relatively low cost of this technology, as well as the accuracy of the transmitted coordinates. However, there are many security threats to GPS navigation. Primarily this is due to the nature of the GPS signal, the signal is transmitted in the clear, so an attacker can block or tamper with it. This study analyzes the existing GPS protection methods. As part of the study, an experimental stand and scenarios of attacks on the UAV GPS system were developed. Data from the UAV flight logbook was collected and an analysis of cyber-physical parameters was carried out to see an effect of the attack on the on-board sensors readings. Based on this, a new method for detecting UAV anomalies was proposed, based on an analysis of changes in UAV internal parameters. This self-diagnosis method allows the UAV to independently assess the presence of changes in its subsystems and identify signs of a cyberattack. To detect an attack, the UAV collects data on changes in cyber-physical parameters over a certain period of time, then updates this data. As a result it is necessary for the UAV to determine the degree of difference between the two time series of the collected data. The greater the degree of difference between the updated data and the previous ones, the more likely the UAV is under attack.