# A Self-Diagnosis Method for Detecting UAV Cyber Attacks Based on Analysis of Parameter Changes
## 1 Introduction
In some methods, in addition to GPS, other sensory mechanisms such as an accelerometer magnetometer and others are used to improve the quality of flight [23]. In this case, the correlation can take place both between devices of one UAV and between devices of a group of UAVs [24]. [PERSON] et al. presented a method for determining the authenticity of a GPS signal based on the correlation between true and incoming signals using C-Support Vector Machine (C-SVM) classification [25]. Application of their method is suggested at the signal receiver level to avoid changes that may occur during signal processing. To train their classifier the authors used both data obtained under laboratory conditions and in real cases of attacks. They believe that the supervised machine learning-based approach (C-SVM) has great prospects since they managed to achieve positive results. The model they presented has achieved high detection rates compared to previously existing ones. In addition, by including a real meaconing event, the complexity of the model was improved. Thus, supplementing training datasets with real-world scenarios appears to be a valuable contribution to safety-critical models such as the detection of attempts to manipulate Global Navigation Satellite System (GNSS) signals. However, a disadvantage of this approach is the significant time and effort required for preparation and elaboration of the data set for training. [PERSON] and [PERSON] proposed a method for direct detection of GPS spoofing attacks using a positioning system based on an accelerometer [26]. They conducted a performance analysis using probability density functions. The difference between the measurements obtained by the accelerometer and a GPS receiver is used to detect an attack. The value of the error is used to obtain decision variables indicating the presence of an attack. The performance of two decision variables was compared by calculating the spoofing detection probability and the detectable minimum spoofing acceleration, considering the predetermined false alarm probability and the predetermined detection probability. The decision variables represent the change in the horizontal and vertical acceleration, both of which must be used together. Then the authors add another variable for the solution of the northern acceleration. The GPS spoofing detection method suggested in their paper is thus influenced by the acceleration error. Flying or driving conditions can affect GPS spoofing detection performance. For instance, if a ground vehicle encounters obstacles in the road, such as potholes, bumps, etc., its accelerometers can show large changes and degrade the ability to detect GPS spoofing. [PERSON] et al. presented a method for detecting and countering spoofing [27]. They used an algorithm based on Maximum Likelihood (ML) to solve the problem of multipath reduction to anti-spoofing. The authors presented tracking channels using multi-correlators and proposed a set of actions to detect and remove fake signals, to ensure that the receiver blocks the signals during a spoofing attack. Due to the complexity and variety of interference implemented by an attacker, using one anti-spoofing method may not prevent all types of spoofing. For example, an anti-spoofing method based on a multi-correlator structure can help block the signal. However, if spoofing started during data collection, this method will not be able to distinguish the genuine and fake recipient signals. Experimental results show that their proposed anti-spoofing method allows estimating the time delay, amplitude, and phase of the carrier of a fake signal with a small delay, and then eliminating it by adding the opposite fake signal. In addition, this method can perform a self-test, so that it can directly distinguish the phase of the genuine signal when the fake signal has a long delay.