# A Self-Diagnosis Method for Detecting UAV Cyber Attacks Based on Analysis of Parameter Changes
## 1 Introduction
With the same GPS signals, the measured distances are the same as the previously recorded distances. However, in a GPS spoofing attack, the distances obtained will be very close to zero, since all receivers transmit information where the same location is indicated. [PERSON] et al. demonstrated the possibility of using a receiver with two antennas for detecting GPS spoofing attacks [16]. Their proposed method is based on fixing the difference in carrier frequencies between different antennas tied to the same generator. In their configuration, the attacker must use an additional transmit antenna for each additional receiver antenna, which complicates the attacker's task. [PERSON] et al. [18] use multiple receivers to confirm the authenticity of GPS signals based on correlation with the military GPS signal without the need to decode it. This uses cross-validation receivers to authenticate the GPS signals. It has been shown that the proposed technique is efficient even when cross validation receivers are tampered with. This method has been tested on fixed and moving GPS receivers and shown to be effective in detecting spoofing attacks. [PERSON] et al. [19] presented an approach to detecting a GPS spoofing attack for a UAV, based on state analysis using a Support Vector Machine (SVM), which is used as an anomaly detection tool. They present solutions for detecting anomalies and a simulation environment for GPS attacks to assess the functionality and performance of the method. Their approach does not require additional equipment, and so it can be implemented on a small UAV. On the other hand, if the intruder has absolute knowledge of the positioning and trajectory of the UAV, he can go unnoticed by the system, causing significant false operations. Nonetheless, since usually the intruder does not know the actual trajectory of the UAV, the risk of a false alarm is small. Thus, the system can detect any spoofing attack. [PERSON] et al. [20] suggested a protective mechanism based on the concept of co-localization [21]. Their mechanism is a methodology that allows a UAV to determine its real position in a two-dimensional coordinate system using the location of three other UAVs. It is assumed that each UAV has a device for measuring the relative distances to other neighboring UAVs. The co-localization of the UAV selects any three adjacent UAV to update their location, assuming that the selected UAVs do not lie on a straight line. The UAV can then pinpoint its 2D position accurately. With a covert GPS spoofing attack, the UAV cannot trust its GPS location or the location of other UAVs. However, the choice of a nearby UAV for the co-localization mechanism is fraught with risk since the UAV itself may be compromised. To overcome this limitation, the authors propose a protection mechanism based on the assumption that an attacker using GPS spoofing can attack only one UAV [20]. In their proposed mechanism, the UAV will use the location of four adjacent UAVs, instead of three, to determine its real location by identifying the UAV under attack and excluding it from calculations. This mechanism has the same cooperative localization requirements (i.e., the UAVs are non-collinear), UAVs can query the location of other UAVs through communication between them, and each UAV must be able to measure distances to neighboring UAVs. Thus, it should be noted that their method has many restrictions on its applicability. [PERSON] et al. presented a method of counteracting GPS attacks based on the use of a technical vision system, which additionally allows calculation of the UAV's speed and some other indicators and correlates them with the data received from the GPS [22]. Thus, the main methods of protecting UAVs from attacks related to GPS signal spoofing are based on the analysis of the state change in comparison with the reference of one sensor or on the correlation of data obtained from several GPS sensors.