# A Self-Diagnosis Method for Detecting UAV Cyber Attacks Based on Analysis of Parameter Changes

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## Abstract

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## 1. Introduction

- To develop and implement a stand for carrying out an attack on a UAV;
- To carry out flight tests with a UAV in normal mode and during an attack;
- To collect the results in the form of UAV logs;
- To analyze the logs and identify the parameters that are susceptible to attack;
- To get raw data for testing the method;
- To develop a method for detecting anomalies for a UAV and to prove its effectiveness.

## 2. Materials and Methods

_{a}), satellite fix status (G

_{n}), GPS uncertainty (G

_{u}), and GPS noise (G

_{noi}) when detecting a GPS spoofing attack. To analyze changes in these parameters, it is necessary to choose an appropriate type of probability distribution. The choice is based on the fact that these parameters are discrete. Let us consider the most common distribution laws and assess whether they are suitable for the given parameters presented above or not. If the number of occurrences of a random event per unit of time is available, when the fact of the occurrence of this event in each experiment does not depend on how many times and at what points in time it happened in the past, and does not affect the future, and tests are carried out under stationary conditions, then Poisson’s law is usually used to describe such a quantity. Poisson’s law is also called the law of rare events. Thus we used the Poisson distribution law because, when analyzing the parameters in this study, it is necessary to assess the occurrence of unexpected peak values, which are rare events during the total number of operations of the UAV [28]. The Poisson model P() is typically used to describe a rare events scheme [29]. Under some assumptions on the nature of random events (the changes observed are random, independent, and discrete events), their number occurring over a specified time interval or a space area often obeys the Poisson distribution:

_{a}is the UAV’s flight altitude; G

_{n}is the state of fixation by satellites (i.e., the number of satellites from which the UAV receives position information); G

_{u}is the GPS’s uncertainty; G

_{noi}is the GPS’s noise (these parameters are discrete); λ is the mathematical expectation (the average number of events of interest per unit of time); and e is Euler’s number.

- Fixation of the raw values of the analyzed parameters for a certain period of time.
- Plotting a suitable distribution type for the collected parameters.
- Selection of the previous values and supplementing them with those collected at a new point in time, to build a time series of values using a sliding window.
- Construction of a new distribution for new values according to the same distribution law.
- Calculation of the Kullback-Leibler divergence between the two distributions.
- The higher the obtained value of the Kullback-Leibler divergence, the more likely it is that the system has been influenced in the form of an attack or external destructive influence. Typically, this value should be greater than or equal to two. This value was chosen with the condition that the divergence should tend to zero and not exceed one. But to avoid unnecessary false alarms, the threshold was increased to two, which was confirmed experimentally.
- Repeat the algorithm for subsequent values, starting from step 3.

## 3. Results and Discussion

- It is versatile and can be applied to various data sets obtainable from the particular UAV’s sensor system.
- It determines only that a change has occurred in the system and does so quite accurately; it is then up to the decision-making system to determine whether the change is an attack or not.
- It does not require information about reference and normal values, or inputs from external sources. The UAV independently analyzes the changes in indicators and compares its own state at different intervals. If the state is stable or quickly becomes stable, it means that there are no anomalies.

## 4. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 1.**The result of calculating the Kullback-Leibler divergence of the deviation of the flight altitude indicator during an attack.

**Figure 2.**The result of calculating the Kullback-Leibler divergence for the indicator “number of GPS satellites” at the time of the attack.

**Figure 3.**The result of measuring the Kullback-Leibler divergence of the GPS noise index: (

**a**) During the attack; and (

**b**) under normal conditions.

**Figure 6.**The state of the parameter indicating the number of GPS satellites: (

**a**) In normal UAV flight mode; and (

**b**) during a GPS attack.

**Figure 7.**Indication of the level of noise affecting GPS: (

**a**) In the normal mode; and (

**b**) during a GPS attack.

**Figure 9.**The state of the system performance evaluator: (

**a**) In the normal mode; and (

**b**) during a GPS attack.

Part Name | Model |
---|---|

Flight controller | Pix Hawk 4 (STable 10.1 firmware) |

Frame | S500 |

Speed controllers | XT-XINTE 30A |

Telemetry | 3DR Radio Telemetry 915 MHz 100 mW Aerial Ground Data Transmission Module for Pixhawk 4 |

Receiver | FS-I6B |

Battery | ZOP Power 3S 11.1V 4200 mAh 40C Lipo Battery XT60 Plug |

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**MDPI and ACS Style**

Basan, E.; Basan, A.; Nekrasov, A.; Fidge, C.; Gamec, J.; Gamcová, M.
A Self-Diagnosis Method for Detecting UAV Cyber Attacks Based on Analysis of Parameter Changes. *Sensors* **2021**, *21*, 509.
https://doi.org/10.3390/s21020509

**AMA Style**

Basan E, Basan A, Nekrasov A, Fidge C, Gamec J, Gamcová M.
A Self-Diagnosis Method for Detecting UAV Cyber Attacks Based on Analysis of Parameter Changes. *Sensors*. 2021; 21(2):509.
https://doi.org/10.3390/s21020509

**Chicago/Turabian Style**

Basan, Elena, Alexandr Basan, Alexey Nekrasov, Colin Fidge, Ján Gamec, and Mária Gamcová.
2021. "A Self-Diagnosis Method for Detecting UAV Cyber Attacks Based on Analysis of Parameter Changes" *Sensors* 21, no. 2: 509.
https://doi.org/10.3390/s21020509