=Paper=
{{Paper
|id=Vol-3094/invited_paper
|storemode=property
|title=Analysis of the Impact of a GPS Spoofing Attack on a UAV
|pdfUrl=https://ceur-ws.org/Vol-3094/invited_paper.pdf
|volume=Vol-3094
|authors=Elena Basan,Oleg Makarevich,Maria Lapina,Massimo Mecella
}}
==Analysis of the Impact of a GPS Spoofing Attack on a UAV ==
https://ceur-ws.org/Vol-3094/invited_paper.pdf
Analysis of the Impact of a GPS Spoofing Attack on a UAV
Elena Basan 1, Oleg Makarevich 1, Maria Lapina 2 and Massimo Mecella 3
1
Southern Federal University, Chekhov St. 2, Taganrog, 347922, Russia
2
North-Caucasus Federal University, Prospect Kulakova 2, 355000, Stavropol, Russia
3
SAPIENZA Università di Roma, via Ariosto 25, I-00185 Roma, Italy
Abstract
The paper discusses scenarios of GPS spoofing attacks on UAVs. Three scenarios are
proposed: when the attack is not carried out, when a low-intensity attack is carried out, and
when an intense attack is carried out. After that, logs from the UAV are collected and the result
of the attack is analyzed in relation to its impact on the cyber-physical parameters of the UAV.
This analysis is carried out with the aim of further developing an intrusion detection and
response system. It is necessary to understand which parameters change to what extent and
under the influence of which attack scenario.
Keywords 1
GPS, Spoofing Attack, UAV, Kalman filters, attack scenario
1. Introduction
Today, an attack on the global navigation system GPS, if properly executed, can lead to serious
consequences for a UAV or a group of UAVs [1]. The flight controller, in addition to the natural error
present in the sensor readings, is also affected by natural GPS vulnerabilities such as signal blocking or
jamming that compromise the availability of the GPS signal. In the GPS Spoofing Attack, satellites
transmitting a GPS signal are forged to manipulate the UAV's navigation system by transmitting fake
coordinates created by an attacker with a higher power than the original signal [2], [3]. However, civil
GPS does not have the protection mechanisms used to transmit the signal. Consequently, civilian GPS
is highly vulnerable to spoofing attacks [4,5]. A targeted attack that takes control of the UAV or simply
destroys it can easily harm everyone in the UAV's flight area or damage other vehicles [6-8]. Therefore,
GPS spoofing has become an important research topic, since an attacker can hijack a UAV, use it to
eavesdrop or attack people or objects without the need to stay close to the target. Thus, the topic related
to the analysis of the impact of the GPS spoofing attack on the UAV navigation system is very relevant.
Before developing a protection system against such attacks, it is necessary to understand how the attack
affects the UAV and its subsystems to further analyze this influence and detect the attack. The main
purpose of this paper is to analyze the possibility of detecting an attack on the UAV navigation system
by analyzing changes in the readings of the flight controller sensors. It is necessary to determine which
cyber-physical parameters change the readings because of a GPS spoofing attack. Once the sets of
parameters that are susceptible to attack are determined, you can develop a system for detecting and
preventing attacks and intrusions.
AISMA-2021: International Workshop on Advanced in Information Security Management and Applications, October 1, 2021, Stavropol,
Krasnoyarsk, Russia
EMAIL: ebasan@sfedu.ru (Elena Basan), obmakarevich@sfedu.ru (Oleg Makarevich), mlapina@ncfu.ru (Maria Lapina),
mecella@diag.uniroma1.it (Massimo Mecella)
ORCID: 0000-0001-6127-4484 (Elena Basan), 0000-0003-0066-8564 (Oleg Makarevich), 0000-0001-8117-9142 (Maria Lapina),
0000-0002-9730-8882 (Massimo Mecella)
© 2022 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
6
2. Analysis of methods of protection against spoofing attacks on UAVS GPS
system
Today there are many methods for detecting an attack on a UAV navigation system. Let us analyze
the parameters for detecting attacks on which various methods are based. Today there are many methods
for detecting an attack on a UAV navigation system. Let us analyze the parameters for detecting attacks
on which various methods are based. In [9], a method for detecting a GPS spoofing attack based on the
use of neural networks is presented. Let us determine the parameters that the authors take to generate
data for training a neural network.
Satellite Number: Used to identify the various satellites orbiting the Earth. This number can be read
from the content of the decoded received GPS signals.
Signal-to-Noise Ratio: SNR is an indicator of the strength of the GPS signal after being mixed with
noise and interference. It can be measured and calculated from the received signals using special
algorithms [10].
Pseudo range: Each GPS satellite has a unique Gold code; its autocorrelation function is in the shape
of an equilateral triangle, which peaks with perfect correlation. This characteristic can be used to
determine the transmission time signal from the satellite to the receiver by cross correlating the Gold
code with its copy generated by the receiver.
Doppler shift: The GPS carrier signal is multiplied by the reference signal at the receiver.
Carrier Phase Offset: Referring to [11], the carrier phase offset observed at a time can be captured.
In paper [12], the impact of GPS spoofing on UAVs is analyzed using a series of tests in a simulation
environment. The results are presented as a deviation from the original UAV trajectory during flight.
The authors emulated GPS spoofing attacks by changing the readings of the parameters of the GPS
receiver. For each flight, after 45 seconds from the start of the mission, the GPS readings were distorted.
The duration of each attack ranged from 1 second to 20 seconds in 1 second increments.
The attack type is determined based on how the attack affected the GPS parameters and is defined
as follows.
Random Longitude: Changes longitude readings randomly from a valid range of values
(-180 to 180);
Random Latitude: Changes the latitude reading at random from the valid range of values
(-90 to 90);
Random Position: Changes the position reading randomly along three axes (latitude, longitude,
and altitude);
Delayed message: does not change the message information but delivers it with a certain delay;
Force Landing: Changes elevation values higher than the actual value while attempting to force
an unplanned landing. Secondary drone capture distorts the position readings randomly from the
trajectory of another drone along three axes (latitude, longitude, and altitude);
Capture from the Attacker's Position: Changes the position readings at random from a static
position, set by the attacker, along three axes (latitude, longitude, and altitude).
Thus, in the context of changing the parameters that are affected by the attack, this study analyzes
only the position of the UAV and the delay of transmitted messages.
The paper [13] presents the following detection method. The GPS spoofing detection system has
been developed using dynamic identification. A centroid motion model and an orientation motion
model were created. Measurement information from inertial measurement units (IMU) and position
information from GPS are visualized responses to the dynamic laws of motion of a UAV. These two
types of measurement information were used to estimate the dynamic parameters of the UAV online
using two extended Kalman filters (EKF). The detection of GPS false signals has been efficiently
implemented by monitoring the relative errors estimations. Numerical modeling has shown that
dynamics parameters can be estimated online using combined filters. In this work, the following
parameters were investigated: Position error, Speed error, Attitude error, Attitude angle error,
Accelerometer, Gyroscope, GPS positioning.
The method proposed in [14] is based on the use of a support vector machine for data analysis of a
hybrid navigation system. The inertial system (IS) gives an error in free fall, which increases over time.
7
The deviation from the error becomes worse when the UAV is equipped with a microelectromechanical
(MEMS) inertial navigation system. GPS is the fundamental mechanism for calibrating the inertial
system for correct navigation. Evaluating errors over time can help detect spoofing attacks. Indeed,
from the experimental data, the authors found that in normal conditions, the error between the GPS and
inertial navigation system has a certain distribution, and that at the time of the error of attack grows
abnormally. The anomaly disappears over time, because the action of the GPS in the inertial unit hides
the effect of the attack. In this case, the error is determined for the following parameters: position
determined by GPS, speed determined by GPS rotation speed, acceleration position determined by IS,
speed determined by IS.
In paper [15], the authors develop and implement a new intrusion detection and response scheme
that operates on UAVs and ground stations to detect anomalies that threaten network performance. This
paper proposes a set of methods for detecting and responding to anomaly based on tracking UAV
behavior and categorizing them (normal, abnormal, suspicious, and malicious) according to the
cyberattack that was detected. The authors focused on the most dangerous cyberattacks that can target
a UAV network: spreading false information, spoofing GPS, jamming, and attacks using black and gray
holes. Numerous experiments confirm that the proposed scheme is effective for detecting attacks even
with many UAVs and intruders. It demonstrates high detection rate, low false alarm rate and fast
detection with low communication overhead.
A set of rules is proposed for modeling the normal behavior of nodes based on the characteristics of
GPS spoofing attacks [16, 17]:
1) a GPS spoofing attack generates a signal strength intensity (SSI) to gain control of the drone, and
this SSI is higher than satellites, as shown by Shepard et al. [18] and Kim et al [19].
2) an attacker transmits multiple signals from one antenna; therefore, they have almost the same
power level [20].
The detection process is carried out as follows: The detection system collects SSI information from
transmitters (satellites and intruders) and then estimates the distribution of the SSI random variable
using a normal distribution. Moreover, SSIs will be correctly distributed (ie have almost the same value)
if they are within the mean [21]. However, SSIs that are in this range are identified as being generated
by the same transmitter. Therefore, the transmitter is suspected in the implementation of GPS spoofing
attacks. In addition, a reasonable maximum SSI can be set to limit the spurious signal power, since
according to Wen et al. [22] An attacker can increase the signal strength by at least 3 dB.
Thus, we can conclude that the reviewed works focus on the parameters related to UAV positioning
and GPS signal characteristics. Moreover, most of the results were obtained by the authors using
modeling methods, and not on real attacks on UAVs. Of course, conducting a real attack on a UAV can
be dangerous, as it will lead to unforeseen consequences. Nevertheless, to expand the range of indicators
for detecting an attack, it is proposed to investigate the results of an experiment that included a full-
scale model of a UAV for carrying out an attack.
The purpose of this article is to analyze the results of an attack on a UAV and to identify sets of
features that change under the influence of a GPS spoofing attack [7].
3. Experimental stand and attack scenario
To carry out the attack, a UAV was developed, the design of which uses the Pixhawk 4 flight
controller. This flight controller is the most popular and modern solution on the market for flight
controllers for drone-type UAVs [23]. The attack scenario is that the UAV should hover over one fixed
point and keep over it all the time. Attacker use HackRF. This device allows you to create a fake signal
and create false satellites with higher power so that the UAV picks up the signal from them, and not
from the true satellites [24]. In this case, the attacker transmits fake coordinates in order to smoothly
move the UAV to another point and fix it there. The aim of the attacker is to intercept control of the
UAV using a GPS spoofing attack [25]. Thus, the UAV, after determining its current location, receives
the static coordinates of the target. A drone-type UAV moves to a given target and fixes its position in
space while maintaining altitude. In case of external physical impact, for example, the impact of natural
factors or the physical impact of another object, the UAV autopilot system increases engine speed and
sets the opposite direction to maintain a given position. When the UAV is displaced from the set
8
position, the position hold system increases the engine speed depending on the distance between the set
point and the actual location of the UAV. Upon returning to the set point, the operation of the engines
goes into the normal mode of maintaining the altitude.
You can set the direction vector and speed of the attacked device by broadcasting a fake geo position.
Using the fact that the UAV is trying to counteract the displacement, starting to move towards the initial
position, it is possible to set the direction of movement necessary for the attacker. By changing the
distance from the fake geolocation to the specified one, you can increase or decrease the speed of
movement, for a more accurate direction and control of the attacked UAV.
4. Analysis of the UAV logbook after the attack
Figure 1 shows the readings of the drone's flight altitude during the attack and normal flight,
respectively. Comparing the two upper graphs, we can say that during an attack, the altitude readings
obtained from the GPS system change slightly, no global changes are observed, during normal flight
the altitude remains practically unchanged.
(a)
(b)
(c)
Figure 1: UAV altitude graph (a) during normal flight (b) with a short-term attack (c) with a long-term
attack term attack (c) with a long-term attack
9
Let us analyze the changes in the UAV flight altitude indicator. Figure 1 shows that during a short-
term attack, as shown in Figure 1 (b), the flight altitude changed slightly within the range from 15 to 20
meters, only at the end we see a small sharp takeoff of the UAV to an altitude of more than 20 meters.
At the same time, it can be seen from Figure 1 (c) that there is a significant change in the flight altitude
readings obtained from the GPS sensor (red line in the graphs), while the accelerometer readings (blue
line in the graphs) remain stable. This suggests that, in fact, the quadcopter could not change the flight
altitude, the attacker simply could not guess the exact flight altitude and therefore the graph has peak
values. Thus, the altitude indicator may be susceptible to attack, or it may not, it depends on the
preparedness of the attacker. Moreover, this indicator changes only in relation to the measurements
made by the GPS sensor, the readings of the accelerometer are stable. As can be seen from graph 1 (a),
during normal flight, when there is no attack, if the flight altitude changes, then both sensors and the
accelerometer and the GPS receiver will show the same values.
Let us analyze the indicator - the number of GPS satellites detected by the UAV. The GPS readings
during normal flight make it clear that the GPS system is working stably, there is no sharp loss of GPS
fixation and the satellites used, there is also no abrupt change in the UAV positioning, which is typical
during an attack, as can be seen from Figure 2.
(a)
(b)
(c)
Figure 2: The number of GPS satellites used (a) during normal flight (b) during a short-term attack on
the UAV's GPS system (c) during a prolonged attack
10
(a)
(b)
(c)
Figure 3: Actual and calculated UAV flight paths (a) during normal flight (b) during a short attack (b)
during a prolonged attack
Figure 3 (a) shows a complete coincidence of the flight path obtained from the accelerometer and
from the GPS receiver. In a short-term attack on the GPS, the goal was to move the UAV from the
starting point to fix it at the point indicated by the attacker. Figure 3 (b) shows that the UAV was
displaced, as evidenced by the shift of the red line to the lower left corner (initially, the UAV was in
the upper right corner). But the trajectories of the red and blue lines do not coincide. On closer
inspection, we can see that the red line repeats the blue pattern only with a smaller radius. Nevertheless,
it is clearly seen that the UAV positioning system is in an unstable state and shows different coordinates
11
by GPS and from the inertial system. In Figure 3 (c), the UAV should be fixed in the lower left corner.
The attacker tried to move it first to the upper left corner and then to the lower right corner. At the same
time, the picture seems confusing at first glance. In fact, the attacker is trying to smoothly move the
UAV by picking up the coordinates and gradually moving the UAV. At the same time, by the end of
the attack, the UAV was displaced, which is confirmed by the coincidence of the red and blue lines in
the lower right corner of the figure. Thus, an attack can be detected by a mismatch in the coordinates
recorded by the GPS sensor and the accelerometer, and other sensors.
(a)
(b)
(c)
Figure 4: Graph of GPS noise readings (a) during normal flight (b) with a short-term attack (c) with a
long-term attack
The norm for GPS noise is 80, in this case, as can be seen in Figure 4 (a), during normal flight the
noise is slightly higher than the norm, this is not a critical reading, it may be due to the terrain on which
the flight was carried out, as well as weather conditions. For example, during the period of this
experiment, strong gusts of wind were observed. One of the indicators that will allow detecting an
attack with high accuracy is the GPS noise level. This is because despite the possibility of falsifying the
exact number of satellites, calculating the flight altitude, smoothly displacing the UAV from a given
trajectory, thereby, having a minimal effect on the previous parameters, an attacker will not be able to
fake the GPS noise level parameter. This is because the main feature of the attack is the generation of
a stronger GPS signal so that the true GPS signal from the satellites is blocked by the attacker's signal.
Figure 4 shows the results of changes in GPS noise. However, it should be noted that the graph of the
noise level during normal flight at the same level throughout the flight is the same, there are minor
12
changes. For a situation where an attack is carried out, everything is obvious. From graph 4 b, c it
becomes clear that up to a certain period a normal flight took place, after the attack, the graph clearly
shows the consequences - a sharp and large deviation from the norm of the GPS noise level. Also, the
green line makes it clear that there is a sharp jamming of the GPS signal. Figures 4 (b), (c) show that
there is a sharp increase in the noise level to 200, then this value does not stay at the same level but
changes the readings all the time.
Consider two parameters by changing which one can indirectly judge the presence of an attack.
Before that it was considered parameters directly connected with the coordinates of the UAV or drone
navigation system, but the attack may affect other parameters of the UAV. Let us analyze how the CPU
utilization parameter changed. Figure 5 shows the results of changing the CPU, both during normal
flight and during an attack.
(а)
(b)
(c)
Figure 5: Graph of readings of changes in the CPU load level (a) during normal flight (b) during a short-
term attack (c) during a long-term attack
As can be seen from the graphs, when there is an attack, CPU load peaks is starting to be more. The
more intense the attack, the more the CPU utilization changes, as can be seen from Figure 5 (c) the
peaks of the utilization level during an intense attack are much larger. This is because the UAV is
constantly trying to move to the point indicated by the attacker, while resistance arises from inertial
systems that try to keep the UAV at the point indicated by the operator. The UAV performs many
additional movements.
13
Next, consider the power consumption level changes in different flight conditions, in Figure 6.
(а)
(b)
(c)
Figure 6: Graph of readings of changes in the level of consumed power (a) during normal flight (b) with
a short-term attack (c) with a long-term attack
Figure 6 shows that the power consumption parameter underwent the greatest changes during an
intense attack. As with the CPU changes, this involves multiple movements of the UAV. For example,
an attacker may aim to disable a UAV by exhausting its resources; the more UAVs moves, the faster
its battery will be discharged. Given the limited resources of the UAV, this can become a significant
problem.
5. Conclusion
Typical signs of an attack on the GPS system are most often recognized by a sharp decrease in the
number of satellites used by the system and the difference in readings between the actual flight path
and the trajectory built by the GPS system, so an attack can be identified by a sharp increase in GPS
noise and signal jamming, and by changing the altitude readings of the UAV [26]. Despite the large
number of methods for countering attacks aimed at spoofing navigation signals, this topic is still
relevant. Today, there are cases of successful implementation of attacks on the navigation system of
14
UAVs [27]. Based on the results of an experimental study and analysis of the on-board log of the UAV
that was attacked and that was received during normal flight, the following was determined. When
conducting an attack, such parameters as: flight altitude, GPS noise level, the number of GPS satellites
recorded, the level of power consumption by the battery and the level of CPU utilization. From Figure
3, where the UAV's flight path is shown, it abruptly changed its flight path and moved unevenly.
Initially, the UAV was supposed to stay at one point, and in Figure 3, where the flight path is presented,
there is a point and around it there are small changes in the blue line, but then the trajectory changes
sharply. All other changes in the parameters of the UAV are related to this, which just indicated the
presence of an attack. In general, we can say that such changes in the flight trajectory could indicate a
change in the behavior or scenario of the UAV's behavior. But because a high level of noise and jumps
in the number of satellites were recorded, we can say that this is an attack. Correlation of the analyzed
parameters can unambiguously reveal the attack and determine its type. Each attack affects a certain
number of subsystems, you can set which parameters which attack affects to determine its type [28].
The collected data in the form of time series can be used in the future to train a neural network, which
can be trained on these sets and will help decide about the presence of an attack. In addition, it should
be noted that the addition of new parameters for analyzing the presence of an attack may allow detecting
new threats to the security of the UAV. For example, when the power consumption of the battery
increases, there is a threat of exhausting the resources of the UAV. When the CPU load increases, the
UAV mission is threatened, because computing power is spent not on calculations according to the
algorithm, but on a constant change in the flight route.
6. Acknowledgements
Research related to UAV attack experiments and analysis of experimental data, as well as an attack
scenario supported by the Russian Science Foundation grant number 21-79-00194,
https://rscf.ru/project/21-79-00194/ in Southern Federal University. UAV design and normal behavior
is supported in the context of the collaboration between Sapienza, NCFU and SFU.
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