Revista de Ciencias Tecnológicas (RECIT). Volumen 3 (1): 10-22

Revista de Ciencias Tecnológicas (RECIT). Universidad Autónoma de Baja California ISSN 2594-1925

Volumen 9 (2): e444. Abril-Junio, 2026. https://doi.org/10.37636/recit.v9n2e444

ISSN: 2594-1925

Review article

Wireless attacks against commercial drones: a systematic

mapping study

Ataques inalámbricos contra drones comerciales: un estudio de

mapeo sistemático

Brayan Pajuelo -Martin1, William-Rogelio Marchand-Niño2

1 Universidad de Buenos Aires. Viamonte 430, Ciudad Autónoma de Buenos Aires, Argentina

2Universidad Nacional Agraria de la Selva. Carretera Central km. 1.21; Tingo María, Huánuco, Perú

Corresponding author: William-Rogelio Marchand-Niño. Universidad Nacional Agraria de la Selva.

william.marchand@unas.edu.pe. ORCID: 0000-0003-2650-4226.

Received: March 1st, 2026 Accepted: June 4th, 2026 Published: June 12, 2026

Abstract. - This study compiles wireless cyberattacks carried out experimentally against drones. It focuses on three

areas of analysis: first, the types of attacks; second, their vulnerabilities; and finally, the proposed mitigation

measures. The scope is limited to studies conducted between 2020 and 2024. Following the methodology, a search

query was applied to the Scopus and IEEE Xplore databases. An initial search yielded 6,860 articles, and after

applying our inclusion and exclusion criteria, 28 articles were selected, which guided the entire analysis of this

research. The most common attacks found were spoofing and jamming. These attacks exploit the reliance on the

Global Navigation Satellite System (GNSS) and, furthermore, the lack of a robust authentication system. Among

the vulnerabilities identified are the absence of an intrusion detection system (IDS) as well as weaknesses in

encryption. The literature proposed mitigation measures such as the use of anti-interference techniques (DSSS and

FHSS), cross-validation of signals, IDS, and strong authentication. It was concluded that strict regulatory policies

are needed to improve cybersecurity in drones. These types of attacks must also be validated on high-end drones.

Keywords: Wireless attacks; Drones; Vulnerabilities; Mitigation; Cybersecurity; Systematic mapping.

Resumen. – El presente trabajo recopila los ciberataques inalámbricos ejecutados de forma experimental en

contra de drones. Se centró en tres puntos del análisis, comenzando por los tipos de ataque, seguido de sus

vulnerabilidades y finalizando con las medidas de mitigación propuestas. Delimitándose a estudios que van del

2020 al 2024. Siguiendo la metodología se aplicó la cadena de búsqueda en las bases de datos de Scopus y IEEE

Xplore. En un primer barrido se obtuvieron 6,860 artículos, y después de aplicar nuestros criterios de inclusión y

exclusión se seleccionaron 28 artículos, que guiaron todo el análisis de la presente investigación. Los ataques más

comunes que se encontraron fueron el spoofing y jamming. Estos ataques aprovechan la dependencia de la Señal

Global de Navegación por Satélite (GNSS), y además, la falta de un sistema de autenticación robusto. Dentro de

las vulnerabilidades encontradas está la ausencia de un sistema de detección de intrusiones (IDS) como también

debilidades en la encriptación. En la literatura se propusieron medidas de mitigación como el uso de técnicas de

anti-interferencias (DSSS y FHSS), validación cruzada de señales, IDS, y contar con autenticación fuerte. Se

concluyó que hace falta utilizar políticas de regulación estrictas que mejoren la ciberseguridad en drones. También

se deben validar estos tipos de ataques en drones de alta gama.

Palabras clave: Ataques inalámbricos; Drones; Vulnerabilidades; Mitigación; Ciberseguridad; Mapeo sistemático.

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1. Introduction

The growing adoption of commercial drones across various industries including surveillance,

transportation, aerial photography, and others has expanded the scope of risks in airspace [1, 2]. The main

issue addressed in this study is the rise in wireless attacks against drones, as they have become a product

relied upon by multiple industries, used for work, recreation, research, or even in warfare. Some studies

have found that the specific characteristics of different models of unmanned aerial vehicles (UAVs) have

introduced new threats. Among these are autonomous functionalities, which may contain vulnerabilities

in communication channels. This can allow vulnerabilities to be exploited through wireless attacks such

as GPS (Global Positioning System) spoofing, jamming, injection of false data, and attacks on data

integrity [3].

The widespread adoption of drones across multiple sectors increases the level of risk they pose; for

example, they can compromise drone operations as well as data privacy. Therefore, as their use increases,

safeguards must also be strengthened to mitigate the risks associated with the adoption of UAVs [4].

One innovative aspect that cuts across all sectors is Artificial Intelligence (AI), which is emerging as a

promising option for protecting UAVs. This technology focuses on the drone system and its control, as

well as on managing communications and analyzing the data it contains. It is becoming a solution worth

considering for protecting drones. However, its real-world application may face some limitations, as it is

not yet a mature solution [5]. Furthermore, if we take it a step further and integrate the use of AI with

technologies such as blockchain, which operate on the basis of decentralized architectures, this will

reinforce mitigation against these types of attacks through multiple layers. However, even with all this,

limitations remain, such as the management of available energy resources. These are among the main

vulnerabilities in the security systems of current drones [6].

On the other hand, it is well known that we are seeing a growing trend in the use of the Internet of Things

(IoT). This growth increases our cybersecurity risks. This is because, when we look at it specifically

particularly when working with the Internet of Drones (IoD) we encounter a critical issue, such as

communication authentication. Data privacy is a key issue when we want to ensure confidentiality; data

is compromised when exposed to jamming, GPS spoofing, and data injection. Furthermore, the industry

is exploring emerging technologies applied to drone cybersecurity, such as Q-learning and blockchain.

These aim to provide new protection alternatives, but their implementation in a real-world environment

presents various challenges [4, 7, 8].

This research is based on a systematic review, limited in scope to common attacks, their vulnerabilities,

and the mitigation strategies proposed in the literature. The objective is to provide an overview of the

current state of drone security and to offer readers guidance for future research. Moving on to the structure

of the study. Section 2 describes our methodology, focusing on a systematic mapping that includes the

application of search strategies and selection criteria. Section 3 presents our synthesis of the findings.

These detail the results and answers to the research questions. Among these are the types of attacks

identified, the vulnerabilities exploited, and the mitigation measures proposed in the literature. Section 4

analyzes the results to identify key findings. Finally, Section 5 presents the conclusions and suggests topics

for future research.

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2. Methodology

This research follows the methodological guidelines for systematic mapping and focuses on the field of

computer science [9]. The research follows a structured process consisting of four stages: protocol

development, technical implementation, systematic analysis, and formulation of answers to the research

questions. In the first phase, which guides the entire study, the research questions were determined, aiming

to identify the most common types of wireless attacks, the vulnerabilities they exploit, and the mitigation

strategies proposed in the literature. Also in this phase, the search strategy was defined, including the

selection of databases to consult, the keywords in the search string, the time frame, and the consolidation

of inclusion and exclusion criteria, which will be filtered step by step.

The next phase, implementation, began with the extraction of primary research studies using the search

string defined in the previous phase. A study screening process was conducted using progressive filtering.

First, titles and abstracts were reviewed. Subsequently, the full texts were analyzed, applying the inclusion

and exclusion criteria, to identify primary research containing information relevant to addressing the

research questions posed. Finally, in the final phase, the collected information was organized under a

specific classification scheme (types of attacks, vulnerabilities, and mitigation measures), thereby

enabling us to answer the research questions that guided this study:

• RQ1: What are the most common types of attacks against drones?

• RQ2: What vulnerabilities are exploited in these attacks?

• RQ3: What mitigation measures have been proposed in the literature?

The search string established is as follows:

("wireless attacks" OR "wireless intrusion" OR "wireless cyber attack " OR "wireless threat" OR "wireless

vulnerability" OR "wireless security" OR "wireless hacking" OR "wireless interception" OR "radio

frequency attacks" OR "rf attack" OR jamming OR spoofing OR " gps spoofing" OR "data injection" OR

deauthentication ) AND (drones OR "unmanned aerial vehicles" OR uav OR "autonomous vehicles" OR

"unmanned aerial systems" OR uas OR "unmanned aircraft")

To ensure the quality of the selected studies, the search was conducted in the Scopus and IEEE Xplore

databases. These databases were chosen due to their leading position in technologies such as UAVs,

wireless communications, and cybersecurity. Both Scopus and IEEE Xplore are top-tier sources for

indexing peer-reviewed research. This choice guarantees that the studies selected through the inclusion

and exclusion criteria are of the highest quality for this type of research.

The study period for published research is limited to 2020–2024. This five-year period reflects the growth

in the use of drones for civilian applications during that time. This growth has led to an increase in attack

vectors and vulnerabilities in recent years. We believe that mitigation strategies documented before this

period may lack the necessary effectiveness to neutralize current emerging threats. Finally, it is worth

noting that the data collection and literature review phase was carried out in January 2025.

The inclusion and exclusion criteria are detailed in Table 1.

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Table 1. Inclusion and exclusion criteria.

Type

Description

CI1

Language

Study published in English.

CI2

Temporality

The document was published between 2020 and 2024 to ensure that

information on wireless attacks on commercial drones is relevant and up to

date, as new vulnerabilities and attack methods have emerged during this

period.

CI3

Type of study

Primary research published in academic journals or conference proceedings

related to cybersecurity, drone vulnerabilities, and wireless attacks.

CI4

Relevance

Document on wireless attacks on commercial drones or security

vulnerabilities in drone systems. This includes attacks such as jamming,

spoofing, data injection, radio frequency (RF) attacks, among others.

CE1

Language

Document that is not written in English

CE2

Temporality

Document outside the selected year range (2020–2024)

CE3

Duplicity

Duplicate documents

CE4

Type of study

Document that is not primary or published in journals or conferences

(reviews, books, book chapters, and letters to the editor)

CE5

Relevance

Document that does not cover at least one of the research questions.

A total of 6,860 articles were retrieved during the search. After applying the inclusion and exclusion

criteria, 28 articles were selected for analysis and responses to the research questions. The details of this

process are summarized in Figure 1.

Figure 1. Diagram of the document selection process.

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3. Results

3.1 Answering research questions

3.1.1 RQ1: What are the most common types of attacks against drones?

The nine wireless attacks found in the primary research literature were categorized. These attacks focused

on commercial drones. The most frequently identified attack types in the literature were spoofing and

jamming. However, in addition to identifying the frequency of each attack type, it is relevant to analyze

the conditions necessary for a wireless cyberattack to be successful. Another key point is the sophistication

that cyber attackers develop to achieve their objective, as this allows us to understand the problem and, in

turn, provide mitigation solutions.

Table 2 summarizes the types of wireless attacks and their frequency.

Table 2. Frequency of wireless attack types identified in the selected studies.

Attack Type

Number of Studies

Percentage (%)

Spoofing

53.57%

Jamming

42.86%

Deauthentication

14.29%

Hijacking

10.71%

False Data Injection

3.57%

Man-in-the-Middle

3.57%

Replay Attacks

3.57%

IMU Spoofing

3.57%

The study identified a frequency of 16 investigations of the spoofing attack type. This is due to our reliance

on GNSS signals without any validation to ensure the authenticity of the received data [10–24]. One of

the inclusion criteria was that the studies had to be primary and that the demonstrated attacks had to have

been carried out in simulated environments and field tests. The analyzed studies used software-defined

radios (SDRs), such as the HackRF-One or the BladeRF X40. The results showed that drones can be

diverted to unauthorized areas, such as airports [20], or even crashed due to altitude manipulation [12].

Although some high-end models, such as the DJI Mavic 2 Pro, have demonstrated resistance to

asynchronous spoofing, most lack effective defenses.

Jamming attacks, which were mentioned in 12 studies, directly affect the control, navigation (GPS), video,

and telemetry channels [17, 25–31]. The use of jamming has been particularly problematic in scenarios

such as the protection of critical infrastructure. It has been shown that drones can be completely disabled

by frequency sweeps (sweep jamming) or high-power microwave pulses (EMP) [10]. Effective attacks

were also reported at distances of up to 150 m and heights of 350 m [29].

Wi-Fi deauthentication attacks have been particularly effective against low-end commercial drones using

insecure IEEE 802.11 networks [32–35]. These attacks allow a drone to be disconnected from its

legitimate controller, thereby facilitating hijacking attacks or access to private data [34]. They have been

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successfully executed with available tools such as aircrack-ng, ESP8266 (NodeMCU), Raspberry Pi, and

Wi-Fi Pineapple, enabling them to make accessible threats even for non-specialized attackers [33].

However, these attacks may not be effective for sophisticated or updated drones.

Command hijacking relies on exploiting unauthenticated control protocols [24, 25, 32]. For example, it

has been demonstrated that command injections can take control of the Crazyflie drone, even causing it

to crash in standalone mode [25].

In emerging but infrequent categories, sophisticated attacks were identified. These include false data

injection (FDI), which involves the stealthy manipulation of feedback/control channels in DJI Tello drones

through state estimation using Kalman filters [36]. Other attacks include man-in-the-middle (MitM),

which enables access to Wi-Fi packets using tools such as Wireshark and command replay [18]. In

addition, it was found that IMU (Inertial Measurement Unit) spoofing uses neural networks to generate

fake inertial data, allowing these types of attacks to be carried out without triggering alarms [22]. Replay

attacks were also identified; this type of attack allows RF signals to be reused in order to perform

unauthorized actions on the drone [37].

All of this creates a need to develop robust security measures, authentication standards, and encryption

protocols for UAVs. Approximately 89.3% of the analyzed studies were conducted in real-world

environments. The remaining 10.7% combined simulation environments with field testing. The tools used

in these scenarios were Gazebo or PX4 SITL.

3.1.2 RQ2: What vulnerabilities are exploited in these attacks?

Eight vulnerabilities in commercial drones were identified. These vulnerabilities have been exploited in

real-world experimental environments, controlled laboratories, and hybrid scenarios, impacting sectors

such as critical infrastructure, urban surveillance, education, and entertainment. A key point is that all

reviewed studies included experimental validation, ensuring high-quality research. The documented

attacks were carried out using real hardware from brands such as DJI, Tello, Holy Stone, SkyViper, and

Crazyflie.

Table 3 summarizes the identified vulnerabilities along with their frequency.

Table 3. Frequency of vulnerabilities exploited in wireless attacks against drones

Vulnerability

Number of Studies

Percentage (%)

Lack of intrusion detection (IDS)

100.00%

Weak authentication mechanisms

78.57%

Lack of strong encryption

71.43%

Overreliance on external data

67.86%

Vulnerable Wi-Fi protocols

57.14%

Reliance on insecure GNSS signals

50.00%

Electromagnetic interference exposure

42.86%

MAC address exposure

17.86%

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• Reliance on unreliable GPS/GNSS signals. Many drones rely solely on open GNSS signals,

which are not authenticated. This vulnerability makes them susceptible to spoofing and jamming,

which can alter their flight path or even force them to land. For example, the DJI Phantom 4

model has been compromised, despite being one of the most widely sold models [10, 20].

• Excessive reliance on external data. This is another vulnerability identified in the studies

reviewed, stemming from the acceptance of data from GNSS sensors, IMUs, or Wi-Fi links

without cross-validation. This allows tampered data to influence drone control. This vulnerability

is exploited in spoofing and command injection attacks; for example, the drone compromised in

this type of attack was the DJI Tello EDU model [36].

• Lack of intrusion detection. For this vulnerability, the drones lacked a monitoring system. Since

constant monitoring enables the detection of malicious activities, this allows replay attacks or

hijackings to be carried out undetected [22, 37]. This vulnerability was identified in all the studies

reviewed and is considered a critical risk factor.

• Weak authentication mechanisms. This vulnerability stems from the lack of strong authentication

in the control and navigation channel. Without a robust authentication system, the system is

susceptible to deauthentication or hijacking attacks on drones. An attacker with basic knowledge

of aircrack-ng can compromise the communication between the drone and its remote control.

Studies highlight the need to implement identity checks or bidirectional authentication [18, 32].

• Wi-Fi Protocols. The vulnerability arises because the Wi-Fi protocol, specifically 802.11b/g/n,

is still used for communication between the drone and its remote control. Since the drone relies

on Wi-Fi, cyber attackers can execute attacks using common wireless cards without needing

specialized hardware. This vulnerability results in a disconnection, which is critical for UAVs.

Several studies have shown that drones such as the DJI Mavic Air and DJI Tello are vulnerable

to these attacks. However, other models, such as the Yuneec H520E, have also been shown to be

resistant to these types of attacks, as they incorporate MAC address hiding and frequency hopping

[32, 34, 35].

• Insufficient or absent encryption protocols. This vulnerability was identified in the

communication channel between the drone and its remote control. Since many drones transmit

their data using outdated algorithms or even in plain text, this poses a critical threat to data

confidentiality, allowing an attacker to intercept and modify information. That is why the lack of

robust protocols such as TLS or WPA3 compromises drone security. For example, the use of

Wireshark has made it possible to intercept commands or multimedia files [34].

• Exposure of the MAC address. Exposing the MAC address allows an attacker to carry out a

targeted attack, since there are multiple devices on a wireless network. This is why, in the initial

phase of an attack, the attacker seeks to identify the MAC address of the target they wish to

attack, in order to subsequently carry out attacks such as deauthentication or control hijacking.

This is in contrast to using proprietary links such as OcuSync or Lightbridge, which are more

difficult to detect [32, 35].

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• Intense electromagnetic interference. This vulnerability stems from a lack of protection against

high-energy disturbances. This type of attack can result in a complete loss of control, navigation

failure, or structural damage to the drone. The studies analyzed have documented that spectral

noise and electromagnetic pulses (EMP) affect devices such as the DJI Phantom 4 Pro and the

Mavic Air, due to their high power and electromagnetic intensity [26, 29].

3.1.3 RQ3: What mitigation measures have been proposed in the literature?

It was noted that not all of the studies analyzed proposed mitigation measures for the attacks. However,

some studies not only demonstrated how the attacks were carried out but also proposed defensive

strategies to strengthen drone security against threats such as jamming, spoofing, hijacking, and

deauthentication.

Table 4 summarizes the identified mitigation measures, along with their frequency of occurrence.

Table 4. Mitigation measures against wireless attacks on drones.

Mitigation Measure

Number of Studies

Percentage (%)

Anti-jamming techniques

10.71%

Strong authentication mechanisms

21.43%

Data encryption (communication/storage)

17.86%

Safe mode activation

3.57%

Anti-replay mechanisms

3.57%

IDS / Firewall systems

10.71%

Robust communication protocols

14.29%

GNSS cross-validation

7.14%

Regulatory policies

3.57%

Anomaly detection (AI/Kalman)

10.71%

• Anti-jamming techniques. This mitigation measure was proposed in three studies, which

include the use of techniques such as FHSS, DSSS, and MIMO (Multiple-Input Multiple-

Output) as defenses against electromagnetic interference [25, 29, 30].

These techniques aim to prevent communication from being interrupted. This is because they allow for

automatic channel hopping when a channel is compromised. Alternatively, the electromagnetic spectrum

can be diversified. The Crazyflie 2.1 drone was used as an example, and it was observed that Gaussian

noise interference attacks [25] cause the drone to crash. This is because this model does not include a

return-to-home function when exposed to these types of attacks that cut off communication.

This led to the proposal of DSSS and FHSS, particularly for these drone models. However, for low-cost

drones, this leads to limitations due to their inexpensive hardware architecture. Another significant

limitation is ensuring stable power consumption when attempting to implement these defenses in a real-

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world environment. For example, a study on the DJI Mavic Air demonstrated that Wi-Fi-only

communications are vulnerable. Meanwhile, drones using FHSS or encrypted links exhibit greater

resilience [29].

 Strong authentication in communication and sensors. A lack of authentication has been identified

as the leading cause of successful attacks. The Crazyflie CRTP protocol allows unverified

command injections. The incorporation of cryptographic authentication along with safe modes

against loss of control has been proposed [25]. Drones, such as the DJI Tello EDU and Mavic Air,

which operate over Wi–Fi without authentication, are vulnerable to deauthentication and

takeovers. The implementation of WPA2-Enterprise, IEEE 802.11w, 802.1X, and management-

frame protection (MFP) has been recommended. It has been suggested that GPS signals be

validated during navigation through cross-authentication or session tokens to avoid spoofing [18,

20, 25, 32, 34, 37].

 Strong encryption of data in transit and storage. Five studies have shown that many drones

transmit commands and data in plain text, allowing for spoofing and replay attacks. For example,

DJI Tello EDU accepts critical commands without validation. End-to-end encryption was

proposed for commands and telemetry. In addition, storage flaws have been detected, such as in

Mavic Air, which allows access to photos and videos without credentials following a

deauthentication attack. Mobile traffic redirection using fake stations has also been reported.

Standards such as AES, TLS, and WPA3 are recommended to protect both communication and

persistent data [18, 20, 32, 34, 37].

 Activation of safe modes in case of communication loss. The Crazyflie 2.1 drone can experience

this type of failure when in autonomous deployment mode. A "safe mode" is proposed that includes

various safety features such as emergency landing, return to home (RTH), and transmission of

GPS coordinates. Safe mode is a mitigation measure because it does not require complex

mechanisms. This mode is important for drone safety, since a loss of communication in UAVs can

cause the drone to free fall [25].

 Anti-replay mechanisms. This type of attack, found in the literature, is simply based on capturing

and then illegitimately retransmitting signals. For an attacker to set up the attack scenario, they

need a System-on-a-Drone (SDR). One of the most widely used SDRs is HackRF One. This tool

records the communication flow from a remote control to a drone, allowing the execution of

commands such as takeoff and shutdown without breaking the communication encryption. The

attack relies on storing instructions for later replication. For example, the 4DRC-4DV2 drone

lacked a session validation mechanism. As a mechanism to counter replay attacks, the literature

proposed the development of dynamic timestamps, unique nonces per session, and context

validation to validate the authenticity of messages. This mitigation measure could block replay

attacks without robust encryption, thus promoting cybersecurity in low-cost UAVs. [37].

 Intrusion detection systems or firewalls. Three studies proposed the development of IDSs to detect

attacks, such as jamming, spoofing, or hijacking. Although complete systems have not yet been

implemented, their importance has been recognized. IDSs are suggested for drones or control

stations to identify malicious traffic, although warnings are provided regarding the power

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consumption and delays that an IDS can introduce [25]. Defending against deauthentication attacks

[32] and monitoring of deficiencies that enable the execution of malicious commands [18] have

been emphasized.

 More Robust Communication Protocols. Several studies recommend adopting modern protocols,

such as Wi-Fi 802.11 ac/ax, encrypted FHSS, and IEEE 802.11w, to mitigate wireless attacks.

Drones using standard Wi-Fi (802.11b/g/n) are vulnerable to deauthentication, spoofing, and

interference. The Yuneec H520E exhibited greater resilience owing to MAC hiding and dual-

frequency operations. They proposed migrating to more secure protocols and proprietary links

using better encryption algorithms. The use of FHSS and dynamic MAC hiding significantly

reduced the attack surface[29, 30, 32, 34].

 Cross-validation of GNSS signals. The exclusive reliance on open GNSS signals, such as GPS L1,

represents a critical vulnerability to spoofing. In light of these vulnerabilities, a proposed

mitigation measure involves validating GPS coordinates by cross-referencing them with the

location of the mobile base station connected via SIM. This cross-validation serves as a double-

check, as it detects inconsistencies in the received data. For example, when an attacker sends

signals with false GPS coordinates in an attempt to trick the drone into moving to a false location.

However, performing the second validation using cellular geolocation detects the attempted GPS

spoofing attack. Additionally, among the mitigation measures proposed by these studies is

combining GNSS with auxiliary sources such as IMUs, altimeters, and geofences. This combined

architecture would improve security without requiring complex hardware [14, 24].

 Stricter regulatory policies. In addition to the hardware and software solutions proposed by the

studies, cybersecurity regulations or standards for UAVs are also necessary. In this regard, some

laws have already increased oversight and proposed penalties for negligence. This is because the

use of unencrypted navigation systems, weak protocols, and the lack of regulations in each country

constitute a structural vulnerability. Even with the best technology to provide UVA protection, if

rules aren't defined from the outset as a regulatory framework for drone construction, the drone

industry, in order to maximize sales, may relegate safety to a secondary or even ignored priority

[24].

 Anomaly validators. For this mitigation measure, we are focusing on the sensors on the drones. An

anomaly validation system will detect inconsistencies in the data collected from the sensors. In

some cases, AI navigation, Kalman filters, or even cross-validation of information between IMU

and GPS data could also be applied. Among the studies analyzed, the use of the IMU to capture

the drone's actual state when anomalous conditions arise has been proposed [25].

Among these proposed mitigation measures is the use of neural networks to provide automatic alerts [16].

Furthermore, sensor fusion and extended Kalman filters (EKF) limit attackers' ability to predict behavior,

improving spoofing detection without requiring excessive device resources [19].

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4. Discussion

In the previous results phase, it was determined that spoofing and jamming are the most common

documented wireless attacks. These types of attacks are consistent with the findings of Sarıkaya [3] and

Mahalle [8], who, during their research, identified them as potential threats to drone security. The spoofing

attack, found in 15 studies that demonstrate its execution, highlights a latent vulnerability. This

vulnerability involves attacking a communication channel, specifically the navigation channel, which

relies on unauthenticated GNSS signals. An attacker can exploit this vulnerability to manipulate drone

instructions.

Jamming, mentioned in 12 of the selected documents, disrupts key communications such as control and

navigation by jamming critical frequencies. Although techniques such as FHSS and DSSS provide

defense, their application in low-cost drones suffers from technical and economic barriers that require

further research to provide viable solutions.

Attacks such as deauthentication of Wi-Fi and hijacking reveal vulnerabilities in standard protocols (IEEE

802.11b/g/n), particularly in low-cost drones. The use of tools such as aircrack-ng and NodeMCU enables

their execution, thereby requiring regulations and standards to mitigate these risks.

However, this study identified common vulnerabilities in commercial drones, confirming the persistence

of these deficiencies over time, as reported in similar studies. However, it is also evident that few studies

have yet explored alternative solutions such as signal cross-validation.

It is noteworthy that some additional vulnerabilities were identified, such as the exposure of MAC

addresses in basic and educational models and high susceptibility to electromagnetic interference, which

particularly affects low-end drones. Other elements identified in this systematic mapping study are the use

of machine and deep learning techniques for attacks, detections, and replay attacks, which should be

considered for experimental research to provide effective solutions.

5. Conclusions

The predominant attack types identified were spoofing and jamming, mainly because of the widespread

reliance on unauthenticated GNSS signals and the technical ease of executing interference on essential

communication channels using accessible platforms, such as SDRs. Other attacks, such as

deauthentication and Wi-Fi hijacking, were highly effective against commercial drones using IEEE 802.11

protocols. This demonstrates that low-cost models require much more robust wireless configurations.

Critical vulnerabilities have been identified with increasing frequency, such as the widespread absence of

IDS, as well as poor implementation of authentication and encryption. Furthermore, studies have

identified a tendency to blindly trust data received via GPS signals. This vulnerability allows cyber

attackers to exploit various attacks, disrupting the proper functioning of UAVs.

Regarding mitigation measures proposed in the literature, some studies have addressed strategies to

counter the growing threat of attacks, aiming to create a system resilient to constant attacks. These include

anti-interference techniques that create protective barriers, such as FHSS, DSSS and MIMO, which

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minimize the impact of wireless attacks. The objective of these mitigation measures is to protect

communications between the remote controller and the drone. As with other technologies, authentication

and encryption play a crucial role in securing a system. Other important considerations include

incorporating anti-replay systems, using autonomous systems with their own security layer, and

employing timestamps and nonces. Another key point is the adoption of modern protocols for deployment

across communication channels. Furthermore, incorporating diverse information sources when receiving

instructions is essential; for example, relying not only on a navigation source but also implementing GNSS

cross-validation.

No system is completely secure, but the goal is to mitigate any existing risks. Beyond the technical aspects,

we must also consider the regulatory environment. Cybersecurity regulations for drones must have at least

minimum permissible requirements to help strengthen security from the outset. By combining all aspects

that contribute to improved security, such as integrating legislation with the latest technological trends in

drone cybersecurity, we can build a safe drone industry for society.

The databases used for this study were Scopus and IEEE Xplore, accessed between 2020 and 2024. Due

to this limitation of scope, other databases were excluded. Furthermore, the inclusion criterion for this

research focused solely on primary studies with experimental validation. The research employs a

systematic mapping methodology; experimental validation was not performed, but rather the analysis

focused on existing research and, in particular, the mitigation measures proposed in the literature to help

researchers and manufacturers improve UAV safety.

The main contribution of this study is to understand the current state of attacks, vulnerabilities, and

proposed mitigation measures against commercial drones. It provides an up-to-date overview by

highlighting existing vulnerabilities and outlining emerging trends. The aim is to offer a framework for

researchers, manufacturers, and all those working with drones. Future research can begin by applying

experimental studies using the attacks described in this study, moving from theory to a real-world

environment with practical tests of wireless attacks against some high-end drone models. Finally, it is

essential to integrate tools such as AI and implement multi-layered cybersecurity to ensure that UAVs are

resilient to zero-day wireless threats.

6. Acknowledgment

The authors would like to thank the Postgraduate Program of the School of Business and Public

Administration – Economics of the University of Buenos Aires for partial funding of the specialization

project in computer security.

7. Authorship acknowledgment

Brayan Pajuelo-Martin. Conceptualization, methodology, research, formal analysis, writing – revision

and editing. William-Rogelio Marchand-Niño. Methodology, validation, writing – original draft, writing

– revision and editing, supervision.

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