# Fast, Reliable, and Secure Drone Communication: A Comprehensive Survey

Vikas Hassija, Vinay Chamola, Senior Member, IEEE, Adhar Agrawal, Adit Goyal,
Nguyen Cong Luong, Dusit Niyato, Fellow, IEEE, F. Richard Yu, Fellow, IEEE,
and Mohsen Guizani, Fellow, IEEE Manuscript received July 11, 2020, revised
December 25th, 2020 and April 26th, 2021 Accepted XXXX (IEEE Communications
Surveys and Tutorials)Vikas Hassija, Adhar Agrawal, and Adit Goyal are with
the Department of Computer Science and IT, Jaypee Institute of Information
Technology, Noida, India 201304 (e-mail<EMAIL_ADDRESS><EMAIL_ADDRESS>aditgoyal@hotmail.com).Vinay Chamola is with the
Department of Electrical and Electronics Engineering & APPCAIR, BITS-Pilani,
Pilani Campus, India 333031 (e-mail: vinay.chamola@pilani.bits-pilani.ac.in).
N. C. Luong is with the Faculty of Information Technology, PHENIKAA
University, Hanoi 12116, Vietnam, and is with PHENIKAA Research and Technology
Institute (PRATI), A&A Green Phoenix Group JSC, No. 167 Hoang Ngan, Trung Hoa,
Cau Giay, Hanoi 11313, Vietnam (email: luong.nguyencong@phenikaa-uni.edu.vn).
Dusit Niyato is with School of Computer Science and Engineering, Nanyang
Technological University, Singapore (e-mail: dniyato@ntu.edu.sg). F. Richard
Yu, Fellow, IEEE is with the Department of Systems and Computer Engineering,
Carleton University, Ottawa, ON K1S 5B6, Canada (e-mail:
richard_yu@carleton.ca).Mohsen Guizani is with Computer Science and
Engineering Department, Qatar University, Qatar (e-mail:
mguizani@ieee.org).Digital Object Identifier: XXXXXXXXXXXX

###### Abstract

Drone security is currently a major topic of discussion among researchers and
industrialists. Although there are multiple applications of drones, if the
security challenges are not anticipated and required architectural changes are
not made, the upcoming drone applications will not be able to serve their
actual purpose. Therefore, in this paper, we present a detailed review of the
security-critical drone applications, and security-related challenges in drone
communication such as DoS attacks, Man-in-the-middle attacks, De-
Authentication attacks, and so on. Furthermore, as part of solution
architectures, the use of Blockchain, Software Defined Networks (SDN), Machine
Learning, and Fog/Edge computing are discussed as these are the most emerging
technologies. Drones are highly resource-constrained devices and therefore it
is not possible to deploy heavy security algorithms on board. Blockchain can
be used to cryptographically store all the data that is sent to/from the
drones, thereby saving it from tampering and eavesdropping. Various ML
algorithms can be used to detect malicious drones in the network and to detect
safe routes. Additionally, the SDN technology can be used to make the drone
network reliable by allowing the controller to keep a close check on data
traffic, and fog computing can be used to keep the computation capabilities
closer to the drones without overloading them.

###### Index Terms:

Blockchain, Drone applications, Drone Security, Fog Computing, Machine
Learning, Software Defined Networks, UAV.

##  I Introduction

The number of drone or UAV based applications are drastically increasing.
Based on the latest TechSci report, the overall revenue from the drone
application-related market is expected to drastically improve from 69 billion
dollars in 2018 to 141 billion dollars in $2023$ [1]. The first application of
drones was seen in $1849$ when the Austrian army attacked Venice with some
unmanned balloons filled with explosives [2]. This was the point where the
idea of drones and its related applications came into the picture and became a
topic of exploration for researchers. During WWII, Reginald Denny invented the
first remote-controlled aircraft called the Radioplane OQ-2. It was the first
mass-produced UAV product in the US, and was a breakthrough in manufacturing
and supplying drones for the military [3]. The use of drones in multiple
domains has been rapidly increasing in the past few years.

Figure 1: Basic Process of Drone Communication [4].

Drones work on a simple procedure that involves a data link from the ground
controller to the drone and a data link from the drone to the satellite. The
ground station controller is also in link with the satellite at every point of
time. The basic functioning of drone communication is pictorially shown in
Fig. 1. Communication between the drone and the other components takes place
through radio waves. Drones can help in sending data from one point to another
with low latency [4]. Drones can provide on-the-fly communication facilities
in areas where terrestrial infrastructure is poor or has been destroyed, and
to provide any further destruction or harm, emergency services are required in
disaster-struck areas [5]. UAVs can act as a communication-bridge between
ground users and network nodes. Furthermore, they can also be used in various
monitoring or surveillance operations. A 3-D network can also be made to
integrate drone base stations (droneBS) and cellular-connected drone users
[6]. Although these applications are highly promising to provide safety and
comfort to all, they can also bring disastrous results if the drone
communication links are hacked and misused. Being resource-constrained, drones
are highly vulnerable to physical and cyber attacks/threats [7]. The storage
and battery capacity of drones is limited and if proper care is not taken, it
is easy to hack the chips and the sensors installed inside the drone’s circuit
to get all the stored information. Therefore, it is highly imperative to focus
on the security standards for drone communication as their applications
increase [8, 9]. The authors of [10] propose a way to reduce the service-time
of drones. Drone path planning can be done for secure positioning and it’s
verification of various components [11].

Figure 2: Structure of our Survey. TABLE I: List of Major Acronyms

Notation | Meaning
---|---
ADMM | Alternating Direction Method of Multipliers
AODV | Ad hoc On demand distance vector
ApaaS | Authentication Proxy as a Service
CPS | Cyber-Physical System
FANET | Flying Ad-hoc Network
FQ | Fair Queuing
GNSS | Global Navigation Satellite System Signals
HAE | Homomorphic Authenticated Encryption
ILP | Integer Linear Program
IMU | Inertial Measurement Unit
LOIC | Low Orbit Ion Cannon
LiDAR | Light Detection And Ranging
NBTM | Neural-Blockchain Based Transport Model
NIDS | Network intrusion detection systems
PKG | Private Key Generator
PUF | Physical Unclonable Function
RSA | Rivest-Shamir-Adleman
TVA+ | Traffic Validation Architecture
VIO | Visual Inertial Odometry

TABLE II: Related Surveys on Drone Security

Year | Author | Contributions
---|---|---
2015 | Lav Gupta et al., [12] | Discussion on security issues in swarm of drones or Flying Ad-hoc Network (FANET)
2016 | Riham Altawy, Amr M. Youssef , [13] | Survey on security, privacy, and safety aspects of civilian drones
2016 | Samira Hayat et al., [14] | Discussion on requirements of UAV networks for upcoming drone applications
2017 | Mohammad Mozaffari, Walid Saad et al., [15] | Issues that UAV faces due to wireless networks
2018 | Silvia Sekander, Hina Tabassum et al., [16] | Issues due to wireless networks and the architecture of 5G for UAV
2019 | Azade Fotouhi et al., [17] | Challenges faced by UAV in cellular communication
2019 | Saeed H. Alsamhi et al., [18] | The challenges faced in collaboration of drones and IoT specifically for smart cities
2019 | Sun Xingming, Yueyan Zhi et al., [19] | Survey on security and privacy issues of UAV
2021 | This paper | Survey on existing and upcoming security challenges in drone communication and their solutions

Due to the increasing use of drones, the issues related to drone security,
privacy, reliability, regulation, and ownership are also increasing at the
same pace. There are various security-critical applications where drones fail
to provide complete security of data, and that results in a great loss and
life-threatening risk. For example, on $29^{th}$, November $2018$, a drone was
hacked in Las Vegas and it came into the path of a tour helicopter [20].
Fortunately, the pilot could manage to avoid a crash, but this may not be the
case in all such events. A crash might have resulted in the loss of life of
many civilians. The incident was investigated by the Federal Aviation
Administration (FAA) and some strict rules against drone usage were also
brought into action. Various such threats can be caused by the unrestricted
use of drones in different applications without any standard security
parameters. In this section, we present various important drone applications
that are associated with critical security issues. Table I shows the list of
major acronyms used throughout this survey.

###  I-A Related Surveys and Our Contributions

Although a few recent works focus on surveys of issues related to drone
communications, the existing surveys generally consider a specific domain or
utility of drones. For example, the authors of [18] provide a detailed survey
on the challenges faced in the collaboration of drones and IoT specifically
for smart cities. Another work presented in [13] discusses the security,
privacy, and safety aspects specific to civilian drones.

Furthermore, a significant number of surveys have been done earlier for
discussing the privacy and security issues present specifically in UAVs or
communication networks. The authors of [17] focus on the use of UAVs for
cellular communications. The authors discuss various standardization
advancements, practical aspects, regulatory issues, and security challenges
related to the use of UAVs in cellular communication. The authors of [12]
provide a full review of various security challenges faced in UAV
communication networks. The authors specifically focus on the issues faced in
a swarm of drones or Flying Ad-hoc Network (FANET). A comparative study of
issues that differentiate FANET from other ad-hoc networks such as Mobile Ad-
hoc Network (MANET) or Vehicular Ad-hoc Network (VANET) is also done in good
detail. Furthermore, the authors of [21] review the use of Game Theory-based
approaches for UAVs. UAV path deviation attacks have been surveyed in [22].
The authors of [14] provide a review of the characteristics and requirements
of UAV networks for upcoming drone applications. A generic review on all the
network-related requirements such as safety, scalability, privacy,
connectivity, security, and adaptability are discussed. The authors of [15]
and [16] also emphasize on the issues related to the use of UAVs in the
wireless network. The work done in [15] provide some key guidelines over
analyzing, designing and optimizing communication systems unique to UAVs. The
authors also discuss the need for various security measures required for
drones. A complete drone architecture for 5G has been presented in good
detail. Moreover, a comprehensive survey discussing the security and privacy
issues faced by UAVs is presented in [19].

Hence, different from any of the previous works, this work is a comprehensive
survey on the most critical existing and upcoming security challenges in drone
communication and the related solutions. This paper aims to help the readers
get an overview of the state-of-the-art security challenges in drone
communication. The readers will also have a good overview of existing and
emerging security solutions for drone communication. Table II shows the major
survey works done in the direction of drone security in last few years.

The main contributions of this work are as follows:

  1. 1.

A complete review of different existing and anticipated attacks in drone
communication.

  2. 2.

Detailed and realistic recommendations to improve the drone application
architecture for secure communication.

  3. 3.

Extensive analysis on the existing and upcoming solutions that empower the use
of drone communication in multiple domains.

  4. 4.

An assessment of the future research areas, existing challenges, and, open
issues for developing secure drone applications.

###  I-B Organization

The rest of the paper is organized as follows. In Section II, we discuss
various security issues and security-critical applications of UAVs in
different domains. Section III discusses the fundamentals of various emerging
technologies for secure drone communication. Four major drone communication
security approaches, i.e., Blockchain, Software Defined Networks (SDN),
Machine learning, and Fog/edge computing are presented in Section IV, V, VI,
and VII, respectively. Section VIII describes various future research areas,
existing challenges, and open issues in drone security. Finally, we conclude
the paper in Section IX. The organization of the survey is also shown in Fig.
2.

##  II Security Issues in Drone Communication and Potential Vulnerabilities

Drone communication faces some specific security challenges along with the
generic cyber-threats. One of the reasons for these specific issues is that
drones are unmanned and it is difficult to handle or prevent unanticipated
issues dynamically and adaptively. Special attention needs to be given to
drone security issues as drones are different from the traditional IoT devices
(mobile phones, sensor-based alarms, smart trackers, etc.), and we need drones
to adapt to several advanced security concepts, such as confidentiality,
authentication, access control, and data protection, while being highly
resource constrained devices. Usage of drones needs to take care of
vulnerability concerns from sensor networks, mobile communication networks,
the internet, et cetera. The drones communicating via cellular data use radio
signals to communicate with the controller. The controller sends the radio
signals through its transmitter and these are received by the drone through
its receiver. The radio signals in between can be jammed or can be tampered
[23]. As stated by an IBM researcher, drones can be hijacked easily if they do
not have encryption on their onboard chips [24]. Because of resource
constraint issues of drones, encryption would not be the ideal solution. With
a huge amount of data exchange in drone communications, encryption and
decryption using complex algorithms require a certain amount of computational
power. The security concerns become more severe when drones use Wi-Fi for
communication [25]. Table III summarizes how susceptible various wireless
communication networks are in comparison with drone communication systems. In
this section, we present various specific security challenges faced by drones.
Furthermore, we also discuss the specific security vulnerabilities for each
attack in some drone applications. Various ideas and methodologies to overcome
these security challenges are discussed in the upcoming sections of this
paper.

TABLE III: Difference between security vulnerabilities of different wireless networks Security Issue | Ad-hoc Network | Sensor Network | Mesh Network | Vehicular Network | Drone Comm.
---|---|---|---|---|---
DoS | ✓ (Low) | ✓ (High) | ✓ (Low) | ✓ (Medium) | ✓ (High)
Man-in-the-middle | ✓ (Medium) | ✓ (High) | ✓ (Low) | ✓ (Low) | ✓ (High)
GPS spoofing | ✗ | ✗ | ✗ | ✓ (Medium) | ✓ (High)
Radar | ✗ | ✗ | ✗ | ✗ | ✓ (High)
Jamming | ✓ (Medium) | ✗ | ✓ (Low) | ✓ (Low) | ✓ (High)
Wormhole | ✓ (High) | ✓ (High) | ✓ (Low) | ✓ (Low) | ✓ (High)

###  II-A Security Issues In Drones

Few of the security threats discussed below are more drone-specific (GPS
spoofing, radars, jamming, and wormhole attacks), whereas the relatively
generic issues mentioned are discussed based on how adversaries can exploit
them to threaten the use of drones.

####  II-A1  Denial of Service Attacks

The DoS or the Denial Of Service is the most common and easy type of attack
that an adversary can use to stop the drone from functioning normally. This is
the simplest way of entering into the drone network and making it useless or
sometimes even harmful [26]. Fig. 3 shows the basic working of the DoS attack
in case of drone communication. Due to a large number of superfluous requests,
the access of shared resources to legitimate users is restricted. This will
cause the system to overload, and might result in rejection of some or all
legitimate requests to be fulfilled. In this process, the network connection
between the ground controller and the drone is de-authenticated as the
adversary sends several data packets to the drone which leads to the failure
of the computational power of the drone [27]. Data packets can be easily
created by any packet generator application and can be sent directly to the
drone’s network. ICMP (Internet Control Message Protocol) packets will be sent
at a very high rate which will make the network of the drone overflow,
resulting in the loss of control of the drone both by the drone as well as the
ground controller. It is also possible that there is some malicious code
present in one of the sent data packets, that can be used to attack the drone.
Such attacks can be used by the hijacker to crash the drone causing harm to
civilians and government agencies.

Figure 3: Denial of Service Attack on Wi-Fi Enabled Drone.

The authors of [28] have demonstrated the effects of DoS attack on $2$ types
of drones namely, Augmented Reality aerial drone (AR Drone) $2.0$ which is a
cheap quadcopter and $3$DR Solo which is a costlier quadcopter. The authors
experimentally evaluate three DOS attack-tools: Netwox, Low Orbit Ion Cannon
(LOIC), and Hping3, to analyze the drone’s behavior. Both these types of
drones are widely available in the markets. Both the drones were tested for
the image quality transmitted and how DoS attacks affect them. The study found
that both the costlier and cheap drones show a significant drop in frame rates
demonstrating clearly to us that even premium drone manufacturers are not
paying enough attention to drone security. The increase in network latency
shows the ease of DoS attacks even on such high end drones.

De-Authentication Attacks: This is a type of attack that can make the use of
drones difficult in various applications. This is a specific type of Denial Of
Service attack in which communication is disrupted between the client and the
Wi-Fi access point. In this attack, the control of the drone is lost by the
pilot as the attacker de-authenticates the ground pilot. Attacker can send a
de-authentication frame to a wireless access point at any point in time as
encryption is not needed to send the frame, despite the privacy technique
employed [29]. The attacker only needs the mac address of the drone which is
made available through any of the tools like ‘Aircrack-ng’ [30]. In the de-
authentication attack, the drone is hijacked by using this tool which
specifies the mac address of the drone. As soon as the Aircrack-ng tool is
activated, the connection between the drone and the ground controller is de-
authenticated. The attacker can use this tool to communicate with the drone
and direct it maliciously. This attack makes the drone go out of control and
leads to a heavy loss.

De-authentication attacks have become one of the newest concerns in the
industry as e-commerce giants, such as Amazon, look towards product delivery
mechanisms for drones. One of the most famous methods for carrying out this
attack is SkyJack [31] which uses an AR. Drone $2.0$ along with a Raspberry Pi
and wireless Adapters to hack and control drones. It sends de-authentication
requests through Aircrack-ng which is used to disconnect the target drone from
their user and then use the node-ar-drone library to communicate with the
target drone.

Figure 4: Man-In-The-Middle Attack On Drone [32].

####  II-A2 Man-In-The-Middle Attack

Man-in-the-middle attack places an adversary in between the client and the
drone. The adversary uses a device known as Wi-Fi Pineapple [30]. Fig. 4
represents the implementation of man-in-the-middle attack. In this attack, the
flight planning software broadcasts the plan to the drone controller which
sends it further to the drone. On successfully receiving the commands from the
controller, the drone sends the acknowledgement, which is received by the
attacker in between the drone and the controller. The attacker uses Wi-fi
Pineapple to send the forced commands to the controller. Once the pineapple is
set, it will run the recon mode which will trace out all the possible access
points that the client software may be using. Once the access point (drone) is
traced, it is added to the Pine-AP SSID (Service Set Identifier) pool. This
command is forwarded to the drone and the actions intended by the adversary
are imperceivably implemented by the drone. One example of man-in-the-middle
attack is active eavesdropping [33] in which the adversary connects himself
with the drone controller. After getting the access of the drone through the
SSID of the drone, the hacker sends the fake commands to the controller,
making them believe that they are communicating with the drone itself [34].

Fig. 4 depicts the man-in-the-middle attack. The authors of [32] explored
various vulnerabilities of UAVs; if a weak encryption scheme is used, the
password becomes easy to crack, and the man-in-the-middle attack can be
performed using the Wi-Fi link. Lack of secure encryption schemes throughout
the chain of communication can cause such attacks. The authors of [35], from
IBM, have demonstrated the easiness of stealing a police quadcopter worth a
thousand dollars by performing the man-in-the-middle attack. The researchers
revealed that a hardware worth only $40$ dollars is sufficient to perform such
an attack. This is a very clear example of an attack in which the controller
is not even aware of the middle layer hacker.

####  II-A3 GPS Spoofing

For communication, drones need incoming signals from GPS satellites, a two-way
link between the drone and the ground-station, and signals notifying the
drone’s presence [36]. Fig. 5 shows the basic working of the GPS spoofing
attack in drone communication. Spoofing can be done using multiple
transmitting antennas, in which the attackers transmitting antenna combines
with the corresponding receiving antenna and transmits the false signals. In
this process of getting the GPS coordinates of the drone, the drone is located
by the satellite using GPS and its coordinates are then sent to the ground
controller. The drones that do not have any encryption on their chipboard, are
easily tracked by the hacker and they share a wrong location to the drone
controller using a directional antenna with narrow beam-width aiming for the
drone. GPS spoofing is mainly carried out on military drones as they are
deployed at certain critical places that can provide highly confidential
information about the other nations. It is relatively difficult to spoof
military drones as they are highly equipped with encryption mechanisms.
Spoofing can be done using multiple transmitting antennas [37], in which the
attackers transmitting antenna combines with the corresponding receiving
antenna and transmits the false signals. The spoofer can take the drone to any
trajectory he/she wants without even giving the controller a hint as fake
coordinates are sent to the controller at regular intervals. This technique
can be used to reduce the velocity of the drone making it less useful.

Figure 5: GPS spoofing attack on GPS Enabled Drone [36].

According to [38], on December $5$, $2011$, an American UAV was detected and
shot down by Iranian forces near the city of Kashmar in northeastern Iran.
According to the American officials, the UAV was spoofed and was forced to fly
over Iran. The attackers hacked the UAV and injected it with wrong GPS
coordinates. The incident resulted in a disturbance in the relationship
between the two nations. The military drone was said to be using an inertial
navigation system, and not the GPS navigation because of increasing number of
spoofing and jamming attacks. Despite the measures taken to prevent any
spoofing attack, or to protect the classified information available from the
drones, the Iranians claimed that they could access it and reverse-engineered
the entire drone.

The authors of [37] have used Software Defined Radio (SDR) platforms to
simulate GPS to transmit false signals to the target drone. This methodology
has been used for a long time to hack or relay wrong information through
drones. Using this approach, they divert and take control of the drones that
depend on GPS for flight paths. For generating fake GPS signals, BladeRFx$40$
SDR is used, which is very versatile, and costs around $420$ dollars.

####  II-A4 Radar

Mono-static radar is the most traditional way of searching for important
entities. Similarly, it can be used to find drones. The radars send
electromagnetic signals that can travel a long distance. These signals travel
in all directions and wherever the presence of drone is detected, the signals
reflect from the surface of the drone and are received at the other desired
end. By further studying the signals, one can easily measure the velocity,
direction, and altitude of the drone. A problem with this technique is that
sometimes the electromagnetic signals consider obstacles like birds,
airplanes, or kites as drones and transmit wrong information to the radar
station, which in turn produces a loss. Radars operating in the millimeter
wave (electromagnetic spectrum) range can be used for surveillance of small
drones, even under adverse weather conditions, with high accuracy and with
distance-independent resolution [39]. Moreover, to overcome these issues,
hackers have tried to use various machine learning techniques, including the
SVM classifier, binary decision tree, etc., to classify between the real
drones and other objects [40]. In this technique, the detector is trained with
a lot of data sets to distinguish between any obstacle and a drone.

The authors of [41] have discussed in detail about the ways in which the
radars can be used to detect and identify drones. Various sensors such as
optic or infrared sensors are also used to detect or identify drones. However,
these sensors have various limitations in terms of range, and their
reliability in night, rain, and fog. The use of radar is declared by the
authors to be superior to visual optic sensors and infrared sensors because of
their range. Applications of drones such as package delivery, and military
operations, makes the drones very vulnerable to attacks that use radars. In
all such areas, detecting and identifying the drones can be a threat to the
drone itself, and might also result in the loss of other task-associated
resources.

####  II-A5 Jammers

These are the electronic devices used by the adversaries to block the signals
at the receiver’s end. It is mainly used for the disruption of communication
between several users. It works on a simple principle in which a transmitter
is used which is tuned to the same frequency as that of the target. If the
jammer has enough power, it over-rides the frequency signals, thereby blocking
every type of signal that the target can configure to. The jamming attack is
analogous to the DoS attack, but the only difference is that in the DoS
attack, the network, service, and the application layer get affected whereas
in the jamming attack radio signals are used to attack the drones which mainly
affect the physical layer. Signals of Wi-Fi and Bluetooth can be easily
jammed, and that too using a low power jammer [42]. The ability of the jammer
is judged by its range. Jammers with a higher range can block signals of
devices that are present upto that range. Fig. 6 shows the implementation of
the jamming attack. The attacker sends the jamming signal to the serving base
station from his end, with the help of a UAV, which matches the frequency of
the signal with the deployed drone. Thereafter, the signals between the drone
and the backup serving base station are blocked. Hence, no data and commands
are allowed to reach to the server, and the deployed drone becomes non-
responsive thereafter. Once the drone loses contact with the control station,
many drones have a auto-pilot mode as a fail-safe which gets activated. Auto-
pilot mode makes it easy for the attacker to launch a GPS-spoofing attack, and
force a landing away from the original destination by spoofing the GPS signals
[43]. A technology was introduced in Australia a few years back, which would
allow the hacker to commandeer a drone mid-flight and lands the drone in a
defined exclusion zone by the new pilot [44].

Figure 6: Jamming Attacks On Drone [45].

The authors of [46] mentioned an incident in which GPS jamming was used to
bring down $46$ drones during a show in Hong Kong. The drones started falling
with great velocity. According to the board’s executive director, these
professional drones were equipped with fail-safe technologies to direct them
back to their take-off location, but because the strength of the jamming
signals was so strong, the drones started dropping mid-air only. The hacker
had to just point the jamming device towards the drones and as soon as the
signal interruption was detected by the drones, they started falling, as
confirmed by Rex Ngan, founder of the Hong Kong Professional Unmanned Aerial
Vehicles Association.

####  II-A6 Wormhole Attack

UAV networks that utilize FANET or MANET are susceptible to routing-based
attacks like wormhole. The communication between UAVs rely not only on the
information exchange between UAV and the ground control station, but also
amongst the UAVs. FANET uses a system of auto-configuration and self-healing
to improve the reliability of the system, but it makes them vulnerable to
wormhole attacks [47]. A wormhole attack is one of the most severe and grave
attacks in MANETs.

In a wormhole attack, two attackers place themselves strategically in the
network in order to listen-in on the communication between the drone-network.
The attacker records the communication at one point in the drone-network and
tunnels the information to the second attacker, where the recorded information
is replayed. As the routing protocol algorithm looks for the nearest node to
transfer the information, wormholes are placed so as to make the distant nodes
believe that they are their closest neighbors. This kind of re-routing
compromises the confidentiality of sensitive information, and also enables the
attacker to launch an attack from any point in the network, because it
practically controls all the routes discovered after the wormhole [48].

Moreover, wormhole attacks in a UAV Ad Hoc Network (UAANET), made of a swarm
of UAVs and a ground control station, are a high-level risk, and special
attention needs to be paid to this multi-node attack. Even without the
knowledge of any the cryptographic keys or hash-functions, the attacker is
able to affect the integrity of the network by transferring control packets,
and further, captures the data traffic [49].

###  II-B Potential Vulnerabilities In Different Drone Applications

This section discusses the potential vulnerabilities in different drone
application. Although the drone applications are vulnerable to every security
issue mentioned above, we present the main security issue faced by the
specific drone application.

####  II-B1 Security Vulnerabilities in Mining Drones

Drones can help a lot in surface and underground mining [50]. Mining is a very
tedious task and involves a risk to the life of the miners, so drones can be
employed to decrease the workload and the risk to human life. Drones equipped
with infrared night vision cameras can help in finding ores easily. They can
also be equipped with a metal detector device to directly detect the ore even
without a camera. Such applications may help in reducing the mining cost, and
can increase the overall efficiency. There are various reasons and motivations
due to which the adversaries may get attracted towards hacking such drones and
launching a DoS attack. The other competitors may send unwanted requests to
the mining drones, thereby preventing them from accurately identifying the
ores and other valuables in the mines.

####  II-B2 Security Vulnerabilities in Disaster Management Drones

Drones can timely inform the respective emergency teams about the disaster
situation and can help in preventing the loss of life and property. They can
also help in delivering essential items to the disaster victims. Anticipated
installation of drones can also be done in disaster-prone areas, to keep an
eye on the upcoming disasters. The disasters may either be natural disasters
like earthquake and flood, or man-made disasters like riots and terrorist
attacks. Although there are multiple applications of drones in disaster
anticipation, identification, prevention, recovery, and damage control, it is
very important to safely deploy and use drones in such applications. These
drones are highly vulnerable to de-authentication attacks. Both false positive
and false negative messages from drones can result in major problems. Some
hackers may try to de-authenticate the drones deployed for disaster
anticipation and may send false positive messages to the emergency teams using
their own malicious drones. This will end up in a waste of time and money for
government bodies. False negatives may try to prevent the timely delivery of
disaster-related messages to the emergency teams and thereby lead to loss of
life and property.

####  II-B3 Security Vulnerabilities in Agriculture Drones

Various countries, like Sierra Leone, Somalia, and India, are dependent on
agriculture for their living [51]. The farmers need techniques that help make
farming easy and increase the productivity. Drones can be used for pollinating
seeds which is a very important task for farmers to grow their crops. The
drones can carry pollinating seeds and can have the dataset of the field where
they have to sprinkle the seeds in the required quantity. This can decrease
the work load of farmers and the mechanized sprinkling of seeds can also save
seeds as drones can be programmed to sprinkle only the required quantity of
seeds at appropriate locations. Drones can also help the farmers by spraying
medicines in the correct quantity to kill unwanted plants like weeds and
insects in the farms. In all the above discussed processes that are being
carried out by the drone, the data should be accurate and if the drone gets
hacked, the hacker can easily change the quantity of seeds or insecticides
that have to be sprayed. The agricultural drones can be easily hijacked using
the man-in-the-middle attack as the adversary can place himself between the
drone and the ground controller and can manipulate the data already fed in the
drone. Any unwanted change in the data can destroy the plants resulting in a
great loss to the farmers, and as well as to the nation.

####  II-B4 Security Vulnerabilities in Military Drones

The initial drones were all very noisy, and therefore, it was difficult to use
them for most of the hidden military operations. Various new drones have now
been invented that make the least sound which makes them difficult to detect.
The invention of such silent drones has increased their usability for various
military purposes as they can fly up to a very remote location secretly. The
cameras installed on the drones can be used to spot the enemy’s location while
carrying out any type of strike against the enemy. Although these drones have
multiple advantages in military operations, if the drones are hacked or the
communication link is spoofed, it can lead to disastrous results. The enemies
can even hack and reprogram the drones to act against the army itself. The
information leak by spoofing the communication link can also end up revealing
military plans to the enemies. Therefore, it is very important to take care of
all security standards before trying to deploy the drones for such critical
applications. The most famous incident of such a case is also known as the
Iran-U.S. RQ-170 incident [38], where Iran’s military used GPS Spoofing to
land a US UAV almost undamaged and reverse-engineered the entire design of the
stealth drones to make their own Sa’egheh drones. This turned into an
international incident.

####  II-B5 Security Vulnerabilities in Delivery Drones

Due to the increase in the pace of E-commerce, a lot of manpower is required
especially for the last-mile delivery of products. The use of drones can be a
promising solution as drones can deliver the products in less time and high
accuracy. Drones can be used to deliver food, medicine, newspapers, and other
things of daily basic needs. FAA approved the first NASA drone to deliver
medicines in July $2015$. The UAV could successfully drop medicines to the
health clinic in rural southwest Virginia [52]. In $2016$, Amazon made its
first drone delivery successful by delivering the package in $13$ minutes
after being ordered by a customer in Cambridge [53]. Amazon also launched a
drone for delivery named Amazon Prime Air which can fly up to a range of $10$
miles for product delivery. These drones can take off and land autonomously,
guided by GPS. Although, drones can help a lot in timely and accurate
delivery, if these drones are hacked, it can end up in a big chaos. The hacker
may use the radars to identify and capture the basic drones used for delivery
and may guide the drones to deliver packets to different destinations or to
himself. Therefore, care of the security standards is important even for the
delivery drones. Since a huge number of people are using e-commerce, any
misstep could endanger the privacy of billions of people in the future.

####  II-B6 Security Vulnerabilities in Drones for Urban Planning

Urbanization refers to the heavy movement of people from rural areas, like
villages, to the cities. Drones can be highly helpful for architects to take
some major decisions regarding renovations and new constructions. Drones can
also help in making and analyzing the plans for water management in cities. A
drone can be deployed with GIS (Geographic Information System), by which
drones can easily capture, analyze and manipulate geographical data of the
water supply management. It is important to have well-defined security
measures for such drones as well. Various rules have to be followed to approve
a safe construction. There have been various cases of buildings getting
collapsed due to illegal construction resulting in a loss of life. If the
architects rely on the results submitted by drones, and the values calculated
and submitted by drones are not secure, then illegitimate people might try to
hack and manipulate the drone functioning to get their illegal constructions
approved. To conceal their identity, people with ill intent could crash such
drones which could lead to a loss of resources. Other owners of illegal or
unauthorized construction sites may deploy jammers to prevent such drones from
identifying illegal constructions.

###  II-C Classification of Drone Communication Systems

####  II-C1 Drone-to-Drone

Even though drone-to-drone (D2D) communication has not been standardized yet,
it can be seen as a peer-to-peer (P2P) network [54]. This makes D2D
communications susceptible to various P2P attacks (DoS/DDoS, jamming attack,
etc.).

####  II-C2 Drone-to-Infrastructure

Drone-to-infrastructure communication can be further classified into
categories such as:

  1. 1.

Drone-to-Satellite: This infrastructure is used for the drone to coordinate
with the GPS. Although, it is expensive to set-up and maintain, such
communication systems are considered safe and secure.

  2. 2.

Drone-to-Network: This type of communication is useful for cellular networks
(4G, 5G, etc.), and it is very important to ensure their security when used.

  3. 3.

Drone-to-Ground Station: This infrastructure is based on common wireless
technologies like Bluetooth and Wi-Fi. They are public, and hence not secure,
making them very susceptible to man-in-the-middle attacks and eavesdropping.

Summary: This section discusses the security issues being faced by the
existing drone applications. Many attacks like DoS attacks, De-authentication
attacks, Man-In-The-Middle attacks, that deal with the tampering of the data
in the drones are mentioned in this section. Several other attacks that deal
with the position of the drones like GPS spoofing, jamming attacks, radars are
also discussed. In the next section, we discuss the overview and fundamentals
of various emerging technologies such as blockchain, SDN, ML, and fog
computing that can help in preventing the above-mentioned attacks on the drone
applications. Furthermore, we gave a brief classification distinction between
drone communication systems.

##  III Overview and Fundamentals of Various Emerging technologies for Secure
Drone Communication

In this section, we discuss the four main emerging technologies that are being
widely used and explored for making drone communication fast, reliable, and
secure. Mainly we discuss the use of blockchain, ML, SDN, and fog computing
for secure drone communication.

Figure 7: Working process of blockchain.

###  III-A Drone Communication Architecture using Blockchain

According to FAA, $1.3$ million drones have been registered with the FAA in
$2019$ and the number is expected to increase to $7$ million by the end of
$2020$ [55]. With the rapid increase in the number of drones, the data
generated by them is also increasing rapidly, which has increased security
concerns about the data. Researchers state that blockchain can contribute to
another layer of security to drone communication, which would prevent data
retrieval and tamper by unauthorized persons [56]. Furthermore, the data on
the blockchain is distributed, such that it becomes very difficult for an
adversary to hack a single system to get control over the complete data in the
network. Figure 7 shows the basic working process of the blockchain
technology.

#### Motivation For Using Blockchain For Drone Communication Security

A blockchain is a growing chain of blocks linked to each other using
cryptographic hash functions. The drone applications are becoming highly
popular and are gradually being used in almost all domains and spheres of
life. With the increasing number of drone applications, it is imperative to
keep the transactions between the drones and other users secure, cost-
effective, and privacy-preserving. The blockchain technology is a highly
promising solution that can be used to deploy real-time drone applications.
Once a transaction is recorded on the blockchain, it remains immutable and no
adversary can try to tamper the records [57]. Furthermore, the use of smart
contracts can help a lot in performing different transactions between
different parties in a secure and cost-effective manner. Depending on the
nature of the application, different kinds of blockchain networks can be
created, such as public, private, consortium, and hybrid. Moreover, there is a
vast variety of consensus algorithms used in the blockchain network ranging
from PoW, PoS, PoB, DAG, and so on. All these features of blockchain can be
leveraged to make drone communication secure, reliable, and cost-effective. In
Section IV, we discuss in detail the various blockchain-based models to secure
drone communication.

Figure 8: Basic SDN Architecture [58].

###  III-B Drone Communication Architecture Using SDN

Software-Defined Networking (SDN) is a networking architecture that can be
used to control or program the network centrally using software applications.
SDN helps in the consistent management of the network as everything in the
network is centrally programmed. The architecture of a typical SDN-based drone
communication network is shown in Fig. 8. The figure illustrates a simple use
case of SDN in drone applications. It shows the steps involved in transmitting
data from the drones in the data plane/layer to the control plane for getting
the data processed and then getting back the required output. In a typical
SDN-based drone communication network, each drone in the network behaves as an
individual switch. The application plane of the SDN sits on a centralized
controller and is responsible for the implementation of any and all high-level
functions to be performed by the network as a whole. The centralized
controller also houses the control plane, which would command and control data
flows between the drones. The data plane consists of the drones themselves,
which respond to commands from the controller. There exist a variety of
protocols and standards for performing various functions in the network.
Because the SDN architecture decouples the control plane from the data plane,
protocols in different planes can be implemented independently. This offers a
considerable degree of freedom in the design of an SDN-based drone
communication network. The authors of [59] review the various 5G techniques
based on UAV platforms using network layer techniques like software-defined
UAV networks.

#### Motivation For Using SDN For Drone Communication Security

As discussed above, SDN enables the network to be centrally controlled, which
makes the network reliable. Moreover, SDN’s decoupled data layer, control
layer, and application layer makes the network control directly-programmable.
The increasing drone applications make use of real-time video streaming which
can be achieved by the use of SDN, as it can provide better QoS because the
traffic is automatically controlled in the SDN. The drone is highly resource-
constrained so there are many vulnerabilities that can be prevented by the use
of SDN, as the controller can keep a close check on the data traffic. The
above-mentioned parameters of SDN helps in maintaining the overall security in
the network. Section V discusses the SDN-based DCN models that can help in
making the drone communication secure from different types of attacks.

###  III-C Drone Communication Architecture Using Machine-Learning

Figure 9: Basic ML Techniques Architecture [60].

Machine learning is a technique that provides the system with the ability to
learn automatically and ameliorate using past experiences without being
explicitly programmed. Once the data is fed, ML learns and predicts the output
automatically without much human intervention. The ML algorithms need a large
amount of training data to make more accurate predictions. ML algorithms are
broadly divided into two categories, i.e., supervised machine learning
(training dataset can be classified into several labels) and unsupervised
machine learning (training data is not classified) algorithms. Figure 9 shows
the basic architecture of ML-based drone communication applications. The
figure illustrates the various ways in which the ML techniques can assist in
making the drone communication secure. Several ML algorithms like CNN
(Convolutional Neural Network), SVM (Support-Vector Machine), ANN (Artificial
Neural Network), RNN (Recurrent Neural Network), etc. can be used for making
drone communication secure. ML algorithms, such as LSTM (Long Short-Term
Memory), can also be used for detecting the faults in drone communication, and
the recovery methods are sent to the drone for its safety. A classification
algorithm can be applied which can help in detecting the DoS attacks and other
attacks that make use of the fake and affected data packets to paralyze the
network. The data packets can be easily classified as either benign or
affected packets, which can prevent the network from getting hacked. These
diverse applications of ML can help achieve highly secure drone communication.

#### Motivation For Using ML For Drone Communication Security

The ML algorithms learn from the training data and improve themselves for
achieving better results and high accuracy without any human intervention,
which is a huge advantage. The ML algorithms can be deployed for detecting the
presence of malicious drones in the network and can help in preventing the
attacks such as man-in-the-middle attack [61] and spoofing attacks [62]. Such
algorithms keep on improving with increasing experience, and provide better
and more accurate results. The models can also be trained to automatically
detect and recover from the faults using neural networks and LSTM. Moreover,
ML algorithms can handle multi-dimensional and diverse data. All these
parameters make these algorithms highly suitable for use in drone
applications. In Section VI we discuss in detail the use of various ML
techniques to secure drone communication.

###  III-D Drone Communication Architecture Using Fog Computing

The concept of fog computing was first introduced by CISCO in $2014$ [63]. Fog
is considered to be a dimension that extends the use and capabilities of the
cloud. Fog is not particularly a substitute for cloud computing; instead, it
is a large complement of cloud computing. The fog layer is a layer or stratum
between the edge devices and the cloud. Deploying cloud servers is very
difficult as it is very costly and is very difficult to establish. So a new
concept came into the market in $2014$, by which the load of the cloud can be
minimized. Fog is a smaller version of the cloud which can be placed near to
the end devices. Fig. 10 shows the layered architecture of cloud, edge, and
fog computing combined. What happens in fog computing is that whenever an end
device user requests some query of fetching any data or uploading any data,
the mobile network helps in connecting to the nearest fog node available. Now
the data can be easily fetched and stored in the fog. The fog uses LAN (local
area network) whereas for accessing cloud facilities we need to access the
internet through WAN (wide access network) which will take more time as well
as more cost. So, fog computing is very helpful in many aspects like cost,
time, and security. Fog domain is made by combining multiple fog nodes which
can be switches, gateways, routers, smartphones, or PCs to form the fog
stratum.

Figure 10: Layered Architecture of Cloud, Edge, and Fog Computing [64].

#### Motivation For Using Fog Computing For Drone Communication Security

Fog Computing is a paradigm that can help in processing and accessing large
data rapidly, efficiently, and with the least possible latency. This is a
layer between the end-device and the cloud servers. Fog computing helps in
increasing the QoS and QoE, as the retrieval time of the data is very less in
the fog. Fog computing also reduces the data load on the cloud servers and
makes the data dissemination very cost-effective as well as reliable. The fog
is a decentralized paradigm in which the data is stored at multiple fog
layers. This proves advantageous as compared to when data is stored in one
place, because data in the fog has no central entity handling the entire data
making it less vulnerable. This also prevents the cloud server from being
getting affected, as the vulnerability can be detected at earlier stages.
These aspects make fog computing a very important technology in making drone
communication secure. The fog-based DCN methods and models that help make the
drone communication secure are discussed in Section VII.

Figure 11: Different drone applications using various security techniques.

Fig. 11 shows various drone applications in different domains that have used
blockchain [65], SDN [66], ML [67], or fog computing [68] for securing drone
communication. The rest of this paper discusses the usage, and benefits of
these technologies in making drone communication more secure in detail.

##  IV Applications of blockchain for Drone Communication Security

Drone technology has been there for almost a century, but in the recent past,
it has gained importance in fields such as agriculture, security, wildlife
conservation, delivery, and so on. Blockchain technology is said to have the
potential to improve data security and transparency across multiple domains
[69, 70, 71, 72]. In this section, we will elaborate the models and the
mechanisms based on blockchain that can be used in making the drone
communication secure.

Various other non-blockchain technologies have also been proposed to increase
drone security. There are various issues related to such solutions that can be
resolved using the features of blockchain technology. A model proposed in [73]
helps in maintaining data integrity by using sensor Physical Unclonable
Function (PUF). This method provides data integrity but fails in maintaining
self-trust and the data provenance. Another non-blockchain model for
preventing a wormhole attack has been discussed in [74]. The authors use a
label-based method for detecting the attack. The model only addresses wormhole
attacks and is still vulnerable to other attacks that blockchain could prevent
such as DDoS attacks, and GPS spoofing attacks.

We further discuss the list of specific security issues that can be resolved
and prevented using blockchain as a solution. Fig. 12 shows the various
security applications of blockchain in a network of drones.

###  IV-A Air Traffic Management

UAVs have recently gained huge popularity. Thus, with a number of drones,
their paths may cross with one another and sudden collision may occur.
Therefore, it is necessary to devise a solution and a platform to maintain
optimal paths for air traffic management [75]. Such traffic management in
drones is different from traditional road traffic management, as there is no
well-defined path of travel for each drone and the coordinates need to be
maintained in $3$ dimensions. Blockchain and IoT have many advantages over
traditional internet-based systems due to the fact that the internet-based
systems are more prone to cyber-attacks that would degrade or disrupt the
functioning of the drones. Traditionally, GPS coordinates are used for UAV
localization and avoiding traffic violations. However, such approaches are
difficult to apply for complex paths due to pilot errors and other intrusion
attacks. The authors of [76] suggest that the neural-blockchain based
transport model (NBTM) can significantly help in optimizing the problem of the
air traffic violation. This model involves the use of $3$ different blockchain
networks to form a master blockchain, taking the input parameters as a
function of the reliability of connections and reliability of flyby time. The
model also generates feedback for initial inputs while iterating towards an
optimal solution. The forward propagation is done between Blockchain $A$ and
$B$, and backward propagation is between Blockchain $C$ and $D$. The primary
components of NBTM are blockchain and neural networks. The neural model is a
4-layer network having $B$ and $C$ as intermediate layers. The output of the
neural network model is used to form the optimal path for UAV to travel. This
model does not employ any separate mechanism for security, but is simply
dependent on the basic principles of the blockchain. The simulation results
demonstrate that the proposed neural-blockchain enhances the reliability (the
statistical parameter for evaluation of consistency) of the network with a
lesser failure rate. Due to the availability of a feedback mechanism, the
model reduces the computation power demand, resulting in lesser complexity and
yields higher efficiency when compared with the model proposed in [77]. In
[77], the authors have proposed a model for reducing the number of transaction
required for updating the ledger in the Internet of Vehicles (IoV), so that
less latency is experienced in maintaining the air traffic whereas in [76],
feedback mechanism gives better results. However, the dynamic partitioning
between a centralized system and the blockchain-based system is still a
challenge to be worked upon.

Preventing mid-air collisions:  Air traffic Control (ATC) needs improvement in
preventing mid-air collisions due to the increasing number of UAVs. The Las
Vegas incident wouldn’t have happened if proper precautions were taken [20].
Due to resource constraints and heavy traffic, the UAVs are subjected to
transmission delays in communication with the ground station, unlike high-
speed LAN serial communication, and therefore an innovative method to improve
ATC and prevent mid-air collisions is required. The authors of [78] propose a
blockchain-based solution for ATC management to prevent mid-air collisions.
Similar to [76] and [79], the authors of [78] focus on physical protection of
drones in case of high air traffic. However, different from [76] and [79], the
authors of [78] focus more on exploiting the feature of peer-to-peer
transactions in blockchain technology as compared to the feature of tamper-
less data storage. If the path at which the drone has to traverse is defined
and is stored in a tamper-less data storage, then the drone can be made
secure, as no adversary can change the path of the drone for its benefit.
Because of drones’ agility, collision avoidance algorithms can also be
employed to prevent mid-air collisions. A fast obstacle collision avoidance
algorithm for UAVs has been proposed by the authors of [80]. Using this
algorithm, the drone can avoid static and dynamic obstacles, while still being
able to get back on it’s initial trajectory. Mid-air collisions can be
dangerous as after the collision the drone could fall down to the ground and
can cause harm to human life. Similarly, mid-air collisions could also bring
in various threats to the airplane flying in the sky as any collision with the
airplane would lead to loss of lives as well. In the proposed model,
blockchain is used to store UAVnet data that comprises UAV-ID, flight route
sheet, flying schedule, and sensor’s data. The computing UAVs are divided into
$m$ groups each containing $n$ number of UAVs. Out of those $m$ groups, one is
used to store information broadcasted from other UAVs and acts as actual
blockchain participant. The other computing UAVs simulate the possible paths
for the idle UAV to reach its destination. The optimal path that would limit
the mid-air collisions is chosen by the Proof of Graph (PoG) consensus
mechanism which is based on Simplified Memory Bounded A* (SMA*) Algorithm
[81]. The authors compare the SMA* Algorithm with A* and Dijkstra’s
algorithms. Although Dijkstra can find the shortest path, the algorithm must
explore all possible paths, resulting in high complexity. A* algorithm uses
exponential memory whereas SMA* uses bounded memory, where in exponential
memory, addition of data increases the computation time exponentially [82],
and in bounded memory, the computation time depends on the amount of memory
the data needs [83]. This specification of the bounded memory makes the
algorithm memory efficient and reduces the required computation time.

Figure 12: Security applications of blockchain in a network of UAVs.

###  IV-B Geo-fencing System

Geofencing can be defined as the virtual fencing or boundary created to
disengage UAVs from entering a sensitive area such as prisons, airports, and
private properties [84]. It is similar to road networks where some vehicles
are not allowed to enter certain zones. However, creating fences is more
complicated in IoD due to lack of well-defined pathway, and motion being in
$3$ dimensions. Traditionally, DJI’s GEO System was used to mark where it is
safe for the drone to fly and where the authorities may raise concerns about
the flight of the drone[85]. However, such systems may not be suitable for
drones, as there are certain prohibited zones where the drones cannot fly,
like near the airports [86]. These systems give the optimal path which cannot
be used in the case of the drones as the proposed optimal path may lie in the
prohibited zone. Blockchain can be effective in maintaining such a restriction
based on the $3$ dimensional coordinate system in real-time. The pioneer work
for blockchain-based flight space allocation in a decentralized fashion is
[79]. Unlike [76], the authors of [79] focus mainly on preventing the entry of
drones into restricted areas rather than performing complete traffic
management. In the proposed model, the UAV during its flight adds its request
for air-space to the blockchain network. The trajectories are then added in
such a way that it does not conflict with any restricted zone defined through
virtual fencing. Blockchain can maintain the constraint that the optimal path
should be chosen such that it should not lie in the prohibited flying zone.
This also mandates that air paths of multiple UAVs do not cross with one
another which would eventually lead into a crash. The proposed model is better
than the baseline scheme in [76], as the proposed model uses blockchain both
for geo-fencing and avoiding traffic violations. The benefits of using
blockchain to maintain geo-fencing include its immutability and safety from
cyber attacks. However, as transactions continue and records grow, and block
sizes increase in a blockchain, eventually exceeding any limits set, each
transaction will need more time to be processed. Thus there is a need for
blockchains with a higher TPS (Transactions per Second) rate to avoid bulking.
Some newer blockchain-based structures that offer higher transaction rates of
$3500$ TPS have also been proposed in the literature [87].

###  IV-C Maintaining data integrity

The immense data including the geographical addresses, and the data captured
by the sensors on the drones, can be collectively used to profile an
individual, and thus, can lead to privacy leakage [88]. The Iranian government
claimed to be able to access all the information from the American UAV and
reverse-engineer the entire drone [38]. As drones have limited computation
resources, the data processing can be done in the cloud. In traditional cloud-
based solutions, the Zone Service Providers (ZSPs) provide for the navigation
of the drones and the feedback systems between drones. However, ZSPs are
vulnerable to attacks due to high latency and high false rate [88]. An
efficient IoD using blockchain technology is proposed in [65]. Different from
[76] and [79], the focus of the authors of [65] is more towards securing the
important data being sent by drones, rather than physically preventing them
from colliding or entering restricted areas. Tamper-proof and transparent
storage of data are the main features of blockchain technology being exploited
by the authors of [65]. In the proposed algorithm, firstly, the drone enrolls
itself in the blockchain ledger for storing the data, and a unique ID is
assigned to the drones. The data is then hashed for maintaining the integrity
and is then uploaded to the blockchain network, via the controller. After the
data is successfully uploaded, an acknowledgment is sent to the drone. The
data records are transformed into a Merkle Hash tree [89]. Furthermore, data
auditing is done in the cloud which is a crucial step as it helps to detect
any anomaly in the data. The proposed model was analyzed for the response time
with varying numbers of drones. The simulation results demonstrate that the
response time increases linearly from about $400$ms for $100$ drones to about
$550$ms for $1000$ drones, thus providing better scalability. The average
response time latency is also fairly stable, varying from $350$ms to $1000$ms
for a $100$ drone network. The proposed model also makes the network less
vulnerable to attacks like DDoS attacks and data losses, while making it more
accountable. One major challenge is the time delay in the drones due to proof
of work. For mining a block, computing machines require enough time, which
results in latency [90]. Due to hardware constraints in drones, lightweight
cryptography and DAG-chain based consensus algorithms can be developed [91].

Secure Data Dissemination:  Data dissemination is the process of distribution
of data/information to its end-users. The authors of [92] propose a
blockchain-based algorithm which helps in secure data dissemination in the IoD
(Internet of Drones). Although the model presented in [92] is based on
blockchain, it is not designed to keep the localization information as shown
in [93]. The authors of [92] make use of blockchain technology only to secure
the data transfer between the drones and drone controllers. A combination of
the approaches discussed in [93] and [92] would be a promising solution for
securing both localization and data dissemination. The proposed model in [92]
is designed using three layers, namely, the user layer, the infrastructure
layer, and the IoD layer. In the user layer, blockchain technology is used for
the verification and the security of each transaction made in the model. The
second layer, the infrastructure layer, consists of all the base stations,
which ensure the connectivity between the drone controller, the drone, and the
end-users. IoD layer consists of the drones that capture real-time data and
communicate amongst themselves for making certain decisions. Two types of
nodes are considered in this model, (i) forger nodes and (ii) normal nodes.
The forger node is used for creating new blocks in the blockchain, whereas,
the normal nodes are used for the verification of the blocks in the
blockchain. This model works in three stages. First, the forger node is
selected, and the other remaining nodes are declared as normal nodes. After
the forger node is selected, the hash value is calculated by the forger node
using the PoS (Proof of Stake) consensus algorithm [94]. The other nodes
validate the hash value broadcasted by the forger node by comparing it with
the hash value that is generated using the Merkle Hash tree. If both the hash
keys match, the block is validated and is added to the main chain. The forger
node then encrypts the data packets and sends the request to the public
distributed blockchain. When the request is accepted, the forger node computes
the digital signature of the data packets with its private key and broadcasts
it to the public blockchain. The data is stored in the blockchain and can be
accessed only using the decryption key, so attacks like spoofing or DoS
attacks can be prevented using this algorithm. The authors evaluate the
security of the proposed model in terms of communication cost and time. The
simulation results demonstrate that the proposed model provides data
authentication, authorization, and accountability that is not offered by other
state-of-the-art related works. Another work related to securing data
dissemination in IoD is [88]. The authors of [88] use Identity-based
encryption techniques for secure data dissemination. Such techniques can
provide data integrity and identity anonymity only, and fail to provide
authentication, authorization, and accountability of nodes in the network.
Also, there is no proposal for data verification and validation in [88] as
compared to the blockchain-based approach used in [92].

TABLE IV: A summary of advantages and disadvantages of major applications of blockchain for drone communication security. | Major
---
approaches
Advantages | Disadvantages |  | Benefits over
---
traditional approaches
[76] |  | • Significantly helps in optimizing the
---
problem of air traffic violation
| • Does not support dynamic
---
partitioning of UAVs into groups
Reduced Latency
[79] |  | • Prevents the UAVs from entering into
---
any restricted zone through virtual fencing
| • Can support only a limited
---
number of transactions per minute
| Immutability, Safety
---
from cyber attacks
[65] |  | • Supports high scalability with stable
---
response time latency
| • Significant latency in the data
---
transmission
| Scalabilty, Data
---
integrity
[78] |  | • Supports high computation speed and
---
memory efficiency with SMA*
| • Does not support dynamic
---
partitioning of UAVs into groups
| Cost-effective,
---
Scalability
[93] |  | • The chances of localization errors are
---
reduced to $1/4^{th}$
• Suspectible to $51$% attack | Localization
[92] |  | • Proof-of-stake consensus algorithm is
---
used to significantly reduce the
computation time and cost
| • Regulatory control and
---
governance features are missing
| Authentication, Authoriz-
---
ation, Accountability
TABLE V: Applications of Blockchain for drone communication security.  Ref. |  Attack |  Mechanism |  Blockchain Feature used |  Major achievement |  Open issues
---|---|---|---|---|---
[76] |  GPS Spoofing, Jamming attack |  The optimal path generated by the neural network is stored in the blockchain |  Peer-to-Peer model |  The model gives the best path with a maximum failure rate of $25.8$% |  Making the model efficient for a large number of UAVs
[79] |  GPS spoofing, DoS attacks, DDoS attacks |  The blockchain assigns trajectory to the UAVs such that no route clashes with the other UAVs route. |  Tamper-proof data, Peer-to-Peer network |  A collision-free trajectory is proposed such that the UAV does not enter the geo-fenced zone |  Model has to be trained for handling large number of UAVs
[65] |  DoS attacks, DDoS attacks |  The data generated from the sensors is stored in the Merkle Hash tree which ensures data integrity |  Distributed- database, Public key infrastructure |  The average response time for data transmission with $1000$ drones is $550$ms |  Implementing private blockchain can make the system more secure
[78] |  Man-in-the- middle attacks, GPS spoofing, DoS attacks |  The details of the UAV stored in the blockchain are used to calculate the optimal path using the SMA* algorithm |  Distributed storage, Tamper-free transactions |  SMA* gave the optimal path in a very less time and it uses bounded memory as well |  Efficient dynamic partitioning of the UAV groups is needed
[93] |  DoS attacks, Wormhole- attack, GPS spoofing |  The co-ordinates of the drones stored in the blockchain is made available to the other drones after the verification |  Decentralized network, Distributed- database |  The localization errors are reduced by $75$% |  Model is still susceptible to $51$% attack
[92] |  Eavesdropping attacks, GPS spoofing, DoS attacks |  If the hash value generated by the Merkle Hash tree and the computed hash value by the forger node is the same then only the data is transmitted |  Data integrity, Distributed- database, Decentralized- network |  The total computation time required for data dissemination is computed to be $0.046$ms. |  Implementing private blockchain can make the system more secure
[95] |  DoS attacks, DDoS attacks, GPS spoofing |  The data generated by the drone is stored in the blockchain and is transformed into the Merkle Hash tree to maintain the data integrity |  Decentralized- network, Peer-to-Peer model, Immutability |  The results show that only the validated drone were allowed to transfer the data |  The model can be further enhanced for multi UAV
[96] |  Man-in-the- middle attack, DoS attack, DDoS attack |  The swarms of drones needs to register themselves on the blockchain using their public key and after the validation, the data is added to the server |  Distributed- Database, Immutability, Public Key- infrastructure |  The data acquisition was done successfully and with high efficiency |  Blockchain with higher TPS can be incorporated in the model for making it more efficient
[97] |  DoS attacks, DDoS attacks, Man-in-the- middle attacks |  The trust is lost from the intruding UAV when several intruder events are detected |  Peer-to-Peer model, Decentralized- network |  $90$% of the UAVs were able to support the data about the event and none detected the intruder event |  Work on detecting the compromised UAV successfully is required
[98] |  GPS spoofing, Man-in-the- middle attacks |  A consumer makes an order according to which a smart contract is generated. Any free UAV can accept the order and the client details are sent. |  Peer-to-Peer model, Decentralized- network, Smart Contract |  The blockchain and smart contracts are proved to be successful in organizing a secure communication between the UAVs |  Implementing private blockchain can make the system more secure
[99] |  DoS attacks, DDoS attacks, Hijacking |  The interest-key-content binding (IKCB) is stored in the blockchain which is compared by the router and the poisoned data is discarded |  Tamper- resistant ledger, Consensus- algorithm |  The proposed model gives better results when compared with the Interest-key binding as it has lower system overhead and the latency is reduced |  The forwarding technologies can be optimized to make the model efficient

###  IV-D Secure Localization

A swarm of drones are deployed that automatically take actions to achieve a
specific goal cooperatively. In such scenarios, the exact location coordinates
of the drones is critical information for completion of the mission. However,
the generic localization algorithms are vulnerable to attacks as the adversary
can easily inject false location coordinates. A pioneering work on secure
localization on the internet of drones using blockchain is presented in [93].
The authors propose a blockchain-based localization algorithm for securing
drones. Three major features of the algorithm are, (i) Decentralization: no
central entity would be present to maintain the localization coordinates of
the drone, (ii) Peer-to-Peer communication between the drones, and (iii) no
need for a central trust node to manage the security of the data and the
coordinates between the drones. The other non-blockchain based approach for
securing localization in IoD is discussed in [74]. This method focuses only on
preventing the wormhole attack. The approach in [74] being centralized in
nature cannot be used to prevent other generic attacks such as DoS. In the
proposed algorithm, the drones need to cooperate with various other anchor
drones knowing their exact coordinates. The coordinates of the anchors are
sent to the requesting drone using the private key of the anchors. Next, the
coordinates of the anchor drones are added to the distributed blockchain
ledger after verification. The requesting drone first requests for the
coordinates of the anchor drones present at a $1$-hop distance. If the
requesting drone receives the location from at least three anchor drones in
the 1-hop, the distance between the requesting drone and the anchor drone is
calculated using the Received Signal Strength Indicator (RSSI) method [100].
However, if the requesting drone does not receive three minimum responses from
the neighboring anchor drones, the distance between the requesting drone and
the anchor drones is calculated using the DV-Hop (Distance Vector Hop) method
[101]. The DV-Hop method works by first computing the average hop distance and
then multiplying it by the number of hops. The authors also calculate the
change in localization accuracy with the increase in the number of malicious
nodes in the network. The accuracy of the proposed algorithm is proven to be
better than the generic localization algorithms [102]. Simulation results
demonstrate that the localization errors are minimized by $1/4^{th}$ in the
presence of $50$% malicious nodes in the network. Moreover, due to the
decentralized nature of blockchain, various other attacks such as DoS attack,
and wormhole attack can also be prevented. However, the model is still
susceptible to the $51$% attack and other attacks, as is the case with
blockchain. $51$% attack happens when the number of malicious nodes in the
network become more than half of the total nodes in the network, and hence,
fair localization coordinates would not be revealed.

Summary: The objective here is to minimize the possibilities of any kind of
physical attack on drones or data losses in drone communication. A summary of
advantages and disadvantages of major works is given in Table IV, and a
summary of the related works is given in Table V. As seen, the blockchain
technology is mostly used to provide a peer-to-peer model to mitigate the
various security issues related to generic centralized architectures. In
various works, the smart contract and incentive model features of blockchain
are also used to enhance data security and reliability in various scenarios
related to drone communication.

##  V Applications of SDN for Drone communication Security

Software-Defined Networking may be defined as a networking paradigm centered
around the separation of the data plane or forwarding plane of a computer
network from its control plane, and the application layer. Software-defined
networking is an architecture that aims to make networks agile and flexible.
The SDN virtualizes the network by separating the control plane that manages
the network from the data plane where all the traffic flows. SDN simply
decouples the network control from the forwarding process of the network
packets. This decoupling allows the network to be controlled separately
without worrying about the traffic flow. This infrastructure will keep the
traffic and the network services abstracted from the network control. These
SDN parameters help in making the drone communication secure. In this section,
we talk about the SDN-based DCN that helps in resolving various security
issues in DCN.

There are several existing solutions that tend to resolve the security issues
related to drone communication without using the SDN architecture. The pioneer
work in this direction is presented in [103]. In this model, the UAV receives
the request from the ground controller and sends the data back to the
controller in the form of visuals. The method described in the model demands
high bandwidth for its execution, which varies with the speed of the drone or
the broadcasting channels. Such traditional solutions fail to provide high
security in the new generation drones, and also fail in maintaining the data
integrity. Another model proposed in [104] uses heuristic algorithms for
providing data integrity but fails in providing good efficiency and
reliability.

TABLE VI: A summary of advantages and disadvantages of major applications of SDN for drone communication security. | Major
---
approaches
Advantages | Disadvantages |  | Benefits over
---
traditional approaches
[105] |  | • Very short average file-transfer time
---
• Every sender gets fair share of
bandwidth
| • Legitimate sender may have to
---
wait for a long time
| Scalability, Lightweight,
---
Self-reliant defense
[106] |  | • The drone from which the DDoS
---
attack is launched can be found in
a very less time
| • Cannot work properly when the
---
number of flow table items in the SD-
IoT is very high
| Detecting and Mitigating
---
DDoS attacks faster
[66] |  | • The average end-to-end outage rate
---
in IoD is reduced by $18$%
• High average end-to-end delay |  | Reduction in end-to-end
---
delay
[107] |  | • The model has high fault-tolerance
---
• High performance due to the presence
of multiple controllers
| • The link between the data plane and
---
the control plane is still susceptible
to attacks
| Scalability, Mobility
---
[108] |  | • The latency and the maximum load
---
experienced by a SDN switch is reduced
by $50$%
| • The complexity of the algorithm is
---
quite high
| Stability, Security,
---
Reduced network latency

Considering the above issues, in this section, we discuss the methods that
involve SDN for maintaining the security in drone communication. We further
discuss the list of specific security issues that can be resolved and
prevented using SDN as a solution.

###  V-A DoS Attacks

Due to resource constraints, we need a highly efficient protocol that is
resistant to large–scale DoS attacks. NetFence protocol in SDN based Drone
Communication Network (DCN) as proposed in [105] can be used to create a
scalable DoS resistant network. The proposed model makes use of traffic
policing inside the network. The packets in the network carry the unforgeable
congestion policing feedback that is attached on the packets by routers. For a
drone to be a part of the network, it needs to first send a request packet to
a NetFence ready receiver. When it is accepted, it receives feedback and along
with the acknowledgement, it sends regular data packets. Non-NetFence senders
can only send packets through the legacy channel which is given the lowest
packet-forwarding priority. Bottleneck routers act as congestion detectors
which regularly check link load and packet loss rate. The rate limiters reduce
data congestion through the Additive Increase and Multiplicative Decrease
(AIMD) algorithm [109]. NS-$2$ simulations were implemented in linux, and the
performance of NetFence in DoS attacks was compared with $3$ other mechanisms
that are Traffic Validation Architecture (TVA+) [110], StopIt [111], and Fair
Queuing (FQ) [112]. Netfence has the advantage of having short average file-
transfer time that does not increase significantly with an increase in
senders, whereas, in FQ, transfer time increases linearly with an increase in
senders. Even though mechanisms like TVA+ and StopIt tend to block large-scale
DoS attacks, per-host queuing is implemented in these algorithms as compared
to per-Autonomous System queuing in NetFence. This is advantageous as the
number of autonomous systems is significantly less than the number of hosts.
No matter how heavy the attack is, the Netfence protocol makes sure that
senders get their fair share of bandwidth. This model has a drawback, a
legitimate sender may need to wait more time in Netfence to transmit data than
in TVA+, StopIt. Additionally, NetFence algorithm also fails to distinguish
between the congestion caused by DoS attack or any other issue in the network.

###  V-B Distributed Denial of Service (DDoS) attacks

The DDoS attack is a bit different from a normal DoS attack. In a DDoS attack,
there are multiple numbers of compromised hosts as compared to a single
compromised host in a normal DoS attack. An intelligent and lightweight
approach is required to prevent DDoS attacks in IoD. A pioneer work in
avoiding DDoS attacks in IoT using SDN is [106]. Unlike [105], the authors of
[106] propose an algorithm to detect and mitigate the DDoS attacks in drones.
Cosine similarity of the vectors of the packet-in message rate at software-
defined Internet of Things (SD-IoT) switch ports is used to determine the
occurrence of DDoS attack. The threshold values of the vector length and
cosine similarity are used to precisely and accurately classify an attack
situation. The simulation results demonstrate that the proposed algorithm is
capable of detecting the device used to launch the DDoS attack in a short span
of time. The results of the proposed work are compared with other state-of-
the-art work that try to detect DDoS attacks using IP filtering [113]. The
simulation results demonstrate that in case of a DDoS attack, the number of
flow table items of the SD-IoT switches, and the number of data packets
received by the SD-IoT controller are less in [106] as compared to [113].
However, a proactive scheme to defend and prevent the DDoS attacks is missing.
The proposed algorithm will work only after the DDoS attack has been launched.
The authors of [114] provide a lightweight solution to counter DDoS attacks
using SDN.

###  V-C Avoiding intentional disruption

Apart from DDoS attacks on a set of drones in the network, the network of
drones being resource-constrained is also susceptible to intentional jamming
and disruption attacks. Such attacks are more severe as compared to DDoS
attacks, as they can paralyze the entire network leaving no room for detection
and mitigation. Different from [105] and [106], the authors of [66] have
proposed an SDN-based framework for secure and robust data relaying in case of
intentional jamming and disruptions. In the proposed model, the drones act as
SDN switches that are controlled by a centralized SDN controller. A novel 3D
spatial coverage metric is used to calculate diverse multiple paths among the
drones to prevent the effect of intentional disruptions on the functioning of
the drone network, as the model gives the directives to the drone for using
the best possible path. The simulation results demonstrate that the proposed
algorithm outperforms the traditional shortest path and shortest multi-path
algorithms in terms of outage prevention [115]. The average end-to-end outage
rate in IoD is reduced by $18$% in the proposed model when compared to [115].
The algorithms in [115] consider only the path that takes the least time,
irrespective of the presence of jammers and intentional disrupts. Although the
proposed model in [66] succeeds in preventing the complete outage of the drone
network, the end-to-end delay is also increased by $12$% when compared to
[115]. The proposed model in [66] also helps in preventing the frequent link
disconnections between the devices linked to the network. Further work is
required to propose some algorithms that can prevent such intentional
disruptions without increasing the average delay of the network. The jamming
incident in Hong Kong drones could’ve been prevented if any of the above-
mentioned measures were taken [46].

TABLE VII: Applications of SDN for drone communication security.  Ref. |  Attack |  Mechanism |  SDN Feature used |  Major achievement |  Open issues
---|---|---|---|---|---
[105] |  DoS attacks |  The drone first registers itself with the NetFence ready receiver and then only the drone is allowed to transmit the data packets. |  Directly- programmable, Scalability |  The model has a very short average file-transfer time |  Implementing the model specifically for UAVs is much needed
[106] |  DDoS attacks |  The cosine similarity of the vectors of the packet-in message rate at the SD-IoT switch port is used to determine the attack |  Abstraction of network devices, Dynamic re- configuration of networks |  Can determine the device using which the DDoS attack is launched in a very short time span |  Implementing the model specifically for UAVs is much needed
[66] |  Jamming attacks, Disruption attacks |  Multiple paths are generated using a $3$D spatial metric which are directed to the UAVs to avoid the disruption |  Decoupled Data- plane and the Control plane |  The average end-to-end outage rate in the IoD is reduced to a large extent |  End-to-end delay increases significantly, which is a major area to be looked upon in the future
[107] |  DoS attacks, GPS Spoofing |  SDN controller authenticates the network device and then only the data is transmitted by the controllers |  Decoupled Data- plane and the Control plane |  Multiple SDN controllers are deployed to prevent the malfunctioning of the devices in the network |  The link between the Control plane and Data plane is still susceptible to attacks
[108] |  DoS attacks, Spoofing attacks |  The Middlebox-Guard (M-G) is deployed at different locations which manages the dataflow |  Directly- programmable, Flexible network architecture |  Latency and the maximum load on the device are reduced by $50$% |  The Integer Linear Program (ILP) pruning algorithm used in M-G has a high complexity
[116] |  DoS attacks, DDoS attacks |  The primary path forwards the common files whereas the backup path forwards the uncommon cases where the primary path is not reliable |  Decoupled Data- plane and the Control plane, Scalability |  It can handles link congestion with high bandwidth |  It has a very high end- to-end delay, which has to be looked upon in the future to make the algorithm more efficient
[117] |  DoS attacks, DDoS attacks |  SDN computes the optimal flow for each multi-path TCP and the Flow Deviation Method (FDM) algorithm is used to re-allocate the bandwidth |  Network- programmability, Decoupled Data- plane and the Control plane |  The model achieves fairer bandwidth allocation that provides better QoS and it makes the network more reliable |  Cannot support a high number of users and the model is not fully secure
[58] |  Grey hole attacks, Black hole attacks, DDoS attacks |  The UAV informs its controller about the neighboring drone while establishing OpenFlow connection and also informs about its update |  Decopuled Data- plane and the Contol plane, Scalability |  The amount of data exchange when compared with the AODV routing algorithm is increased by $2$% |  Further works on increasing the security of the model is needed
[118] |  GPS Spoofing |  Cluster heads are assigned to different densely populated sectors and the data is transferred through the cluster head only when in the range |  Flexible network architecture, Scalability |  The model provides faster and efficient coverage rate of about $99$% and a latency of around $20$% |  Need to decrease the latency to make the model more efficient

###  V-D Malfunctioning devices

Apart from DoS, DDoS, and intentional jamming, there are various other issues
in drone communication that are related to the different sensors deployed on
the drones. Traditional internet systems use IP and firewalls, which cannot
solve these issues, as it is not possible to fit all objects and protocols to
a common and singular protocol. A lightweight model for avoiding
malfunctioning devices in IoD is proposed in [107]. In the proposed model, the
SDN controller first authenticates the network device requesting to be
connected to the network. Only after successful authentication, the data is
disseminated to the connected devices using the controller that makes sure
that no malfunctioning of the device is taking place. Traditional network
protocols are not designed to support high levels of traffic, scalability, and
mobility. Hence, the use of SDN in this work increases the functionality of
the network by reducing the hardware complexity. SDN also has the ability to
extend the network security to the access end-point devices. Multiple SDN
controllers have been used instead of a single one to improve fault tolerance
and robustness. Unlike [107], the authors of [119] have proposed a similar
framework using only a single controller. If an attacker compromises the SDN
controller, he gains full control over the network. Hardware and software
failures may also occur, which pose a potential risk to the entire network.
The work in [107] is superior as it uses multiple controllers, so if one goes
down, another can take control to avoid a system failure in case of any
malfunctioning device. The proposed work reports increased network performance
with multiple controllers because each controller has a partial view of the
network and the controllers collaborate and exchange information with each
other. However, the link between the Control Plane and Data Plane of the SDN
is still vulnerable and susceptible to attacks and these issues are yet to be
resolved.

###  V-E Data Integrity

An SDN-based data security model Middlebox-Guard (M-G) is proposed in [108].
Different from [107], and [119], the M-G manages the dataflow to ensure
network efficiency and helps in minimizing the network latency. To reduce the
latency, the middleboxes are placed at locations where the communication link
is the shortest using a placement selection algorithm. The middleboxes are
placed in different locations and an offline Integer Linear Program (ILP)
pruning algorithm [120] is deployed at each middlebox. ILP helps in solving
uncontrollable computation optimization problems at every middlebox to tackle
the switch constraints, such as the use of CPU, RAM, etc. The ILP algorithm
also provides the optimum routes to be used for the data transfer. Also, an
online ILP is used to minimize the maximum middlebox load across the network.
M-G is compared to a model known as SIMPLE proposed in [121], as both solve
middlebox placement and route selection problems. M-G outperforms the latter
in terms of security, latency, and load. In [108], POX was used as the
controller, and OpenvSwitch was used as the SDN switch for carrying out the
experiments. On running the entire system, latency and maximum loads were
reduced by $50$%. In terms of security, middlebox failures and overload
conditions were analyzed, and the response times for these were calculated to
be less than $0.15$ seconds, which means, it shows a fast response.

Summary:  SDN can help in preventing many attacks that drones are susceptible
to, including DoS attacks and DDoS attacks, and can help in maintaining data
integrity in drones. SDN technology can also help in avoiding the intentional
disruption and jamming attacks that impose danger on drone communication. A
summary of the advantages and disadvantages of major works that use SDN as a
solution to drone communication security are described in Table VI.
Furthermore, Table VII summarizes the related works that use SDN in
maintaining security in drone communication. According to the best of our
knowledge, the decoupling of the control plane, the data plane, and the
network plane helps a lot in maintaining security standards in drone
communication.

##  VI Applications of Machine Learning for Drone Communication Security

Machine learning is the study of algorithms that are capable of learning and
improving automatically through experience, and can make accurate predictions
based on the data with which they are fed [122]. They can provide generalized
observations for unseen and unknown states and networks as well. Different
machine learning algorithms are useful in different drone applications and
domains. The use of specific ML algorithms is dependent on the domain, and
type of data available. The ML approaches have been extensively explored in
literature both for physical security of drones, and for drone communication
security. The physical security approaches basically deal with using different
ML algorithms to detect unauthorized drones or to prevent authorized drones
from entering into unauthorized zones. Both these types of security issues are
intrinsically related to each other. For example, if the system fails to
identify or detect an unauthorized drone and allows it to enter into a network
of authorized drones, it can easily allow all possible communication attacks
on the network. Therefore, drone detection using ML can be considered as a
preliminary step that can prevent the possibility of drone communication
issues to a great extent. The authors of [123] study different ML frameworks
and provide a model to prevent jamming attacks. A distributed learning
framework is essential to manage the various tasks in a swarm of drones [124].
Therefore, in this section, we review the various works that try to use
various ML algorithms to detect the drones or to identify and prevent the
generic security vulnerabilities. First of all, we discuss the issues with the
traditional approaches that do not use ML algorithms, and then we move on to
the challenges in drone communication and possible ML-based solutions.

There are some traditional techniques that do not use ML algorithms for
detecting drones. The most primitive technique is drone detection using radar.
Detection using radar is highly expensive, and it can be used only for
detecting large objects. Another model that can be used for drone detection is
using Light Detection And Ranging (LiDAR) that has been implemented in [125].
LiDAR sends the laser beam towards the object and analyzes the beams returned
after colliding with the object. However, LiDAR is also a extremely expensive
method for detection, and is highly vulnerable to climatic conditions.
Moreover, these techniques tend to give false positive results, thereby
resulting in wastage of resources.

We further discuss specific security issues that can be resolved and prevented
using ML as a solution.

###  VI-A Drone detection using SVM and CNN

ML algorithms can be used in radar detection to address various detection and
classification problems associated with the traditional methods of radar
detection [40]. The authors discussed different SVM models to classify the
detected objects as drones or birds, classify different kinds of drones
depending on payload or number of rotors. These models showed high accuracy on
test data (>$90$%). An efficient drone detection model using the Support
Vector Machine (SVM) and Convolutional Neural Networks (CNN) for noise
detection is discussed in [126]. Unlike [67], the authors of [126] use SVM and
CNN for drone detection as compared to LSTM approach used in [67]. The data
was collected using audio from $6$ listening nodes to listen to the UAV flown
[126]. Both types of ML algorithms have their own pros and cons. The SVM-based
models are easier to implement as compared to other deep learning algorithms
such as LSTM. However, the SVM-based models are only suitable for small
datasets with limited outliers. In the proposed model, multiple listening
nodes and a control center are used. The listening nodes are deployed on a
circle surrounding the protected area. A microphone is installed on the
listening nodes, to detect the sound of the drone. After the detection, the
modules per frame are computed, and are sent to the control center for further
evaluation. At the control center, SVM is deployed. SVM is a supervised
classifier that helps in classifying between the required entities by mapping
the input vectors into a high-dimensional feature space [127]. This classifier
plots the pattern of the frames and the sound that is sent to the control
center, and plots whether the drone is detected or not. The simulation results
in [126] demonstrate that the SVM algorithm is more efficient than CNN in
detecting drones. However, the main limitation is that these algorithms have
noise-related issues that make the results inconsistent. Moreover, the signals
were not normalized in the proposed model leading to a lot of outliers.

TABLE VIII: A summary of advantages and disadvantages of major applications of ML for drone communication security. | Major
---
Approaches
Advantages | Disadvantages |  | Benefits over
---
traditional approaches
[126], [128] |  | • Supports high computation speed
---
and is cost efficient
| • Unwanted noises in the background
---
makes the results inconsistent
Efficiency
[129], [130] |  | • Model training time is very less
---
and gives high accuracy
| • Generating such a large dataset
---
artificially is very difficult
| Identification, Classific-
---
ation of types of drones
[131] |  | • Stores data sequentially, so the data
---
retrieval latency is very less
| • The latency in data transmission
---
increases when a very large data file
is transmitted
| Minimizing latency,
---
Data reliability
[132] |  | • High reliability with very fewer
---
resource requirements
| • Fails in further classifying the type
---
of attack
| Detecting and preventing
---
DoS attacks
[133] |  | • Very lightweight model, can run
---
on Raspberry Pi $3$B
| • A lot of training and testing in Deep
---
Learning algorithms may be required
| Easy deployment,
---
Privacy
[134] |  | • Can detect the adversary in GPS-
---
denied environment with great
accuracy
| • Fails when the drone moves in an
---
irregular pattern and is sensitive to
lighting conditions
| Efficiency in GPS-
---
denied environment

###  VI-B Drone detection using RNN and CRNN

An efficient model using deep learning techniques like Recurrent Neural
Networks (RNN) and Convolutional Recurrent Neural Networks (CRNN) for drone
detection is mentioned in [129]. The authors of [129] have acquired a large
dataset of drone propeller audio data, and have overlapped the audio clips
with a variety of background noises to mimic real-life scenarios. Data
labelling was done for the identification problem as unknown (random noises in
the surrounding), Bebop (drone $1$), or Mambo (drone $2$), and for the
detection problem as drone or not a drone. The experiment has been divided
into two categories, the first, targeting detection of drones and the second,
targeting their identification based on type. The detection problem has been
evaluated and compared with existing literature, and the mentioned algorithms
have been compared based on their accuracy, F1 score, precision, recall
metrics, and computational time required to train and test the model. The
experiment results of the model in [129] show that deep learning methods using
drone acoustics show great effectiveness for drone detection. CNN and CRNN
algorithms remarkably outperform RNN in both detection and identification.
Although CNN showed better performance than CRNN, the difference in
performance was negligible, and CRNN required significantly less time to
train, making it the most practical choice. Another model as discussed in
[130] uses CNN for drone detection, but uses images instead of drone
acoustics. Although the results are promising, the dataset for such a study
could only be artificially created which decreases its reliability, and
identification of the specific type of drone is also not possible like it is
in [129]. The authors of [135] have noted that RNN achieves superior
performance when compared to CNN. The discrepancy is attributed to differences
in the model’s architecture and design parameters, but a direct comparison of
the results could not be performed by the authors of [129].

###  VI-C Fault detection and Recovery of UAV data using LSTM

UAVs are used for certain critical applications like military, and product
delivery. Therefore, it is imperative to deploy a certain mechanism for making
data transmission in UAVs ultra-reliable. Furthermore, the latency of data
transmission should also be kept minimum. Being resource-constrained, the UAVs
need to transmit real-time data to the cloud servers for storing. Pioneer
works in the direction of minimizing the latency and increasing the data
reliability using LSTM are [67], [131]. Unlike [129], the authors of [67],
[131] use LSTM for drone communication security. LSTM networks are a special
type of RNN networks with some special features. The main feature of LSTM over
RNN is the presence of a ’memory cell’ that can maintain information in memory
for a long time. In the proposed model, firstly, a regression model using LSTM
is built to extract spatial-temporal features of the drone data. This is done
to get an estimate of the monitored parameters or features. The authors use a
set of $11$ distinct parameters or features, like roll angle, altitude,
indicated airspeed, etc., to sense the UAV’s current attitude and position
through airborne sensors as an input to the proposed model. The output is used
to train the fault detection model after the normalization of the data. Next,
various filters are used to reduce the difference between the actual data and
estimated values, thereby removing the effects of various noises. A threshold
value is compared with the estimated values to detect the faults. In case a
fault in data is discovered, the faulty data is replaced with the data
estimated by the proposed model or the recovery data. The simulation results
demonstrate that the proposed model is capable of providing a quick recovery
of the data in a limited time. The experimentation shows that the Mean Square
Error (MSE) was recorded to be less than $0.078$ whereas, the Mean Absolute
Error (MAE) was less than $0.205$. However, further work on increasing real-
time data recovery may be done to make the model more accurate and effective
in fault detection and recovery.

###  VI-D DoS attacks

The authors of [136], and [137] have proposed an ML-based model to detect
Denial of Service Attack using Neural Networks and Modified Support Vector
Machines respectively. However, a pioneer model for detecting and preventing
DoS attacks in IoD using machine learning is proposed in [132]. Unlike [126,
129, 131], the authors of [132] focus on preventing the DoS attacks on drone
data, rather than physically detecting the unwanted drones in the network. The
dataset consists of labeled data categorized as Benign for normal traffic, and
attacks like brute force, DoS/DDoS, and web attacks. The authors proposed and
implemented the random forest algorithm [138] and multi-layer perceptron
algorithm [139] on the CIC IDS $2017$ dataset. CIC IDS $2017$ dataset consists
of all the data of current attacks, such as DoS and DDoS, in pcap format. The
incoming data traffic in the drone is classified using the deployed
classification algorithms to be as benign or affected packet. In both of the
models, an accuracy greater than $98$% was achieved with the MLP achieving an
accuracy of $98.87$% with $30$% training records and the RF algorithm
achieving an accuracy of $99.95$% with $50$% training records. However, none
of the previous works including [136] and [137] could achieve such an accuracy
level with a relatively low resource requirement, as desired by an IoD system.
The further task is to test the system for the multi-classification of DoS
attacks. Moreover, the model does not further classify into attacks such as
Hearbleed, slowhttptest, and http flood. Also, the resources required can be
further reduced to make the system more efficient by reducing the number of
features.

TABLE IX: Applications of ML for drone communication security. Ref. |  Attack |  Mechanism |  Machine Learning Feature used |  Major achievement |  Open issues
---|---|---|---|---|---
[126] |  GPS spoofing |  The sound of the drone is used for classifying the presence of drone using SVM and CNN |  SVM and CNN classifies whether the drone is present in the specified area or not |  SVM shows better results in detecting the UAV as compared to CNN |  The background noises of the wind and the surroundings gave inconsistent results
[129] |  GPS spoofing |  The algorithms like RNN are used to identify the presence of UAV on the basis of sound |  Algorithms like RNN and CRNN is used to classify the presence of the drone |  CRNN showed the best results in detecting the presence of the drone |  The model can be trained to detect the wide class of drones
[67] |  DoS attack, Worm- hole attack |  The LSTM-based fault detection model detects the fault and the quick recovery commands are sent to the UAV |  LSTM is used to store the previous data of the UAV which helps in building the model that efficiently detects the fault |  The model achieved very less MSE and MAE, which makes the model very efficient |  Work on increasing the efficiency of the model is much needed
[132] |  DoS attack |  The Random Forest and Multi-Layer Perceptron algorithm classifies the data packets received as benign or the DoS affected packets |  Random Forest and Multi- Layer perceptron algorithm is used to classify between the affected and the non- affected packet received by the drones |  The MLP algorithm achieved an accuracy of $98.87$% whereas the RF algorithm achieved an accuracy of $99.95$% |  The model does not classify the type of attack taking place and work on decreasing the latency is needed
[133] |  DoS attack, DDoS attacks |  The data received made obscure by adding some noise and CNN is used to reconstruct the obscured image by using different weights |  CNN algorithm is used to reconstruct the obscured data by using some random weights, hence making the data secure. |  The model re- constructed the obscured data with an accuracy of $81.3$% and it can run on R Pi $3$B as well |  Research is open for working on increasing the accuracy and the efficiency of the model
[134] |  GPS spoofing, DoS attack |  The target drone and the size of the drone is detected using bounding box object detection algorithm |  The bounding box object detection algorithm and the YOLO detection algorithm is used for the real-time detection of the drone |  This model achieved $77$% accuracy in detecting the target drone with an average frame rate of $5.22$ fps |  The hunter drone is inefficient because of its heavy weight
[140] |  Jamming, Black hole attacks |  Whenever any event is detected by the UAV, the information is sent to the controller and IDS identifies the malicious node |  A hierarchical intrusion detection system is used for detecting the malicious nodes that are injecting false data |  The model achieved a detection rate of more than $93$% and a false positive rate of less than $3$% |  Implementing the model on the swarm of drones is a much needed work
[141] |  GPS spoofing |  A machine learning-based naive Bayes algorithm is used to check for the presence of the UAV and the classification is done using the k-nearest neighbor algorithm |  The naive Bayes algorithm is used for the detection of the micro-UAV and for the classification of the micro- UAV kNN and is used |  The confusion matrix obtained for the kNN classifier in the model achieved and accuracy of $97.1$% |  This model can make use of a $3$D feature cluster map that would help improve the real- time classification
[142] |  Jamming attacks, DoS attack |  UAVs are used for data transmission and intrusion detection system is used for detecting any anomaly |  An intrusion detection system is used for the detection of the anomaly in the network |  An efficient way of securing the multi-level ad hoc networks is presented |  Other networking solutions can be used to make the model more efficient
[143] |  DoS attack, DDoS attacks |  Devices selected in white list using the algorithm are only used for data transmission |  Random Forest algorithm is used for classifying the connected devices as legitimate devices or malicious devices |  The model showed an accuracy of $99.49$% in detecting the un- authorized device in the network |  The efficient detection of a variety of compromised drones can be worked upon in the future

###  VI-E Privacy Leakage

In the case of drones, the authentication algorithms used to enable access to
the network are generally cryptographically placed. However, recently, the use
of machine learning algorithms to avoid privacy leakage in IoD networks is
being explored [88]. For avoiding privacy leakage in drones, a pioneer model
is proposed in [133]. Different from [132], the authors of [133] focus on
using deep learning algorithms to proactively secure the data, rather than
detecting attacks on the network. The authors use deep learning auto-encoders
to secure sensor data and to store media on a server. Each bit of data
collected from the sensors of the drone is first converted into a digit image
of size $28$ by $28$ pixels. Further, some noise is added to the sensor data
to make it obscure and it is then sent to a remote cloud server to be saved.
Convolutional Neural Network (CNN) is implemented in the reconstruction and
classification components. Reconstruction component is used for reconstructing
the obscured data into the original data by training the model weights. As the
model weights are not known, it becomes almost impossible for the adversary to
retrieve the original data. Next, the classification component recognizes the
data from the reconstructed data and the digital data is further converted to
sensor data by using deep learning auto-encoder. The proposed model is such a
lightweight model that it can be run on Raspberry Pi $3$B too. The model was
tested on the MNIST dataset, and the results demonstrate an accuracy of
$81.3$% in identifying the reconstructed data. In general, data privacy is
ensured by encrypting the data with a variety of cryptographic
representations. However, further techniques are required as the privacy
techniques using cryptographic keys can be broken once the key is obtained.
Another technique that helps in preventing data leakage is Homomorphic
authenticated encryption (HAE) [144]. Unlike [133], the [144] model works
without the use of a key. HAE allows the users who do not have a key to
perform computation on the ciphertext. The computed ciphertext decrypts to the
correct function value.

###  VI-F Adversarial attacks

Adversarial machine learning is mainly used to cause a malfunction in a
machine learning model by supplying deceptive inputs. The IoD brings in a vast
range of sensors, mobile network security issues, and privacy-protecting
challenges that are different from traditional internet systems. A model
proposed for avoiding adversarial attacks has been proposed in [145] that uses
CNN and RNN for adversary detection. Similar to [129, 128], the authors of
[145] use the RNN and CNN-based models. However, these models are used to
detect and prevent adversarial attacks rather than detecting the presence of
drones, as done in [129, 128]. A pioneer work in detecting and preventing
adversarial attacks in IoD is [134]. The authors of [134] use a black and
white version of the Tiny You Only Live Once (YOLO) detection system and
visual-serving without motion capturing systems. The proposed techniques are
even efficient in a GPS denied environment. The proposed model is presented
using a drone hunting platform that self localizes using visual inertial
odometry (VIO) through ZED stereo camera, and runs a visual tracking and
identifying algorithm on Jetson TX$2$. The commands are sent by the algorithm
to the PX-$4$ based flight controller. The simulation results demonstrate that
the platform could effectively track and chase the adversary. The model
achieved $77$% accuracy with an average frame rate of $5.22$ fps. The proposed
work runs significantly faster than other deep learning detection models as
mentioned in [145] and [146] with comparable accuracy. Also, it works to
detect the adversary in a GPS denied environment, which is not done in other
previous works in this direction. However, the fundamental drawback in the
proposed model is that the detection algorithm is sensitive to poor lightning.

Summary: The objective here is to enhance the possibilities of adversary drone
detection using various machine learning and deep learning approaches. Apart
from drone detection, various works have also focused on using such algorithms
to prevent attacks in drone communication network. A summary of advantages and
disadvantages of major works is shown in Table VIII, and a summary of the
related works is given in Table IX. As seen, ML algorithms have a high
capability in detecting unwanted drones and preventing the drones from
entering restricted areas. These algorithms are also being widely proposed for
secure traffic management and prevention of mid-air collisions.

##  VII Applications of Fog Computing for Drone Communication Security

Fog computing is a powerful complement to cloud computing which can provide a
better QoS and can also help in decreasing the security issues in the cloud
computing system. It is difficult to connect such a large number of drones
directly to the cloud due to high latency delays and unpredictable network
connections. Connections between the drones and the fog layer can be easily
established with low latency issues. The most important benefit of fog
computing is that it does all the computations and keeps the data near to the
drone, which keeps it more safe and secure. Fog computing also supports
mobility, scalability, heterogeneity as well as platform-independence. Concept
of edge computing comes close to fog computing and is said to overlap to a
great extent [147]. Edge computing is for moving the resources from the cloud
towards the edge of the network, and is more focused towards the ’things’
side. However, fog computing concerns itself mainly with the infrastructure.
In this section, we first of all discuss the basic issues with the traditional
approaches that do not use fog computing and then we move on to the challenges
in drone communication and possible fog computing based solutions.

There are various traditional methods that help in securing drone
communication without leveraging the benefits of fog computing. A traditional
man-in-the-middle attack detection system has been proposed in [148], which
uses the precise timing of arrival of data packets to infer the possibility of
the attack. If the packet arrives late than the expected threshold time, the
possibility of the attack is inferred. This method can fail in several
circumstances where heavy background noise is present as the arrival of the
data packet highly depends on the transmission channel. Bamasag et al. in
[149], proposed a multicast authentication model for data transmission in the
desired time interval. This model makes the use of Shamir’s secret sharing
technique [150] in which the secret can be unlocked if the authenticator has
enough number of shares. Although this method provides some reliability,
storing such a large number of keys is not preferred considering the resource
constrained nature of drones.

We further discuss the list of specific security issues that can be resolved
and prevented using fog-computing as a solution.

TABLE X: A summary of advantages and disadvantages of major applications of Fog Computing for drone communication security. | Major
---
Approaches
Advantages | Disadvantages |  | Benefits over
---
traditional approaches
[151] |  | • High performance and accuracy as the
---
spoofing attack can be detected from
$10$ meters and in $250$ milli-seconds
| • Cannot efficiently avoid collisions
---
and also sometimes fails in
detecting the obstacles
| Easy deployment,
---
Confidentiality
[152] |  | • A very high efficiency and a low
---
resource-demanding model
| • Highly dependent on network’s
---
latency which demands more
optimization
| Security against man-
---
in-the-middle attacks
[153] |  | • Identity-based authentication enhances
---
end-to-end security between the edge
layer and the fog layer
| • The data processing time highly
---
varies with the configuration of the
device used for detection
| Authentication, Data
---
integrity, Non-repudiation
[68] |  | • The architecture covers all the three
---
aspects i.e. minimizing the latency and
the energy consumption and maximizing
the reliability in the drones
| • The architecture is not very fast
---
and future work is needed for
increasing the efficiency of the
architecture
| Better performance than
---
LRGA-MIE and LP-based
algorithms
[154] |  | • The model decreases the latency
---
experienced by the drone and increases
the Quality-of-Experience (QoE)
| • Highly sensitive to the number
---
of drones
| Low latency, High
---
QoE

###  VII-A GPS spoofing attacks

UAVs in the fog environment are susceptible to a lot of challenges against its
benefits like mobility, scalability, and accurate location tracking. GPS
spoofing is a notable security breach attack that sends incorrect GPS
information to the receiver. UAVs need special attention since traditional
internet systems like cloud computing in the former causes latency overloads
and unforeseeable network issues. In the past, there are various GPS spoofing
detection methods that have been adopted. The major ones are detection based
on cryptographic algorithms and using auxiliary equipment as mentioned in
[151]. The authors take flight security and safety of drones, acting as fog
nodes in an airborne fog computing system, in consideration. The model uses
visual sensors combined with IMU (Inertial Measurement Unit) for information
fusion to solve GPS spoofing issues. Using DJI Phantom $4$ with a frame rate
of $30$ fps, it was observed that the spoofing attack can be detected from
$10$ meters and in $250$ milli-seconds. The authors of [155] propose a fog-to-
cloud computing framework for a Dragnet based amateur drone surveillance
system. GPS spoofing and jamming attacks can be detected using the framework,
which is inspired from traditional military anti-drone technologies. The
amateur surveillance system is empowered with a "brain" for high-level
intelligence consisting of a fog-to-cloud model. It is a system of coordinated
measures for sensing a spoofing attack on the system by global decision-making
based on the actions on the amateur drones. The Kashmar incident as mentioned
in [38] could’ve been prevented if US had employed some of the frameworks of
fog computing as mentioned above.

###  VII-B Man-In-The-Middle attack

Man-in-the-middle attack problem threatens the very demanding aspect of IoD,
which is integrity, as mentioned in [156]. A pioneer work in the direction of
low resource demanding model with a high level of security to prevent man-in-
the-middle attack in IoD is proposed in [152]. The authors propose an
intrusion detection system (IDS) and intrusion prevention system (IPS) for
preventing man-in-the-middle attack at the fog layer. Although, the model is
proposed for IoT devices in general, it can be easily implemented on IoD as
well. In the proposed network model, IDS nodes are deployed at a one-hop
distance. Whenever an IDS node finds a compromised node or an intruder, it
simply indicates the nodes in its proximity to cut off connection with the
compromised node. On deployment, IDS nodes acquire the key from the cloud and
distribute it to fog nodes. To prevent intrusion, all the packets are
encrypted using Advanced Encryption System (AES), and Diffie-Hellman key
exchange [157] is used for key exchange. IDS nodes periodically interrogate
the fog nodes and observe the receiver’s behavior. IDS nodes expect the
receiver to decrypt the packet in some pre-defined time. If the round trip
time of interrogation exceeds the pre-defined time, the IDS concludes that the
fog node is malicious. Additionally, if an attacker knows the existence of IDS
nodes and the protocol they are using, he still does not know the nature of
interrogation which is pre-programmed before the deployment of the nodes. This
further reduces the chances of the attack. The proposed model, when
implemented at the fog layer, could help in the identification and prevention
of the man-in-the-middle attacks so that manipulated information does not
reach the cloud. The simulation of the model was done over OMNET++. Latency
overhead for deploying IDS and IPS was $40$ milli-seconds. Time taken to
detect an attack was discovered to be between $2.48$ seconds to $2.53$
seconds. Since $2$ seconds was the time between investigation sessions, actual
discovery time was approximately $0.5$ seconds. Energy overhead incurred on
the fog nodes by IDS nodes was negligible which makes it a very less resource-
demanding model for detecting man-in-the-middle attack. However, in the
proposed model, the investigating-time is inversely proportional to the
network’s latency and energy overhead of the IDS network model. Further work
is required in the direction of optimization algorithms to improve the
efficiency of the proposed model.

TABLE XI: Applications of Fog computing for drone communication security. Ref. |  Security Issues |  Mechanism |  Fog Computing Feature used |  Major achievement |  Open issues
---|---|---|---|---|---
[151] |  GPS spoofing, Eavesdropping |  The visual sensors combined with IMU are deployed on the drone and the data is transmitted to the fog layer detecting the attack |  Distributed computing, Scalability |  The spoofing attack can be detected from $10$ meter far and in $250$ milli-seconds. |  Methods for avoiding mid-air collisions can also be implemented
[152] |  Man-in-the- middle attacks |  The malicious data is prevented from entering the cloud layer as the attack is detected in the fog layer by using the cryptographic keys |  High computation power |  The model is very less resource-demanding and can detect the attack in less than $0.5$ secs |  Future work on increasing the efficiency of the model is much needed
[153] |  Eavesdropping, Man-in-the- middle attacks, Hijacking |  The drone gets authenticated from the fog layer that contains the hashing algorithm, and then only is allowed to transmit data |  Low latency, Mobility, Heterogeneity |  The average end-to-end processing time was $2.59$ secs and the average overall response time was $3.17$ secs |  The average overall response time highly varied with the number of devices
[68] |  Latency, Resource constraints |  The task is divided into several small task using ADMM algorithm and is transmitted to the nearby ready drones that completes the task and transmit back the result acting as a fog node |  Low latency, High computation power |  The model showed positive results in minimizing the latency and the energy consumption and maximizing the reliability in the drones |  Future work is required in making the model more reliable and safe for the drones
[155] |  GPS spoofing |  The surveillance devices acting as a fog layer sends the data to the cloud layer and gets the result back and transmits to the amateur drone for implementation |  High computation power, Distributed computing |  The model detects the authorized drone with a greater probability than detecting the false or unauthorized drone |  To efficiently detect the drone, a high detection delay is expected from the model which should be reduced
[158] |  DoS attacks, DDoS attacks |  The serial number of each device is stored in the fog layer and whenever any device wants to communicate with the other device it needs to verify its serial number with the fog layer |  Low latency, High computation power |  The model uses very less bandwidth and increases the security in the IoT devices as no device can communicate without authentication |  The model can be implemented on the drones specifically to increase their security

###  VII-C Eavesdropping

Eavesdropping is an attack that affects the confidentiality of the data in the
drones [156]. Classical security solutions exist such as Secure Sockets Layer
(SSL) [159], but it cannot be implemented on drones as they lack enough memory
and CPU power to perform the required cryptographic operations. Therefore,
offloading the additional security operations to a more resourceful entity
such as fog nodes is a promising solution. A model that addresses this problem
in drones is proposed in [153]. Fog Security Service (FSS) mechanism is
proposed that uses public and private key cryptography schemes. It consists of
a Verifier, PKG (Private key generator), and a hashing algorithm at the fog
layer. In the proposed model, input security parameters that include an
identifier (unique), username, and password for verification of the sender are
assigned to every drone. PKG is used for communication between the fog layer
and the edge layer. After node authentication, asymmetric encryption is used
for getting symmetric keys from the fog layer. For public-key encryption, the
Rivest-Shamir-Adleman (RSA) algorithm [160] is used. Nonce values are also
used for preventing play-back attacks. FSS provides identity-based
authentication as the private key is used for encryption and decryption both.
This enhances the end-to-end security between the edge and the fog layer. For
IoD networks, ground access points along with UAVs are present [161].
Therefore, installing the proposed FSS layer along the data transmission paths
could identify and prevent eavesdropping problems in the drones. OPNET based
network simulator is used to evaluate the proposed method. In addition to
different traffic loads, several devices representing different capacities and
resources were used for experimentation. The performance of the model was
evaluated based on the response time. The average E2E processing time was