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A Systems Analysis of Energy Usage and Effectiveness of a Counter-Unmanned Aerial System Using a Cyber-Attack Approach

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Abstract

Existing counter-unmanned aerial system (C-UAS) defensive mechanisms rely heavily on radio frequency (RF) jamming techniques that require a large amount of energy to operate. The effects of RF jamming result in undesirable consequences, such as the jamming of other nearby friendly radio devices as well as the increase in RF footprint for local operators. Current cybersecurity analysis of commercial off-the-shelf (COTS) UASs have revealed multiple vulnerabilities that give rise to opportunities to conduct C-UAS operations in the cyber domain. This is achieved by performing cyber-attacks on adversarial UASs through hijacking the device-specific communication’s link on a narrow RF band and without the need for broad-spectrum RF energy bursts during C-UAS operations, which can result in lower energy usage to accomplish the same outcome. This article validates the cyber-attack C-UAS (CyC-UAS) concept through reviewing recent C-UAS operational experimental scenarios and conducting analysis on the collected data. Then, a simulation model of a defense facility is constructed to analyze and validate specific mission scenarios of interest and several proposed concepts of operation. A comparison of the energy requirements between CyC-UAS and existing C-UAS techniques is performed to assess energy efficiency and trade-offs of different C-UAS approaches. In this article, the comparison of energy requirements between the CyC-UAS prototype and existing C-UAS products that utilize RF jamming methods reveals that CyC-UAS achieves significant energy savings while not affecting other telecommunication devices operating at the same frequencies. While both the C-UAS techniques adopt the denial-of-service strategy, the CyC-UAS is able to achieve the same mission by consuming much less energy. Therefore, the CyC-UAS concept shows promise as a new, lower energy, and lower collateral damage approach to defending against UAS.
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Citation: Lee, C.H.; Thiessen, C.;
Van Bossuyt, D.L.; Hale, B. A
Systems Analysis of Energy Usage
and Effectiveness of a Counter-
Unmanned Aerial System Using a
Cyber-Attack Approach. Drones 2022,
6, 198. https://doi.org/10.3390/
drones6080198
Academic Editor: Pablo
Rodríguez-Gonzálvez
Received: 2 June 2022
Accepted: 4 August 2022
Published: 9 August 2022
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4.0/).
drones
Article
A Systems Analysis of Energy Usage and Effectiveness
of a Counter-Unmanned Aerial System Using a
Cyber-Attack Approach
Chee Hoe Lee 1, Christian Thiessen 2, Douglas L. Van Bossuyt 1,* and Britta Hale 3
1Department of Systems Engineering, Naval Postgraduate School, Monterey, CA 93943, USA
2Department of Information Science, Naval Postgraduate School, Monterey, CA 93943, USA
3Department of Computer Science, Naval Postgraduate School, Monterey, CA 93943, USA
*Correspondence: douglas.vanbossuyt@nps.edu
Abstract:
Existing counter-unmanned aerial system (C-UAS) defensive mechanisms rely heavily on
radio frequency (RF) jamming techniques that require a large amount of energy to operate. The effects
of RF jamming result in undesirable consequences, such as the jamming of other nearby friendly
radio devices as well as the increase in RF footprint for local operators. Current cybersecurity analysis
of commercial off-the-shelf (COTS) UASs have revealed multiple vulnerabilities that give rise to
opportunities to conduct C-UAS operations in the cyber domain. This is achieved by performing
cyber-attacks on adversarial UASs through hijacking the device-specific communication’s link on a
narrow RF band and without the need for broad-spectrum RF energy bursts during C-UAS operations,
which can result in lower energy usage to accomplish the same outcome. This article validates the
cyber-attack C-UAS (CyC-UAS) concept through reviewing recent C-UAS operational experimental
scenarios and conducting analysis on the collected data. Then, a simulation model of a defense facility
is constructed to analyze and validate specific mission scenarios of interest and several proposed
concepts of operation. A comparison of the energy requirements between CyC-UAS and existing
C-UAS techniques is performed to assess energy efficiency and trade-offs of different C-UAS ap-
proaches. In this article, the comparison of energy requirements between the CyC-UAS prototype and
existing C-UAS products that utilize RF jamming methods reveals that CyC-UAS achieves significant
energy savings while not affecting other telecommunication devices operating at the same frequencies.
While both the C-UAS techniques adopt the denial-of-service strategy, the CyC-UAS is able to achieve
the same mission by consuming much less energy. Therefore, the CyC-UAS concept shows promise
as a new, lower energy, and lower collateral damage approach to defending against UAS.
Keywords:
unmanned aerial system; counter-unmanned aerial system; C-UAS; cyber-attack;
cybersecurity
1. Introduction
Current counter-unmanned aerial systems (C-UASs) used against smaller unmanned
aerial systems (UASs) rely largely on radio frequency (RF) jamming and denial-of-service
(DoS) against adversarial UAS [
1
]. C-UAS used on installations, for example, realize this via
RF jamming or communication link jamming. However, this paradigm not only contradicts
well-established tactics, techniques, and procedures (TTPs) for defense of installations and
bases, but it also underutilizes potential cyber-attack C-UAS (CyC-UAS) measures [2,3].
In addition, current UAS defense mechanisms rely heavily on DoS (either jamming,
laser, or device destruction) [
4
]. RF jamming via energy bursts and laser mechanisms
requires enormous amounts of energy, which necessarily affects usage for expeditionary
forces or in energy constrained environments [
5
]. Furthermore, undesirable consequences
such as jamming of nearby friendly devices, increased RF footprint for local operators,
and unintentional loss/destruction of the adversary UAS may occur [6,7].
Drones 2022,6, 198. https://doi.org/10.3390/drones6080198 https://www.mdpi.com/journal/drones
Drones 2022,6, 198 2 of 25
In contrast, cybersecurity analysis of low-cost UASs has pointed to many vulnerabili-
ties ripe for exploitation that would provide a C-UAS with both energy improvements and
scalpel-edge accuracy in defense mechanisms, such as through cyber-attack hijacking the
adversary UAS or forms of jamming that utilize the device-specific communication link
frequency band instead of broad-spectrum RF energy bursts, and therefore have highly
controlled effects [2,8,9].
In recent studies, the application of cyber-attacks in the C-UAS domain has indicated
both energy improvements and scalpel-edge accuracy in defense mechanisms [
10
], such
as through cyber-attacks to hijack adversary UAS, or in the form of jamming that uti-
lizes device-specific communication link frequencies instead of broadband jamming, and
therefore achieves highly controlled effects on the malign device [2].
Techniques used to employ existing C-UAS by the military, state governments, fed-
eral agencies, and private companies consume high levels of energy during operation.
Certain
C-UAS
techniques such as frequency jamming may not always be suitable in an
environment where operating machines utilize RF transmission for communication, such
as a military airbase, a major sporting event, or anywhere in a crowded urban area [
11
].
The US Navy, Department of Defense (DoD), civilian airports, sporting venues, wildland
firefighters, and other facilities and users that may be targets of adversarial UAS may
benefit from the research presented in this paper.
This paper performs comparisons of the energy consumption of existing C-UASs
versus a proposed CyC-UAS. Further, this research analyzes the effectiveness of CyC-UAS
versus existing C-UAS approaches. Through the attainment of energy readings extracted
from the conduct of physical experiments with a CyC-UAS prototype [
10
], as well as
the comparison of energy consumption between existing C-UAS methods and CyC-UAS,
the results indicate that CyC-UAS can significantly reduce C-UAS energy consumption
and can serve as a useful portion of a broader C-UAS defense strategy for many types of
installations and expeditionary situations.
The remainder of this paper contains the following: Section 2surveys existing literature
to identify threats that arise from the use of UAS to motivate the need for C-UAS. Section 3
presents a literature review of existing available C-UASs to determine (1) concept of
operations (CONOPS), (2) capabilities and limitation, and (3) specifications. Section 4
presents a literature review and study of current developments of CyC-UAS with specific
focus on energy consumption and effectiveness, and reviews a recent CyC-UAS experiment.
Then, we provide an analysis of data collected in several experimental scenarios for the
conducting of CyC-UAS operations where data on the physical behavior of the CyC-UAS
system and adversarial UASs are documented. In Section 5, a simulation model of a
defense facility is constructed to analyze and validate specific mission scenarios of interest
and proposed CyC-UAS CONOPS. In Section 6, comparison of the energy requirements
between CyC-UASs and existing C-UAS techniques are performed to assess the energy
efficiency of CyC-UASs. Finally, the paper concludes in Section 7with a discussion of the
results and broad conclusions, recommendations, and future work.
2. UAS Threat Analysis and Vulnerability Assessment
The use of UASs in the military domain has produced enormous advantages and
benefits in military operations [
12
]. Such military operations include electronic warfare
attacks, precision strikes, intelligence, surveillance, and reconnaissance (ISR) missions,
and resupply missions [
13
,
14
]. The effectiveness of UASs was proven and validated
during military operations such as Operation Iraqi Freedom and Operation Enduring
Freedom [15,16]
, and, more recently, the military conflict between Ukraine and Russia [
17
].
In the commercial domain, the use of UAS to fulfill recreational or leisure purposes, such as
imaging and video capturing for social events, has further expanded into businesses across
different industries. Businesses have integrated the use of UAS to transform daily tasks [
18
].
For example, some insurance companies have adopted UASs to perform inspection of
damaged assets for claims, and in the farming industry, farmers use UASs to monitor crops
Drones 2022,6, 198 3 of 25
in the field to achieve labor savings [
19
,
20
]. The commercial sector within the United States
has been investing heavily in UAS development over the years, due in part to the positive
economic growth in UAS-related patents. A study conducted by Mckinsey and Company
suggests that by 2026, the usage and investment in UASs in the commercial sector will reap
a profit between USD 31 billion and USD 46 billion [
21
]. The upward trends suggest that
the utility of UASs will continue to gain popularity among consumers and that the use of
UASs for industrial and defense applications will continue to expand and grow.
2.1. Malicious Use of UASs
On the other hand, with the ease of access to small commercial off-the-shelf (COTS)
UASs through the commercial market, organized crime and terrorist groups have started
to adopt UASs to conduct malicious activities [
22
]. These activities include the illegal
intrusion of UASs into restricted infrastructure, such as the civil airport facilities with the
intent of disrupting the services and operations. For example, the Gatwick Airport situated
in London largely stopped flight operations between 19 and 21 December 2018 due to
a deliberate UAS attack that affected about 140,000 passengers, with about 1000 flights
diverted or canceled [
23
]. Terrorist groups such as the Islamic State (ISIS) were found to be
using weaponized UASs on the battlefield in Iraq and elsewhere [
24
]. Many of the UASs
that ISIS and other terror organizations have employed are weaponized COTS UASs where
explosives or munitions have been attached to an otherwise consumer-grade UAS [
25
].
These malicious attacks coupled with the rapid growth of UASs in the commercial and
military domains pose significant challenges and concerns to safety and security within the
civil and military domains [26].
2.2. Classification of UASs
Different classes of UAS are grouped based on the designed “max gross take-off
weight (MGTOW)”, “maximum operating altitude”, and “top speed”, as shown in Table 1.
Typical COTS UASs that are readily available for procurement in the commercial market are
relatively smaller in size and lighter in weight, and often fall under the Group 1 category.
Table 1. UAS groupings based on weight, operating altitude, and top speed. Source: [27].
UAS Group Weight Range (Lb.)
MGTOW Nominal Operating Altitude Speed (Knots) Representation UAS
Group 1 0–20 Less than 1200 above ground
level (AGL) 100
Raven (RQ-11), WASP DJI
Phantom, Solo, Typhoon H,
Ghostdrone 2.0
Group 2 21–55 Less than 3500 AGL Less than 250 ScanEagle
Group 3 Less than 1320 Less than flight level (FL) 180 Less than 250 Shadow (RQ-7B)
Tier II/STUAS
Group 4 More than 1320 Less than flight level (FL) 180 Any
Fire Scout (MQ-8B, RQ-8B),
Predator (MQ-1A/B), Sky
Warrior ERMP (MQ-1C)
Group 5 More than 1320 More than FL 180 Any
Reaper (MQ-9A), Global
Hawk (RQ-4), BAMS
(RQ-4N)
2.3. Existing UAS Capabilities—Payload-Enabled
A typical UAS is equipped with a camera to enable a UAS operator with situational
awareness of the UAS’s surroundings and environment [
28
]. Depending on the payload
weight limit (determined in part by the MGTOW) of the UAS, the UAS can carry a payload
to meet a desired operational outcome. The different types of payload configurations
can be classified into three distinct classifications, namely, (1) non-sensing, (2) sensing,
and (3) counter measure payload [
29
]. For (1) with adversarial UASs, these payloads
Drones 2022,6, 198 4 of 25
can comprise homemade explosives, biological, and radiological weapons (e.g., chemical,
biological, radiological, and explosives (CBRE)). For (2), these types of payloads enable
live video feeds for the purpose of surveillance and intelligence gathering or precision
strikes on a specific target. Lastly, for (3), these types of payloads enable the disruption of
telecommunication devices through RF jamming and similar. The list of payload-enabled
capabilities is summarized in Table 2. While the development of payload capabilities is
usually developed based on good intentions and for legitimate uses, malicious entities may
utilize these capabilities to conduct malicious UAS activities against the public.
Table 2. Types of UAS payload-enabled capabilities. Source: [29].
Type Capabilities
Non-Sensing Payload
Payload Release The payload is carried to a certain altitude and is released upon hovering above the target.
Kamikaze Both the payload and UAS crash into the target.
Sensing Payload
Electro-Optic Imagery and video recording functions to support ISR operations.
Light Detection and Ranging (LIDAR) The pulsing of a laser that enables distance measurements.
Countermeasure Payload
RF Jammer The payload overloads sensor and RF control inputs which causes disruption to operations.
Spoofers The spoofing capability payload disrupts navigational or command and control receiver
systems, such as those that rely on Global Navigation Satellite System (GNSS), for instance.
2.4. Emerging UAS Threats—Swarm Capabilities
The concept of a swarm in the context of UAS operations comprises a group of UASs
working as a system, collaborating, and communicating with each other to achieve the
desired mission objective [
30
]. In addition, swarm technology adopts an automation
architecture to achieve self-maneuvers so as to assist the UAS operator in controlling
multiple UASs to achieve a common goal [
31
]. The integration of micro-UASs coupled
with the concept of a swarm poses challenges to existing C-UAS measures [
32
]. This is due
to the small radio-cross-section (RCS) of micro-UASs where detection at large distances
with existing radar would be challenging [
32
]. While the concept of swarms for UASs is
still in the testing and development phase [
33
], it is essential to assess the effectiveness of
existing C-UAS techniques and emerging C-UAS techniques, such as the CyC-UAS concept,
in anticipation of the emerging threats posed by a swarm of UASs.
One of the main threats to installations today is small COTS UASs (Groups 1 and 2),
as these UASs are often easily accessible in the commercial market, inexpensive, and are
difficult to detect and neutralize [
34
]. A near-future threat is swarms of COTS UASs used
to target strategic and critical infrastructure.
The threats imposed by UASs were defined and discussed in this section. To gain
insight on the impact on the threats, various capabilities were also discussed.
3. Literature Review of Existing C-UAS Techniques
As discussed in Section 2, the infiltration of adversary UASs into restricted areas to
perform malicious activities may cause severe consequences or threaten the interests of
a facility. For this reason, it is critical to develop effective methods to deter any potential
intrusion into restricted areas by adversarial UASs. Since the early 2000s, the need for
C-UAS capabilities has been defined and developed through the adoption of engineering
techniques to derive feasible solutions. This section seeks to (1) introduce the C-UAS
processing chain (also known as the kill-chain) operating in a defined area, (2) provide
a broad overview of the main existing C-UAS techniques and their capability trade-offs,
and (3) introduce the need for a command and control (C2) system within C-UAS networks
to enhance C-UAS operation.
Drones 2022,6, 198 5 of 25
3.1. C-UAS Processing Chain and Techniques
The C-UAS processing chain encompasses the following phases as shown in Figure 1.
These phases include the need to “detect”, “locate/track”, “classify/identify”, and then
to “mitigate” [
29
,
35
]. At the initial phase, the C-UAS must be capable of performing
detection and providing the location of the adversary UAS. While the location of the UAS
is being “tracked”, the C-UAS attempts to identify and classify the unknown UAS such that
“mitigation” actions could be taken against the adversary UAS. These mitigating actions
may include the use of “kinetic” and/or “non-kinetic” techniques to prevent the adversary
UAS from performing any malicious activities within the protected area. To achieve
the various C-UAS functions at the different phases, several engineering solutions have
been adopted.
Figure 1. C-UAS kill-chain [35].
3.2. “Detect”, “Locate”, and “Track” Techniques
Table 3shows a list of commonly adopted engineering techniques to enable the
functions of detection, to locate, and to track an adversary UAS. A brief description of the
system capabilities and its limitations is also given.
Table 3. “Detection”, “locate”, and “tracking” techniques.
Techniques Capabilities Limitations
Radar
The radar sensor is capable of detecting a UAS if the UAS is
within the range of the radar sensor. This is achieved through
the receipt of reflected pulses of RF energy from the UAS.
Additional information about the UAS, such as the location
and the velocity of the UAS, can also be obtained through the
radar sensor. In advanced radar sensors, “tracking” the
location and “classifying” the type of UAS is achievable
through advanced signal processing algorithms.
Due to the “small” radar-cross-section of some COTS
Groups 1 and 2 UASs, detection and tracking remain
a challenge [36]. The ability to accurately “detect”
and “track” a small target could be degraded due to
unfavorable weather conditions such as the effect
of rainfall.
Radio Frequency
RF sensors are capable of detecting the frequencies
transmitted by other telecommunication devices in the RF
spectrum. By integrating the RF sensor with other UAS
software algorithms and devices, the system is able to
differentiate between a UAS and other RF devices. Therefore,
detection of a UAS can be achieved.
Many advanced UASs have recently adopted
frequency-hopping-spread spectrum (FHSS)
techniques instead of using a single set frequency for
communications [37]. This approach has added
additional complexity for the RF detection sensor to
effectively determine transmitting frequencies and
the sequence of transmission of a UAS using FHSS.
RF detection sensors can also be less effective in
crowded RF environments due to other RF
transmitting devices [38].
Electro-Optical (EO) and
Infrared (IR) Cameras
An EO/IR sensor is capable of capturing images during the
day and night using visible and infrared sensors. An EO/IR
sensor is usually coupled with computer vision algorithms to
differentiate between a UAS and other objects.
EO/IR detection sensors can consume large amounts
of electrical power due to the nature of the sensors
used. The cost to include EO/IR sensors in the
system is much higher as compared to other existing
UAS detection systems. This sensor is also limited by
range, given the nature of the sensors [39].
Acoustic Sensor
Acoustic sensors are capable of detecting sound emitted by
an object of interest. Coupling an acoustic sensor with UAS
audio comparison algorithms, detection of a UAS is
achievable by matching the detected sound with the sound
recorded in existing databases.
The detection range of acoustic detection sensors is
negatively affected if the surrounding environment
is noisy, such as a densely populated area or an
environment with high winds condition [40].
3.2.1. Mitigation Techniques: Non-Kinetic
Non-kinetic mitigation measures in C-UAS operations seek to deny, degrade, or dis-
rupt the capability of a UAS without the need for physical destruction [
41
]. Table 4shows a
list of commonly adopted non-kinetic mitigation measures used in C-UAS missions.
Drones 2022,6, 198 6 of 25
Table 4. List of non-kinetic mitigation measures.
Techniques Capabilities Limitations
Frequency Jamming
A frequency jammer transmits large amounts of
electrical power over a range of predefined RF
frequencies to interfere with and disrupt the
communication link between the UAS and the
ground control station over a period of time. This
action forces the UAS to trigger the “return home”
algorithm or to perform an emergency landing
based on the default UAS safety protocol.
Typical RF jammers consume large amounts of
electrical power. To meet this requirement, RF
jammers are typically bulky due to the heavy
and large electronic components used. This
restricts the ease of deployability of the device.
Jamming on a single frequency may not be
effective to counter UAS operations if the UAS
uses FHSS [42]. In addition, other friendly
communication devices operating at the jammed
frequency may also be affected [11].
Global Navigation
Satellite System
(GNSS) Jamming
The GNSS jamming technique attempts to disrupt
the GPS communication link between the UAS and
GPS satellites.
This technique may not be effective for UASs
that do not require GPS for navigation.
GNSS Spoofing
The GNSS spoofing technique enables
“impersonation” by feeding the UAS with false
navigation information and then eventually taking
over the role as the host of the UAS for control.
This method may be ineffective with adversarial
UASs equipped with inertial measurement unit
sensors. It is not suitable for use in places where
satellite navigation is required by other
systems [43].
3.2.2. Mitigation Techniques: Kinetic
Kinetic mitigation techniques in C-UAS operations seek to degrade the UAS through
inflicting damage on the physical components of the UAS [
41
]. Table 5shows a list of
commonly adopted kinetic mitigation measures used in C-UAS missions.
Table 5. List of kinetic mitigation measures.
Techniques Capabilities Limitations
Net Capture
This technique adopts the concept of a “firing gun”.
Upon triggering of the firing gun, netting embedded
within the weapon is deployed to capture the UAS.
The firing gun can be deployed on a UAS or
mounted on a handheld device.
This capturing device needs to attain close
enough range to the adversarial UAS in order to
be effective [43].
Directional
Electromagnetic
Pulse (EMP)
This technique uses an electromagnetic pulse to
damage onboard radio electronic system on the UAS.
The directional EMP adopts the similar concept of a
“firing gun” and can be deployed on a
handheld device.
Since EMP at different frequencies requires
different transmission distances, the EMP
method to take down a UAS may not be effective
if the required distance is not met, even though
an adversary UAS is detected [5].
The C-UAS processing chain is complete with the integration of various detection and
mitigation techniques mentioned in this section. For example, the radar UAS detection
system is responsible for the detection, identification, and tracking of the location of an
adversarial UAS. Then, it is the responsibility of the frequency jammer to mitigate the
adversarial UAS to prevent it further infiltrating into a facility.
3.3. Command and Control (C2) System
The function of the command and control (C2) system in the C-UAS network aims to
provide the stakeholders with (1) a holistic overview of the situation within the operating
environment, (2) the ability to analyze the situation, and (3) to execute the necessary
decisions based on the assessment made [
44
]. This is achieved through the integration of
various detection and mitigation devices with the C2 system.
Drones 2022,6, 198 7 of 25
3.4. C-UAS Network
As illustrated in Figure 2, the C-UAS network includes three functional blocks, namely,
(1) “detection and tracking”, (2) “react”, and (3) “mitigate”. The ”detection and tracking”
functional block comprises a single or a set of UAS detection devices to detect and track
adversarial drones within a defined boundary. The information such as the location and
speed of the detected adversarial drones would then be sent as output information to
the “react” functional block for further analysis. In the “react” block, since the outputs
from the various UAS detection devices are in different form, a data fusion unit would
be required to process the incoming information and output a standardized and coherent
set of data to the C2 system, such that the information presented to the stakeholders is
consistent and accurate for the purpose of decision-making [
5
,
45
]. Based on the profile
of the adversarial drone, the C2 system selects and triggers the most suitable mitigating
technique to neutralize the adversarial drone.
Figure 2. C-UAS network [44].
The functions at the different phases of the C-UAS processing chain were discussed
in this section. To achieve the goals of a C-UAS mission, various detection and mitigation
techniques are adopted, as discussed in this section. The introduction of a C2 system within
the C-UAS network enhances the ability for the stakeholders to analyze the situation such
that the most appropriate actions are applied against the adversarial UAS.
4. Literature Review on C-UAS Acquiring Cyber-Attack Techniques
In recent studies, the application of cyber-attacks in the C-UAS domain show the
scalpel-edge accuracy that such attacks can produce when defending against an adversarial
UAS. Many CyC-UAS approaches work by either denying or disrupting adversary UAS RF
communications without the need for jamming [
3
,
46
]. This section seeks to provide (1) a
broad overview of the main existing cyber-attack methods on C-UAS operations and (2) the
proposed concept of operations based on a CyC-UAS system’s capabilities and architecture.
4.1. Existing Cyber-Attack Techniques
The current literature on C-UASs using cyber-attack techniques focuses on identifying
the vulnerability within the seven-layer open systems interconnection (OSI) model of the
communication network protocols [
47
]. Specifically, the cyber-attack scheme attempts to
manipulate or tamper with the information flowing into the transport (layer 4), network
(layer 3), data Link (layer 2), or physical (layer 1) layer of the OSI model, with the intent to
deny the use of communication network services [48].
4.2. Distributed Denial of Service Attack
The denial-of-service (DoS) attack is classified as one type of cyber-attack technique
and aims to suspend or to interrupt the use of a communication network [
49
]. This is
accomplished through disrupting the network connection services by flooding the network
with data packets such that the network becomes overwhelmed, and results in the inability
Drones 2022,6, 198 8 of 25
of any host to establish communications with other telecommunication devices within
the network [50].
In wireless communications, a typical construct of a UAS consists of an aerial device
(also known as a drone) and a ground control station (GCS) that communicate via a set
of operating frequencies [
51
]. In the context of CyC-UAS operation, the DoS cyber-attack
technique can be performed against wireless networks [52].
In the context of CyC-UASs, the C-UAS adopts the DoS attack technique on the UAS
through the wireless network linking the GCS and drone (henceforth, we will simplify
terminology and also refer to the aerial component of the system as simply the UAS).
Commercial UASs that operate using WiFi network protocols such as 802.11 (usually in the
2.4 GHz and 5 GHz frequency ranges) are extremely vulnerable to such attacks because
the operating radio frequencies are known and are easily targeted using network interface
cards [53].
4.3. User Datagram Protocol Flood Attack
The User Datagram Protocol (UDP) uses a connectionless communication model with
minimal packet ordering mechanisms to enable data package transfer within a network [
54
].
In C-UAS operations, the UDP flood attack technique attempts to degrade UAS wireless
network performance by flooding the network with data packets, forcing the adversary
UAS to trigger internal safety protocols such as the “return to base” algorithm or to perform
an emergency landing based on the UAS’s default safety protocol [55].
4.4. TCP SYN Flood Attack
Unlike the UDP protocol, the Transmission Control Protocol (TCP) protocol is a
connection-oriented communication model, where a three-way handshake between the
client and the server must be established first before commencing data package transfers
within the network, as shown in Figure 3[
56
]. For the sender to establish communications
with the receiver, the sender first sends a synchronization (denoted by SYN) request
with the sender’s IP address to the receiver. Then, the receiver sends a synchronization
acknowledgment (denoted SYN ACK) to the sender’s IP address. The sender then replies
to the receiver with an acknowledgment (denoted ACK) to complete the establishment
process [56].
Figure 3. TCP “3-way handshake”.
In the case of a TCP flood attack, the attacker initiates the TCP protocol with the
receiver with a spoofed IP address [
57
]. The receiver then replies with an SYN ACK to the
IP address that was provided by the attacker. Then, the attacker repeats the same attack
approach on the receiver multiple times. As a result, the network is flooded, causing the
server to be unable to communicate with the network due to memory exhaustion [
55
]. In the
context of CyC-UAS operations, the C-UAS and the adversarial UAS act as the attacker
(sender) and receiver, respectively. The TCP flood attack causes the wireless network of the
adversarial UAS to collapse, forcing the UAS to activate its return-to-base protocol, conduct
an emergency landing, or other internal safety protocol [58].
Drones 2022,6, 198 9 of 25
4.5. Deauthentication Attack in Wireless Network
The IEEE 802.11 technical standard governs local area network (LAN) technical specifi-
cation and describes the set of media access control (MAC) protocols for the implementation
of wireless LAN [
59
]. The deauthentication attack exploits the OSI layer two vulnerabil-
ities in wireless access points to prevent legitimate users from accessing a network [
60
].
With information such as the MAC address of the telecommunication devices available
openly within the wireless network, an attacker is able to identify the targeted device. Then,
the attacker can launch a deauthentication attack on the targeted device in an attempt to
cut off the wireless connection between the targeted device and the network by sending
continuous deauthentication frames to the targeted device [
61
]. Because a deauthentication
attack can disrupt the connection between a client and its host with only one forged frame
for every six legitimate frames between a client and its host [
60
], deauthentication attacks
are especially useful when limited power is available in countering adversarial UASs [
10
].
In the context of CyC-UAS operations, the C-UAS may adopt the deauthentication cyber-
attack technique by sending continuous deauthentucation frames to the adversary UAS
over the wireless network, so as to deny communications between the adversarial GCS
and its UAS [
61
]. Similar to the attacks against WiFi networks, in the context of a CyC-
UAS, deauthentication attacks are only carried out against UASs using the 802.11 wireless
standard [
10
]. Thus, these attack types will not be effective against UASs that use fre-
quency hopping spread spectrum or other communication schemes that operate outside the
2.4 and 5 GHz WiFi frequency bands.
4.6. Comparison between Cyber-Attack Techniques
Table 6summarizes and compares the three cyber-attack techniques for the CyC-UAS
operation. While the list of mentioned cyber-attack techniques can be used for CyC-UAS
operation, the deauthentication attack is the most effective mode of attack since (1) the
technique is able to identify a specific UAS target with the identification of its MAC address
from the WiFi network, and (2) it has less coding complexity to identify the IP address of
the target.
Table 6. List of cyber-attack techniques for CyC-UAS operation.
Techniques Capabilities Limitations
User Datagram
Protocol Flood Attack
Easy to implement since the communication between
the CyC-UAS and adversarial UAS is connectionless
and session-less.
CyC-UAS gains limited access to the
adversarial UAS since the connection is
connectionless. For example, CyC-UAS is
unable to take over control or to intercept
information transmitted by the
adversarial UAS.
TCP SYN Flood attack
With the IP address of a particular adversarial UAS
known, a dedicated TCP/SYN flood attack can be
performed on a specific adversarial UAS.
The complexity of a TCP/SYN flood attack is
relatively higher as additional algorithm must
be integrated within the CyC-UAS to identify
the IP address of the desired adversarial UAS.
This may result in higher processing time
during the C-UAS process.
Deauthentication
Attack
Easy to implement since the information on MAC
address of the adversarial UAS can be obtained in the
wireless network.
This attack is effective only against adversarial
UASs that use wireless access points.
4.7. CyC-UAS Physical Setup
The essential hardware of a CyC-UAS system comprises a micro-controller,
transceiver, and an RF antenna [
61
]. The source-code of the cyber-attack algorithm embed-
ded in the micro-controller launches a detection algorithm to scan for adversarial UASs
within the surrounding environment. Upon successful detection of an adversarial UAS,
the C-UAS launches the mitigation attack algorithm on the UAS. The CyC-UAS transceiver
Drones 2022,6, 198 10 of 25
and the RF antenna serve as the intermediary between the micro-controller and the RF
environment to complete the processing chain of the CyC-UAS. Figure 4shows a simple
CyC-UAS prototype setup.
Figure 4. CyC-UAS hardware prototype.
4.7.1. Past C-UAS Experiments with CyC-UAS Prototype
In recent studies, the application of cyber-attacks in the C-UAS domain has shown
potential improvements in energy consumption in comparison with other existing conven-
tional C-UAS techniques [
10
]. For example, the CyC-UAS technique is capable of disrupting
the communication link of a specific adversarial UAS target instead of transmitting across
a range of frequencies with a high amount of energy adopted by conventional frequency
jamming C-UAS. Through the conduct of these experiments, the effectiveness and effi-
ciency of the cyber-attack technique applied on COTS UASs that operate in the
2.4 GHz
and
5 GHz
WiFi frequency bands were validated [
10
]. The experiments are specifically
scoped towards seeking an understanding on the amount of energy consumed during
C-UAS operation. In particular, the deauthentication cyber-attack technique was used in
various attack experiment scenarios. These experiments were conducted in an outdoor
environment with the use of various telecommunication equipment.
4.7.2. Experiment Setup
We follow the experiment setup from [
10
]. Table 7shows the list of equipment used
and the respective roles of the equipment during the experiments. The equipment and
testing focus is based on targeting commercial UASs that use the IEEE 802.11 standard.
Table 7. List of equipment and roles.
Equipment Roles in Experiment
UASs
Parrot Bebop Adversarial UAS.
Skydio 2+ Adversarial UAS.
AquaQuad
Friendly UAS used as mobile C-UAS
platform (to be integrated with Raspberry
Pi 4 and WiFi antenna).
Drones 2022,6, 198 11 of 25
Table 7. Cont.
Equipment Roles in Experiment
Raspberry Pi 4 Model B + WiFi Network Interface
Card (Alpha AWUS036ACH)
C-UAS (deauthentication attack source
code embedded in Raspberry Pi 4.)
Multimeter–AiLi UM25C USB
Integrated onto Raspberry Pi 4 to collect
electrical power readings (voltage
and current).
Smart phone
Software applications for the Parrot
Bebop and Skydio2+ to be installed onto
smart phone devices to perform the role
of ground control station (GCS) of
adversarial UAS and mobile C-UAS,
respectively.
4.8. Experimental Scenarios
The experiment scenarios were designed based on the information required to validate
the performance of the CyC-UAS system at various ranges and altitudes. There were
three distinct scenarios, namely, (1) CyC-UAS and adversarial UAS are both stationary,
(2) CyC-UAS is stationary and adversarial UAS is in motion, and (3) CyC-UAS is mobile
(attached to a friendly UAS) and adversarial UAS is in motion.
4.8.1. Observations from Scenario 1—CyC-UAS and Adversarial UAS at
Stationary Positions
In this scenario, both the CyC-UAS system and the single adversarial UAS were held
at stationary fixed positions during the “detection” and at the “attack” phases at stand-off
distances of 10, 100, 250, and 400 m, as shown in Figure 5. The CyC-UAS system used
in the experiments has a maximum detection range in a ground-to-air configuration of
approximately 250 m and is capable of detecting intrusion of adversarial UASs that falls
within the detection range. The CyC-UAS system scans the environment consistently to
detect adversarial UAS intrusions. Upon a successful detection, the CyC-UAS initiates
a deauthentication cyber-attack technique on the adversarial UAS. It was observed that
the CyC-UAS system was successful in (1) detecting and attacking the adversarial UAS
at distances of 10, 100, 250, and 400 m and that (2) the time taken upon a detection till the
neutralization of an adversarial UAS is estimated to be 15 s, consuming about 1.1 W of
electrical power. At the end of the attack, the adversarial UAS returned to its last known
connection point and landed subsequently. At about 400 m away, the CyC-UAS was unable
to detect the adversarial UAS situated at 400 m away. It was deduced that the transmitted
signal of the CyC-UAS was not strong enough to reach the adversarial UAS at a distance
of 400 m, which was primarily limited by interference from buildings, trees, and power
lines in the area as well as the transmission power that the Raspberry Pi 4 and the wireless
network card were designed to output.
Drones 2022,6, 198 12 of 25
Figure 5. CyC-UAS and adversarial UAS at stationary positions.
4.8.2. Observations from Scenario 2—C-UAS at Stationary Position and Adversarial UAS
in Motion
In this scenario, both the CyC-UAS and adversarial UAS started at stationary positions,
having a separation distance of 250 m just beyond the effective range of the CyC-UAS
system used in these experiments, as shown in Figure 6. The CyC-UAS begins scanning
the environment to detect the adversarial UAS. Then, the adversarial UAS commences its
operations by flying towards the CyC-UAS. Upon a successful detection of the adversarial
UAS, the CyC-UAS initiates the deauthentication cyber-attack technique on the adversarial
UAS. It was observed that the adversarial UAS (1) came to a halt and hovered at a stationary
position for about 10 s before (2) returning to its last known connection point and landing
subsequently. It was observed that the GCS of the adversarial UAS was unable to control
the adversarial UAS due to the loss of telecommunications between the GCS and UAS
caused by the deauthentication cyber-attack [10].
Figure 6. C-UAS at stationary position and adversarial UAS in motion.
4.8.3. Observations from Scenario 3—CyC-UAS and Adversarial UAS Both in Motion
In this scenario, the CyC-UAS was fitted onto a proprietary UAS, called the
AquaQuad [
62
], to turn the CyC-UAS into a mobile C-UAS. Both the mobile CyC-UAS
and the adversarial UAS moved in the same direction, having a separation distance of
about 20 m [
10
]. While both UASs were in motion, the mobile CyC-UAS performed the
deauthentication cyber-attack on the adversarial UAS. It was observed that the (1) mobile
Drones 2022,6, 198 13 of 25
CyC-UAS was able to detect the adversarial UAS while both the UASs were in motion and
that (2) during the deauthentication cyber-attack process, the adversarial UAS came to a
halt (while hovering for about 10 s) before returning to its last known connection point and
landing subsequently.
The experiments performed in the scenarios above provide insights into the effective-
ness and efficiency of CyC-UAS operations. The use of the deauthentication cyber-attack
technique in all the experiments was successful in neutralizing the adversarial UAS by
severing the telecommunication link between the adversarial UAS and the GCS. In ad-
dition, the conduct of the experiments provided essential information to assess system
performance of the deauthentication cyber-attack technique. The information attained from
the experiments, as well as the physical behavior of the adversarial UAS observed in the
experimental scenarios, was then used to define the system performance of the CyC-UAS
system in the subsequent section.
4.9. Proposed Concept of Operation
Given the system description of the capability of the CyC-UAS, two CONOPs schemes
are proposed and elaborated for further discussion in this subsection; namely, defensive
deployment and aggressive deployment.
4.9.1. Defensive CyC-UAS Deployment
In the defensive deployment scenario, the mission of the CyC-UAS is to prevent
the infiltration of adversarial UASs within a defined protected area to protect a specific
installation or infrastructure. In this setup, several CyC-UASs are deployed in stationary
positions to defend against infiltration of adversarial UASs into the protected area, as shown
in Figure 7. The defensive deployment concept aims to provide a permanent defensive
mechanism to prevent potential adversarial UAS attacks. Upon a successful detection of
an adversarial UAS, the CyC-UAS automatically launches the mitigation algorithm in an
attempt to neutralize the adversarial UAS. Since the CyC-UAS alone is capable of fulfilling
the functions of the C-UAS processing chain, and because the CyC-UAS has the ability to
perform a mitigation attack on the UAS immediately upon a successful adversarial UAS
detection, the lag-time between detection and mitigation is minimized.
Figure 7. CONOPS of stationary defensive deployment of CyC-UAS to protect fixed infrastructure.
Drones 2022,6, 198 14 of 25
The CyC-UAS can be deployed on ground mobile platforms, such as military vehicles
maneuvering at the battlefront or police or national defense vehicles protecting civilians, as
shown in Figure 8.
Figure 8.
CONOPS of ground mobile defensive deployment of CyC-UAS to protect vehicles and civilians.
4.9.2. Aggressive CyC-UAS Deployment
In this CONOPS, the CyC-UAS employs an aggressive approach in the attempt to
neutralize any potential adversarial UASs, as shown in Figure 9. To enable CyC-UAS with
the ability to maneuver within the operating area, the CyC-UAS is integrated on an air
mobile platform. For example, by integrating the CyC-UAS onto a friendly UAS, the system
can rapidly maneuver in three dimensions such that it enhances the CyC-UAS’s ability to
detect, track, and mitigate adversarial UASs.
Figure 9.
CONOPS of aggressive deployment of CyC-UAS to project protection against adversarial
UASs beyond fixed or mobile CyC-UAS platforms.
This section discussed various DoS cyber-attack techniques that are adopted for C-UAS
operations. The existing literature validates the effects of cyber-attacks on adversarial UASs
Drones 2022,6, 198 15 of 25
based on physical experiments. With a good understanding of the system architecture and
the capabilities of the CyC-UASs, two feasible CONOPS were proposed.
5. Modeling and Simulation
This section develops a simulation model to represent CyC-UAS operations based on
the proposed CONOP presented in Section 4.9. The simulation seeks to gain an under-
standing of the CyC-UAS system performance and limitations using the deauthentication
cyber-attack technique. In particular, the simulation is used to better understand the
estimated energy consumption for a given simulated scenario of CyC-UAS operations.
The experimental results achieved during the experiments, as well as the physical obser-
vations attained from the various experimental scenarios presented in Section 4.7.1, are
applied as system parameters to the CyC-UAS simulation model. The CyC-UAS software
model and simulations were constructed and conducted in ExtendSim10 [63].
5.1. Mission Scenario for C-UAS Operation
The aim of the CyC-UAS system was to prevent the intrusion of adversarial UASs
into a defined protected area, as shown in Figure 10. There were two CyC-UAS systems
deployed at stationary positions beyond the protected area such that the systems could
potentially detect and neutralize any incoming adversarial UASs. On the other hand,
the aim of the adversarial UASs was to penetrate the protected area. In this scenario, it is
assumed that (1) the protected area may be subjected to concurrent intrusion attempts by
multiple adversarial UASs (a swarm attack) and that (2) the adversarial UASs would move
in a straight-line direction, represented by the red arrows in Figure 10.
Figure 10. CyC-UAS operational scenario.
5.2. Modeling Setup
The area of operation (AO) was divided into three different zones (Zone 1, 2, and 3), as
represented in Figure 11. The ability to detect and to perform a cyber-attack is dependent
on whether the adversarial UAS falls within the detection range of the CyC-UAS systems.
In this case, since the region in Zone 2 was overlapped by two CyC-UAS systems, the chance
of detecting and neutralizing an adversarial UAS that enters the region is doubled, since
either one of the CyC-UAS systems could perform the detection or attack on the adversarial
UAS. In addition, it was assumed that the three different zones have equal chance (Zone 1,
2, and 3 = probability of 0.333) for an adversarial UAS to appear in the respective regions.
Drones 2022,6, 198 16 of 25
Figure 11. Zones of area of operations.
In this model, it was assumed that both the CyC-UAS systems would be scanning the
environment actively to detect any number of adversarial UASs. The CyC-UAS would
then initiate the deauthentication cyber-attack on the adversarial UASs based on a first-
in-first-out attack sequence. It was assumed that an adversarial UAS would come to a
halt and hover at a stationary position for about 10 s once the cyber-attack was initiated.
Should the attack on an adversarial UAS be successful, the adversarial UAS would land.
On the other hand, if the attempt to neutralize the adversarial UAS was unsuccessful,
the adversarial UAS would continue to traverse in the initial direction towards the protected
area. In addition, the CyC-UAS is capable of re-engagement with an adversarial UAS if
attack attempt is unsuccessful and if the adversarial UAS remains within detection range of
the CyC-UAS. The CyC-UAS has the ability to perform both the role of detection and attack
concurrently. These assumptions mentioned above were applied to the simulation model.
Table 8shows the system performance parameters of the CyC-UAS and adversarial
UAS applied in the ExtendSim10 simulation model. The model was also designed to record
the power consumed by both CyC-UAS systems throughout the detection and attack phases.
Once the first adversarial UAS falls within the detection range of the CyC-UAS systems,
data collection of the power consumed by the CyC-UAS commences and is terminated
when the last-detected adversarial UAS is neutralized. The overall power consumption
of the CyC-UAS is the summation of power consumed by both the CyC-UAS systems
deployed in the model.
Table 8. CyC-UAS and adversarial UAS parameters.
C-UAS Parameters
Maximum detection range: 250 m
Time to detect and neutralize target: Log-normal distribution (mean = 15 s, Std = 2 s)
Probability of success for detect and attack actions for 1×
adversarial UAS: 0.8
Power consumption to detect and attack 1×adversarial UAS: 1.1 W
Adversarial UAS Parameters
Adversarial UAS traveling speed: 30 km/h
To simplify the simulation model, experimental values measured at a separation
distance of 250 m between the CyC-UAS and the adversarial UAS performed in Section 4.7.1
Drones 2022,6, 198 17 of 25
were applied in this simulation model. This model assumed that the adversarial UASs
traverse the AO with a constant speed of 30 km/h. Further, it was assumed that the
CyC-UAS has a detection range of 250 m, and that the overall detection region was in the
form of a circular shape having a diameter of 500 m. Assuming that the adversarial UAS
traverses (1) across the detection region of 500 m and (2) at a constant speed and direction,
the adversarial UAS would be present in the detection region for about 60 s, as shown in
Figure 12.
Figure 12. Adversarial UAS traversing detection region.
The flowchart in Figure 13 provides an overview of the sequence of activities and
decision points upon detection of an adversarial UAS. With the system descriptions as well
as the system parameters presented above, a simulation model was built in ExtendSim10
to understand the CyC-UAS system performance.
5.3. Simulation
In alignment with the aim of the mission objective of the CyC-UAS system presented
in the scenario, four performance metrics, as shown in Table 9, were identified to measure
the effectiveness and the capability of the CyC-UAS system.
Table 9. Metrics of analysis for the CyC-UAS system.
Metrics Description
# of adversarial UASs neutralized
The primary objective of the C-UAS system was to prevent the intrusion of
adversarial UASs entering the protected area. To achieve this objective,
the C-UAS system must first detect and then subsequently neutralize the
adversarial UASs.
# of adversarial UAS penetrations into
protected area
It is assumed that an adversarial UAS has successfully penetrated the
protected area if the adversarial UAS was not neutralized by the C-UAS.
# Accumulated energy consumed by C-UAS
The power consumed by the C-UAS during the entire detection and attack
phases is accumulated and recorded.
# Accumulated C-UAS operating period (s) The overall time taken for C-UAS operations is recorded.
Drones 2022,6, 198 18 of 25
Figure 13.
Sequence of activities and decision points for CyC-UAS upon detection of an adversarial UAS.
To simulate a swarm attack, the group of adversarial UASs is represented as a salvo
attack in ExtendSim10. Three salvo attacks that consist of 8, 10, and 12 adversarial UASs
are simulated independently. In each of the salvo attacks, the adversarial UASs are injected
into the model as inputs. In addition, each salvo simulation run is repeated 100 times to
achieve sufficient samples to attain an average value for the metrics stated above.
5.4. Simulation Results
Table 10 shows the average results of the metrics for the C-UAS across the different
numbers of adversarial UASs in a single swarm attack.
Based on the 100 simulation runs performed in each scenario, the C-UAS system that
comprises two CyC-UAS systems was capable of neutralizing between eight and nine
adversarial UASs in a single swarm attack for all scenarios. However, as the number of
adversarial UASs in the swarm attack increases beyond nine (10, 12, and 14), the number
of adversarial UAS misses increases as well. Therefore, based on the C-UAS deployment
layout and the assumptions stated above, the C-UAS system is effective in neutralizing
nine adversarial UASs in a swarm attack.
Drones 2022,6, 198 19 of 25
Table 10. Metrics and corresponding results.
# of Adversarial UASs in a Single Swarm Attack
8 10 12 14
# of adversarial UASs neutralized 8 9 9 9
# of adversarial UASs penetrating protected area 0 1 3 5
# Accumulated energy consumed by C-UAS (W/h) 0.0342 0.0397 0.0385 0.0409
# Accumulated C-UAS operating period (s) 56 65 63 67
The average accumulated energy consumed and the C-UAS operating period taken by
the C-UAS management system to neutralize nine adversarial UASs in each swarm attack
scenario (10, 12, and 14 adversarial UASs) are as shown in Table 11.
Table 11. Average accumulated energy consumed and operating period.
Average Energy and Time Consumed for 9 Adversarial UASs # Adversarial UASs in a Single Swarm Attack
10 12 14
# Average accumulated energy consumed by C-UAS (W/h) 0.0397
# Average accumulated C-UAS operating period (s) 65.00
A C-UAS management system simulation model was built based on (1) the application
of deauthentication cyber-attack technique, (2) proposed CONOPs, (3) mission scenario,
and (4) the applied C-UAS system parameters attained during the physical experiment.
A swarm attack on the C-UAS management system was also simulated to observe the
capabilities and the limitations of the system. In addition, the simulations that were
conducted also provide information on the overall energy consumed and the period taken
for the entire C-UAS operation.
The mission scenario presented in this section and the set of simulated results shown
can be used as a baseline to compare and analyze the effectiveness and efficiency of some
other convention C-UAS techniques. This is performed in the next section.
6. Comparison of Energy Consumption and Performance between C-UAS Techniques
The experiments performed in Section 4.7.1 provided insights into the energy con-
sumption requirement for CyC-UAS operations. The aim for this section is to assess the
energy efficiency of CyC-UAS by (1) understanding the energy requirement from exist-
ing
C-UAS
techniques through the review of technical specifications of existing products,
as well as to (2) compare the energy consumption requirements between CyC-UAS and
existing C-UAS techniques. In addition, this section also aims to compare the system
performance of various C-UAS techniques.
6.1. Existing Products
The EAGLE108 is an existing C-UAS that is capable of performing detection and miti-
gation on an adversarial UAS through RF signal detection and RF jamming [
64
]. Table 12
shows the system specifications of EAGLE108. While there are several C-UAS systems
that use RF jamming, the EAGLE108 is representative of many available systems. Some
C-UAS systems that use RF jamming operate at much higher output transmission powers.
However, this article limits analysis to the EAGLE108 because data are readily available
in open source literature and it is a system commonly used by civilian organizations in
addition to national security organizations.
Drones 2022,6, 198 20 of 25
Table 12. EAGLE 108 system specifications.
Existing Product System Description Technical Specifications
EAGLE108–Manufactured by
PHANTOM TECHNOLOGIES LTD [64]
- EAGLE108 enables consistent detection
and tracking of a UAS given a specified
range.
- Output transmission power: 375 W.
- The EAGLE108 neutralizes the
adversarial UASs by jamming the UAS
downlink signal.
- Detection and mitigation range: 1000 m
- Assets deployment: fixed installation - RF jamming capability: WiFi signals
(2.4 GHz and 5.8 GHz).
- Time taken from detection to mitigation
of adversarial UAS: estimated 15 s.
6.2. Energy Consumption Comparison
Based on the experimental setup using the CyC-UAS prototype, it was shown that
the CyC-UAS has an effective detection range of about 250 m. To enable a comparison of
energy requirements between the CyC-UAS prototype and the EAGLE108, the following
assumptions were made: (1) the scanning environment has clear line-of-sight; (2) there is
negligible frequency interference.
Based on the system specifications of EAGLE108, the system has a transmission output
power rating of about 375 W for frequency jamming. Based on the literature provided
by the company, it is assumed that the EAGLE108 operates at maximum power during
frequency jamming operations. In addition, the company lists a power consumption of
2 A at
12 V
for the detection module [
64
]. Using Ohm’s law of
P=V·I
yields a result of
24 W for detection. Thus, it is assumed that maximum total power consumption for the
EAGLE108 is around 400 W, inclusive of both detection and mitigation.
In comparison, the CyC-UAS depicted in Table 7uses 1.1 W to power the network in-
terface card (Alpha AWUS036ACH), as found in the experiments detailed in [
10
]. The Rasp-
berry Pi 4 B consumes between 3.8 W and 6 W [
65
]. Thus, it is assumed that maximum total
power consumption for the CyC-UAS is around 7 W. It is clear that the CyC-UAS power
consumption is much more favorable than the broadband RF jamming of the EAGLE108.
Ignoring the detection module of the EAGLE108 for both power consumption and time
to go through the C-UAS kill-chain (detect, locate and track, classify, and identify, as per
Figure 1), the EAGLE108 mitigation system requires about 15 s on average for the system to
complete the C-UAS processing chain on an adversarial drone. While the mitigation system
can operate for up to two minutes continuously, it is assumed that this is a rare occurrence.
Thus, it is estimated that a total of 1.565 W/h is required to complete the mitigation step of
the C-UAS kill-chain.
The CyC-UAS engaged the mitigation subsystem for 15 s during experimentation [
10
].
However, the amount of time required can change based upon details of the adversarial
UAS. Thus, the most appropriate comparison between the EAGLE108 and the CyC-UAS is
to look solely at the mitigation subsystems over the 15 s engagement window. Table 13
shows the estimated, consolidated transmission power and energy consumed for the
CyC-UAS prototype and the EAGLE108 mitigation subsystems.
Table 13. Estimated power and energy consumption at 250 m.
Power and Energy Consumption to Engage One Adversarial UAS at 250 m CyC-UAS Prototype EAGLE108
Power consumed for mitigation (W) 1.1 375
Energy consumed for mitigation (W/h) 0.00458 1.5625
Drones 2022,6, 198 21 of 25
6.3. Energy Comparison Analysis
Based on (1) the transmission power required for the EAGLE108 and (2) that the
EAGLE108 requires about 15 s to complete the mitigation portion of the C-UAS kill-chain,
the EAGL108 requires far more transmission energy, in comparison to the transmission
energy required for the CyC-UAS prototype, to achieve the same C-UAS outcome.
In the case of EAGLE108, since RF jamming is employed as the mitigation technique,
a large amount of power is required to overcome the adversarial UAS’s communications
signal, such that the signal is disrupted and terminates the operations of the UAS. On the
other hand, the requirement for having a large amount of transmission power is not
required for CyC-UAS. Instead, the CyC-UAS technique only requires sufficient transmis-
sion power such that the transmission signal can reach the adversarial UAS to establish
communications with the UAS to conduct the C-UAS operation.
Based on the comparison and benefit analysis made, it is concluded that the CyC-UAS
technique utilizes much less transmission energy as compared to the RF jamming technique,
which yields great improvement in energy-savings, resulting in better energy efficiency.
6.4. Performance Comparison Analysis
While both the CyC-UAS prototype and EAGLE108 adopt the DoS mitigation method
to disrupt the use of adversarial UASs, CyC-UAS uses a dedicated attack approach on
a specific target and does not affect or disrupt other telecommunication devices that are
operating within the environment during the C-UAS operation. In contrast, the EAGLE108
transmits a large amount of energy on a particular frequency to the environment to jam the
telecommunication link between the adversarial UAS and GCS. This approach may poten-
tially affect other friendly communications devices that operate in the jammed frequency
within the same environment.
The energy efficiency of the CyC-UAS was validated through the comparison of energy
consumption between the CyC-UAS and other popular existing C-UAS techniques, such as
the RF jamming method. The result from the comparison shows that CyC-UAS achieves
significant energy-saving as compared to conventional RF jamming methods. In addition,
in comparison with the RF jamming technique, the CyC-UAS is capable of achieving the
same C-UAS mission objective without disrupting other nearby telecommunication devices.
7. Conclusions
The effectiveness and performance of the CyC-UAS concept was validated through
the conduct of experiments and simulations revealed in this article. The literature review
suggested that COTS UASs that operate in the WiFi frequency band (2.4 GHz and 5 GHz)
are extremely vulnerable to CyC-UAS attacks, since the operating frequency is known.
In the context of CyC-UASs, the cyber-attack scheme attempts to manipulate or tamper
with the information flowing within the OSI model, with the intent to deny the use of the
communication network. The DoS technique, which aims to suspend or to interrupt the
use of a communication network, is accomplished by flooding the communication network
with data packets such that the network becomes overwhelmed.
The deauthentication attack DoS method makes use of deauthentication frames in a
wireless network. This technique was used in the construction of a CyC-UAS prototype
that consists of a micro-controller (with transceiver integrated within) and an RF WiFi
antenna that was used to conduct a set of experiments to validate the effectiveness of
the deauthentication attack technique applied on COTS UASs that operate in the
2.4 GHz
and 5 GHz WiFi frequency bands. The results from the experiments revealed (1) the
physical behavior of the adversarial UAS upon a successful CyC-UAS attack, (2) the
range limitations of the CyC-UAS prototype, and (3) the transmission power and energy
requirement for the CyC-UAS. This information was essential for the development of the
CyC-UAS simulation model.
Given the system description and physical behavior of the CyC-UAS, two feasible
CONOP schemes were investigated, including defensive deployment and aggressive de-
Drones 2022,6, 198 22 of 25
ployment. In the defensive deployment CONOP, the CyC-UAS is used to defend against
provocative adversarial UASs on stationary or mobile infrastructure. In the aggressive
deployment CONOP, the CyC-UAS achieves the ability to maneuver in three dimensions
to enable the CyC-UAS to be able to operate as the aggressor in an attempt to seek, locate,
and mitigate potential adversarial UASs.
A simulation model to mimic the proposed defensive deployment CONOP was de-
veloped and exercised. The simulation model was modeled based upon the information
attained from the experiments and the physical responses gathered based on the deauthen-
tication cyber-attack technique. To simulate the responsiveness of the CyC-UAS based on a
swarm attack, the group of adversarial UASs were represented by a salvo in the simulation.
The result from the simulation runs revealed the estimated number of adversarial UASs
that the CyC-UAS was capable of eliminating, as well as the estimated energy consumed
during the C-UAS operation.
Energy efficiency analysis of the CyC-UAS was achieved through the comparison of
energy consumption between CyC-UAS and other popular existing C-UAS techniques,
such as the RF jamming method. The comparison between the CyC-UAS prototype and
the EAGLE108 showed that CyC-UAS achieved significant energy-saving as compared to
the conventional RF jamming method.
7.1. Recommendations
The results attained through (1) review of the existing literature, (2) conduct of ex-
periments, (3) simulations, and (4) comparison of energy requirements and performance
between C-UAS techniques validate the concept and effectiveness of the application of
cyber-attacks in the C-UAS domain. The CyC-UAS concept demonstrates a high level of
potential that may supersede some conventional C-UAS techniques, specifically in the
domain of energy-saving. Therefore, it is recommended to continue research and devel-
opment efforts on the application of cyber-attacks in the C-UAS domain to maximize its
potential in C-UAS operation.
7.2. Future Work
To further enhance the realism and the effectiveness of CyC-UAS operation presented
in this article, it is recommended to (1) enhance the existing simulation model as well as to
(2) integrate the CyC-UAS concept with other existing technologies.
7.2.1. Simulation of CyC-UAS Performance with Differing or Variable Traversing Speed of
Adversarial UASs
To simplify the current simulation model in this article, it was assumed that all the
simulated adversarial UASs traverse towards the target at a constant speed. To increase the
realism of the simulation model, it is recommended to model the speed of the adversarial
UASs traversing towards the target to be at (1) different and (2) variable speeds.
7.2.2. Creation of a C2 Network to Link Multiple CyC-UAS Systems during
C-UAS Operation
The intent of linking multiple CyC-UAS is to provide stakeholders with a holistic
overview of the battle environment. This application is essential in the event of a concurrent
attack by multiple UASs. The creation of a simulation model is recommended to simulate
the integration of a C2 network and the CyC-UAS systems to gain insights into the capability
and limitations of the system.
7.2.3. Integration of CyC-UAS with FHSS System
Existing commercial UASs that utilize the WiFi frequency bands (2.4 GHz and 5 GHz)
are extremely vulnerable to CyC-UAS attack. Therefore, the manufacturers of commercial
UASs are moving towards adopting FHSS protocols as part of the transmission schemes.
It is recommended to explore existing FHSS decoding schemes and integrate them with
CyC-UAS techniques.
Drones 2022,6, 198 23 of 25
Author Contributions:
Conceptualization, C.H.L., D.L.V.B., and B.H.; methodology, C.H.L.; software,
C.H.L.; validation, C.H.L.; formal analysis, C.H.L.; investigation, C.H.L.; resources, D.L.V.B. and B.H.;
data curation, C.H.L. and C.T.; writing—original draft preparation, C.H.L.; writing—review and
editing, C.H.L., C.T., D.L.V.B. and B.H.; visualization, C.H.L.; supervision, D.L.V.B. and B.H.; project
administration, D.L.V.B. and B.H.; funding acquisition, D.L.V.B. and B.H. All authors have read and
agreed to the published version of the manuscript.
Funding: This research was funded by the DASN RDA Operational Energy Office.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Please contact the authors for data.
Acknowledgments:
Any opinions or findings of this work are the responsibility of the authors,
and do not necessarily reflect the views of the U.S. Department of Defense, the Singapore Armed
Forces, or any other organizations. Approved for public release; distribution is unlimited.
Conflicts of Interest: The authors declare no conflicts of interest.
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Unmanned aircraft systems (UAS), or unmanned aerial vehicles, often referred to as drones, have been experiencing healthy growth in the United States and around the world. The positive uses of UAS have the potential to save lives, increase safety and efficiency, and enable more effective science and engineering research. However, UAS are subject to threats stemming from increasing reliance on computer and communication technologies, which place public safety, national security, and individual privacy at risk. To promote safe, secure, and privacy-respecting UAS operations, there is an urgent need for innovative technologies for detecting, tracking, identifying, and mitigating UAS. A Counter-UAS (C-UAS) system is defined as a system or device capable of lawfully and safely disabling, disrupting, or seizing control of an unmanned aircraft or UAS. Over the past five years, significant research efforts have been made to detect, and mitigate UAS: detection technologies are based on acoustic, vision, passive radio frequency, radar, and data fusion; and mitigation technologies include physical capture or jamming. In this tutorial, we provide a comprehensive survey of existing literature in the area of C-UAS, identify the challenges in countering unauthorized or unsafe UAS, and evaluate the trends of detection and mitigation for protecting against UAS-based threats. The objective of this tutorial is to present a systematic introduction of C-UAS technologies, thus fostering a research community committed to the safe integration of UAS into the airspace system.