A Review of Security Threats of Unmanned Aerial Vehicles and Mitigation Steps

Abstract

The use of unmanned aerial vehicles (UAVs) has increased exponentially over the last decade for a broad range of applications. The recent commercial availability of a new generation of small UAVs has emphasised the growing threat posed by these machines. This paper is aimed at reviewing the security threats posed by UAVs in areas such as terrorist attacks, illegal surveillance and reconnaissance, smuggling, electronic snooping, and mid-air collisions, in addition to discussing on the categories of UAV intrusions in terms of intention and level of sophistication of the operators. Mitigation steps for UAV intrusions are also discussed, focusing on geofencing, detections systems (radar, and acoustic, radio frequency (RF) emission and electro-optical (EO) sensing), electronic defences (command link jamming and appropriation, and Global Navigation Satellite System (GNSS) jamming and spoofing), and kinetic defences (shooting down UAVs and net capture using interceptor UAVs).

Full text

A REVIEW OF SECURITY THREATS OF UNMANNED AERIAL VEHICLES AND
MITIGATION STEPS
Dinesh Sathyamoorthy
Science & Technology Research Institute for Defence (STRIDE), Ministry of Defence, Malaysia
E-mail: dinesh.sathyamoorthy@stride.gov.my
ABSTRACT
The use of unmanned aerial vehicles (UAVs) has increased exponentially over the last decade for a
broad range of applications. The recent commercial availability of a new generation of small UAVs
has emphasised the growing threat posed by these machines. This paper is aimed at reviewing the
security threats posed by UAVs in areas such as terrorist attacks, illegal surveillance and
reconnaissance, smuggling, electronic snooping, and mid-air collisions, in addition to discussing on
the categories of UAV intrusions in terms of intention and level of sophistication of the operators.
Mitigation steps for UAV intrusions are also discussed, focusing on geofencing, detections systems
(radar, and acoustic, radio frequency (RF) emission and electro-optical (EO) sensing), electronic
defences (command link jamming and appropriation, and Global Navigation Satellite System (GNSS)
jamming and spoofing), and kinetic defences (shooting down UAVs and net capture using interceptor
UAVs).
Keywords: Unmanned aerial vehicles (UAVs); security threats; geofencing, detection systems;
electronic and kinetic defences.
1. INTRODUCTION
Unmanned aerial vehicles (UAVs), also known as drones, are aircrafts that do not carry any crew, but
rather, are operated remotely by human operators, or autonomously via preprogrammed software or
robots. UAVs vary widely in size and capacity, and have become increasingly prevalent. Their use
has increased exponentially over the last decade for a broad range of applications, including
cartography and mapping, inspection of remote power lines and pipelines, delivery services,
telecommunications relay, police surveillance, traffic monitoring, border patrol and reconnaissance,
and emergency and disaster monitoring [1, 2].
From a military perspective, UAVs, which can be recoverable or expendable, are generally used to
operate in dangerous or hostile territories, without endangering the operators. It is employed for
surveillance and reconnaissance, information collection, detection of mines, and for combat purposes.
UAVs hold many attractions for the military. They are generally smaller, lighter and cheaper as
compared to manned aerial vehicles as they do not need equipment to support a crew. UAVs can also
be used for many hours in a stretch, while switching operators [1, 2].
The recent commercial availability of a new generation of small UAVs, often quadcopters or some
other form of rotorcraft, has emphasised the growing threat posed by these machines. These UAVs
can be easily purchased over the internet and can carry a payload of up to a few kilogrammes. They
are cheap, easy to fly and small enough to evade traditional security surveillance. A recreational UAV
costing a few hundred dollars can be turned into an aerial equivalent of an improvised explosive
device (IED), or be equipped with a camera and data downlink to become a spy UAV [3-7].

This paper is aimed at reviewing the security threats posed by UAVs, in addition to discussing on the
categories of UAV intrusions in terms of intention and level of sophistication of the operators.
Mitigation steps for UAV intrusions are also discussed, focusing on geofencing, detections systems,
and electronic and kinetic defences.
2. SECURITY THREATS
A few recent incidents have highlighted potential dangers posed by UAVs. In September 2013, a
UAV flew over a crowd and crash-landed right in front of the German Chancellor during a campaign
rally, which turned out to be a stunt staged by a protester (Figure 1(a)) [8]. In January 2015, a UAV
accidentally crashed into the compound of the White House, after evading the White House’s radar
that was calibrated to warn of much bigger threats, such as airplanes and missiles [9]. Four months
later, in May 2015, a man was arrested by the US Secret Service for flying a UAV close the White
House [10]. In March 2015, a UAV was illegally flown in the vicinity of Malaysia's Kuala Lumpur
International Airport (KLIA), taking photographs of an airplane landing [11]. In April 2015, a
protester landed a UAV on the roof of the Japanese Prime Minister's office. It carried a container of
sand with traces of non-harmful radioactive isotopes (Figure 1(b)) [12].
(a) (b)
Figure 1: UAV intrusions: (a) The UAV that crash-landed in front of the German Chancellor in
September 2013 [8]. (b) The UAV that landed on the roof of the Japanese Prime Minister's office [12].
These incidents, while harmless, are alarming and serve as a warning of the potential threats of UAVs
at the hands of terrorists. Miasnikov [13] summarised a set of advantages that make UAVs attractive
to terrorists:
 Possibility to attack targets that are difficult to reach by land
 Possibility of carrying out a wide-scale attack aimed at inflicting a maximum death rate on a
population
 Covertness of attack preparation and flexibility in choice of a UAV launch site
 Possibility of achieving a long range and acceptable accuracy with relatively inexpensive and
increasingly available technology
 Poor effectiveness of existing air defences against targets such as low-flying UAVs
 Relative cost effectiveness of UAVs as compared with ballistic missiles and manned airplanes
 Possibility of achieving a strong psychological effect by scaring people and putting pressure
on politicians.
An attacker could strap guns or explosives to a UAV, and fly it into people or structures to inflict
physical damage or loss of life. The targets of these attacks may be individuals, buildings or
transportation infrastructure such as commercial airliners [3, 5, 7, 14]. In September 2011, a model
hobbyist was arrested and accused of planning to build explosive-laden UAVs to attack the Pentagon
and US Capitol [15]. In February 2014, a Moroccan national was caught in Connecticut, US for
plotting to fly UAVs with bombs into a school and a government building [16]. At a conference
hosted by the US Department of Homeland Security in January 2015, counterterrorism officials

displayed several models of UAVs that were fitted with explosives (Figure 2) [17]. Furthermore, an
attacker could use a UAV to spray a weaponised chemical or biological agent over a crowd of people
or in a urban area [3, 7, 13, 14].
Figure 2: A UAV equipped with 3 lbs of mock explosive.
Source: Poulsen [17]
In addition to the threats posed by terrorists, defence forces are being offered ever more capable
UAVs in the micro and mini sectors [5]. For example, Aeronautics developed a UAV bomb, known as
Orbiter IK (Figure 3). It is a loitering mini-UAV with a wingspan of 2.2 m and can carry up to 2 kg of
explosives in its fuselage. It can be controlled by an operator, or is capable of being given a waypoint
and independently scanning the area to detect and destroy a stationary or moving target [18].
Figure 3: Aeronautics’ Orbiter IK UAV, which can carry up to 2 kg of explosives in its fuselage.
Source: Air Recognition [18]
An operator could also use a UAV to reconnoitre targets for attack, or monitor the actions of
individuals or law enforcement (Figure 4) [3, 4, 14]. Off-the-shelf UAVs are being used by both sides
of the Ukrainian civil war for intelligence gathering missions [19]. The rapid spread of hobbyist
UAVs makes this scenario both the most likely threat involving UAVs and the most difficult to
identify. In the past few years, there have been many cases in which it was difficult to determine
whether the UAV was being used for recreational use, newsgathering, activism, or for an activity that
could result in harming public safety [3, 4, 14].
There have also been a number of cases where criminal organisations or individuals have used UAVs
to smuggle illicit material, usually across borders or into prisons [4, 14]. UAVs were spotted flying
over the walls of prisons in Quebec, Canada [20] and Melbourne, Australia [21] in November 2013
and March 2014 respectively, while staff at a prison in Bedfordshire, UK discovered a crashed UAV
that appeared to be delivering contraband [4]. In January 2015, a smuggler's UAV flying from Mexico
crash-landed just south of the US border city of San Ysidro, California in a failed drug delivery
(Figure 5) [22].

Figure 4: A UAV fitted with a camera for illegal surveillance.
Source: Yutim [11]
Figure 5: The UAV carrying small packages of drugs that crashed near the US-Mexico border.
Source: Valencia & Martinez [22]
UAVs could also be used for electronic snooping. At the Black Hat security conference held in
Singapore in March 2014, SensePost unveiled its Snoopy UAV, which can steal data from
unsuspecting smartphone users. The UAV uses the company's software, which is installed on a
computer attached to a UAV. The code can be used to hack smartphones and steal personal data
without the user's knowledge. This method could be used to particular effect in a crowded
environment where many people have their cell phones automatically searching for WiFi networks
[14, 23]. In February 2015, it was revealed that AdNear, a Singapore-based marketing firm, was using
UAVs to determine the locations and movements of mobile phones by collecting signal strengths and
other wireless data from passersby below. With this data in hand, AdNear could then deliver hyper-
targeted advertisements and other promotions to potential customers as they are walking past a
storefront in the hopes of incentivising a customer to enter the store [24].
Pilots have expressed concern regarding the proliferation of commercially available small UAVs. For
example, if a UAV got into the engine of an aeroplane, it could stop the engine. Furthermore, as
UAVs get larger and more capable, the potential and consequences of mid-air collisions increase [4,
11]. Helicopter pilots have reported several near-miss incidents with UAVs [25], while there have
cases of near-misses between commercial passenger jets and UAVs [26, 27]. In July 2015, it was
reported that hobbyist UAVs that were flown in into forest fires areas, allegedly to capture images of
the fires, were forcing firefighting helicopters to be grounded until the UAVs could be removed from
the area (Figure 6) [28].

Figure 6: Warnings about flying UAVs in forest fire areas issued by the US Forest Service.
Source: BBC [28]
Humphreys [6] divided the threat of UAVs into three categories based on the operators’ intention and
level of sophistication:
 Category 1: Accidental intrusions, whether the UAV operators are sophisticated or not
 Category 2: Intentional intrusions by unsophisticated operators
 Category 3: Intentional intrusions by sophisticated operators, those who can assemble a UAV
from components, and modify its hardware and software.
Mitigation of the first two categories of intrusions can be done using geofencing, and detection
systems such as radar, and acoustic, radio frequency (RF) emission and electro-optical (EO) sensing.
However, the third category of UAV intrusions is much more difficult to counter. A sophisticated
attacker could mount a kamikaze-style attack against a sensitive target using a fixed-wing powered
glider with an explosive lightweight payload. The UAV glider could be launched from a significant
distance from the target. It could cut its engine on the final approach to evade acoustic detectors, and
could be built of poorly-radar-reflective material (e.g., styrofoam) to evade radar detection. The UAV
could be configured to operate under radio silence, ignoring external RF control commands and
emitting no RF signals of its own. The UAV would thus be difficult to detect and would be
impervious to command link jamming or hijacking. Moreover, the attacker could configure the
autopilot to ignore Global Navigation Satellite System (GNSS) signals during the final approach to
the target, relying instead on an inexpensive magnetometer-disciplined inertial navigation system
(INS). Such a modification would render GNSS jamming or spoofing useless during the final
approach [6].
Another risk factor with the commercialisation of UAVs is the potential for attackers to intercept the
signals of a legitimate UAV and then take control of the UAV. In 2009, Iraqi militants used a Russian
software developed to steal satellite television signals to intercept real-time video feed from US
surveillance UAVs flying over Iraq. In 2011, a keylogging computer virus infected the ground
stations of US Predator and Reaper UAVs [3, 29]. While there is a large technological leap between
either intercepting video feeds or infecting a computer with a virus, and being able to take over a
UAV’s command link, there is certainly a potential risk that attackers will develop this capability.
This threat may be small for the US UAV fleet of Predators and Reapers because their command links
are encrypted [30]. However, the unencrypted command links of commercial UAVs could easily be
hacked by attackers. Moreover, Shephard et al. [31] demonstrated that GNSS spoofing can be used to
hijack UAVs. With increasing proliferation of commercial UAVs, UAV hacking and hijacking is a
real threat to consider [3, 32].

3. MITIGATION STEPS
3.1 Geofencing
Commercial UAV manufacturers can play a key role by implementing GNSS-enforced geofences
within their systems that prevent their UAVs from being flown within exclusion zones around
airports, sports stadiums, government buildings, military bases and other security-sensitive sites [6,
14]. For example, DJI, one of the biggest UAV manufacturers, has embedded geofencing software in
its UAVs that prevents them from flying over thousands of sites worldwide where UAV operation is
illegal [33] (Figure 7). This would be an effective mitigation step for UAV intrusions from
unsophisticated operators. However, sophisticated operators could hack the software to disable the
geofencing [6, 14].
Figure 7: Exclusion zones for Malaysia set in DJI’s geofencing software.
Source: DJI [14]
3.2 Detection Systems
3.2.1 Radar
Existing air surveillance radar systems are ineffective against small UAVs as they are developed to
detect large aerial platforms moving at high speeds. As UAVs fly at similar speeds and altitudes to
birds, the two could be indistinguishable from each other [3, 6, 34]. For high-risk events and known
appearances of high-risk personnel, it may be necessary to bring in radars that have the fidelity to
detect such small objects, such as the Blighter system developed by Plextek (Figure 8), and operators
trained to distinguish between birds and UAVs [3, 35]. Further complicating matters, to avoid radar
detection, UAVs may be built using poorly-radar-reflective materials and fly below the altitude of 100
ft [6].
Figure 8: The Blighter radar system developed by Plextek for detection of UAVs.
Source: Baker [35]

3.2.2 Acoustic Sensing
Acoustic sensors operate by identifying the distinct noise made by the motors that drive the propellers
of UAVs [4, 6]. DroneShield's acoustic sensor (Figure 9) was designed to provide high detection rates
with low false alarms. It contains a database of common UAV acoustic signatures so that false alarms
are reduced (e.g., lawn mowers and leaf blowers) and in many cases the type of UAV is also included
in the alert [36]. This system is being used by law enforcement officers in the US to enforce “no UAV
zones” [4]. A significant advantage of acoustic sensing is that is that it has low cost, even when
implemented as a network of sensing devices placed around the protection perimeter [6]. However, it
is incapable of detecting fixed-wing UAVs operating as gliders or rotorcraft UAVs in free fall.
Sophisticated operators could change the sound signature of a UAV by buying different propellers or
making other modifications. It is unlikely to offer reliable detection at more than a 500 m standoff
range and is ineffective in urban areas with a lot of ambient noise [6, 37]. Furthermore, it can be
spoofed through playback of an audio recording of a UAV [6].
Figure 9: The acoustic sensor developed by DroneShield for detection of UAVs.
Source: DroneShield [36]
3.2.3 Radio Frequency (RF) Emission Sensing
UAVs typically send data back to their controller through a wireless data link. Using a directional
antenna or a network of synchronised ground stations, such RF emissions can be detected and located
(Figure 10). In order to be economical and offer rapid detection, the system must have some
knowledge of the emission centre frequency and bandwidth, which are regulated for commercial
UAVs [3, 6, 7]. Drone Labs’ DD610AR UAV detection system employs RF emission sensing of a
UAV’s command and data links to identify the coordinates of the UAV and its operator, and the
unique identifier of the UAV, which can be used to prove that a particular incursion was done using a
specific UAV (Figure 11) [37, 38]. However, RF emission sensing can be easily evaded by
sophisticated operators by maintaining radio silence [6].
3.2.4 Electro-Optical (EO) Sensing
EO sensors in the form of optical and thermal cameras can be quite effective at detecting UAVs [6].
Dedrone's DroneTracker (Figure 12), which combines optical and thermal cameras, can be used to
form an EO sensing network to increase the chances of detecting a UAV [39]. However, optical
cameras would have a difficult time distinguishing birds from UAVs. By utilising computer
algorithms that look at flight patterns, it is expected that a bird will fly a more random pattern than a
UAV would. However, this notion fails in a place where birds glide, such as seagulls, which ride wind
currents and stay at a steady level, and this fools optical systems. Furthermore, hobbyist UAVs are
mostly made of plastic and use electric motors, and thus, do not produce a lot of heat. Thermal
cameras would more likely detect a bird a UAV in most cases [37].

Figure 10: Detection of a UAV via RF emission sensing.
Source: Wiesback [7]
Figure 11: Drone Labs’ DD610AR RF emission sensing system for detection of UAVs.
Source: Drone Labs [38]
Figure 12: Dedrone’s DroneTracker can be used to form an EO sensing network.
(Source: Dedrone [39])
3.3 Electronic Defence
3.3.1 Command Link Jamming and Appropriation
Modern commercial UAVs are controlled by one or more wireless links to the operator’s control
equipment. Traditional radio control (RC) controllers are still used as a backup means of control even
for UAVs capable of a high degree of autonomy. These controllers send low-level commands to the
autopilot system, or directly to the UAV’s motors or servos that actuate the UAV’s control surfaces
[6]. Jamming a UAV' command link could effectively eliminate the ability of the UAV operator to
conduct accurate targeting within the denied area [3, 6, 14].

Figure 13 shows how a jammer operates by raising the RF noise level in the vicinity of the area to be
defended. The further the UAV travels from the controller, the signal from the controller becomes
weaker; while the closer the UAV moves towards the jammer, the RF noise level from the jammer
becomes stronger. This makes it harder for the controller to overcome the RF noise of the jammer.
Once the signal from the controller falls below the RF noise level, the operator would no longer be
able to control the UAV. In order to overcome the jammer, the operator would have to increase the
transmission power or get closer to the target area, both of which increases the chances of detection
[3, 40].
To avoid the effects of command link jamming, the UAV can simply transition to an autonomous
operational mode soon after takeoff, accepting no further external commands [6, 14]. Furthermore,
command link jamming could also disrupt other communication devices, such as mobile phones and
mobile wireless devices. To help mitigate the interference, jamming could be used with detection
systems so that the jammer is only switched on of if UAVs are detected [3].
Figure 13: Simplified RF jamming effect.
Source: Card [3]
Command link appropriation could be used to take control of a UAV. However, to appropriate the
command link, the defender would need first need to determine the communications protocol, channel
(from a potentially large number of channels (e.g., 100)) and code being used [6, 7]. Furthermore, the
command link could be encrypted [6].
3.3.2 Global Navigation Satellite System (GNSS) Jamming and Spoofing
Virtually all modern commercial UAVs capable of autonomous flight are navigated using GNSS
satellites. Civilian GNSS signals are weak, rendering them susceptible to jamming [41], and
unencrypted and unauthenticated, rendering them susceptible to spoofing [42]. The defender could
take advantage of the weak security of GNSS to confuse or commandeer the UAV (Gettinger, 2015;
Humphreys, 2015). Shephard et al. [31] demonstrated that GNSS spoofing could be used to take over
a UAV by transmitting a series of false coordinates (Figure 14).
GNSS jamming would force attackers to operate either using line-of-sight (LOS) RC control, or non-
GNSS autonomous navigation. LOS control exposes the operator to visual detection and recognition,
and can be denied by command link jamming. Non-GNSS autonomous navigation in an unmapped
environment is either expensive (e.g., a navigation- or tactical-grade INS initialised with GNSS) or
can only be applied accurately over short time intervals (e.g., a microelectromechanical systems
(MEMs)-grade magnetometer-disciplined INS) [6].

Figure 14: Global Positioning System (GPS) spoofing conducted by Shephard et al. [31].
However, GNSS jamming would cause substantial collateral damage, denying the use of civilian
GNSS signals in a wide area around the protected site. For example, automobile commuters would be
denied use of their in-car navigation systems, cell towers could no longer be synchronised by GNSS,
and approaches to airports could no longer benefit from GNSS for safety and efficiency. GNSS
spoofing would potentially be even more damaging to surrounding civil systems [6, 43, 44].
Furthermore, an attacking UAV can simply disregard GNSS signals during the final approach to the
target, relying on a low-cost MEMS-grade magnetometer-disciplined INS, which over a 60 s interval
may only exhibit a 5 m drift in perceived location [6].
3.4 Kinetic Defence
Commercial UAVs are in general fragile in the face of hard-contact kinetic attacks, such as small
guided missiles, cannon-fired smart munitions, lasers and firearms [5, 6]. However, in urban
environments, where attacks are more likely, law enforcement and military will be averse to shooting
UAVs down because any projectile used may cause collateral damage when it returns to the ground
[3, 6, 14]. Furthermore, a kinetic model for defending a target in an urban environment could require
several systems with trained operators to be in place along likely air avenues of approach in order to
adequately defend the area. This would increase the cost of defending against UAV threats [3].
Net capture of UAVs by interceptor UAVs has been demonstrated (Figure 11), though it not yet
considered as a mature technology. Net capture has the additional benefit of enabling eviction of the
intruder UAV from the vicinity of the site to be protected [6, 45].
Figure 15: A Malou Tech MP200 interceptor UAV catching an intruder UAV with a net.
Source: Gayle [6]

4. CONCLUSION
This manuscript provided a review of the security threats of UAVs and mitigation steps. UAV
intrusions can be divided into three categories based on the operators’ intention and level of
sophistication: accidental intrusions, whether the UAV operators are sophisticated or not; intentional
intrusions by unsophisticated operators; and intentional intrusions by sophisticated operators.
Mitigation steps for UAV intrusions include geofencing by UAV manufacturers, detection systems
(radar, and acoustic, RF emission and EO sensing), electronic defences (command link jamming and
appropriation, and GNSS jamming and spoofing), and kinetic defences (shooting down UAVs and net
capture using interceptor UAVs).
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