IEEE - Aerospace and Electronic Systems - May 2022 - 22

A Review of Security Incidents and Defence Techniques Relating to the Malicious Use of Small UASs
data. SafeShore [110] is a project, which integrates together
various detection methods with intelligent data fusion in an
attempt to address maritime border security issues. The
Aladdin project [111] looks to develop a system to detect,
localize, classify, and neutralize suspicious UASs. Detection,
classification, and localization uses radar, optronic,
acoustic, and other sensors, while the neutralization aspect
is said to include jamming, hacking, and physical methods.
The project also includes the development ofa deep learning
algorithm to fuse the different sensor data together. In academia,
Shi et al. [112] used multiple sensors to perform UAS
detection, localization, and jamming as a counter measure.
Acoustic, imagery, and RF signals are collected and processed
using support vector machine in parallel. The data
results are fused using a logical OR operation to produce a
final detection decision. Diamantidou et al. [53] proposed
fusing features from multiple sensors for UAS classification
and detection. They do this using a framework of a neural
network to merge extracted features and increase accuracy
from utilizing the benefits of more than one sensor. Zhang
et al. [113] performed the small UAS detection, tracking,
and localization with a multisensor approach. It uses visual
imagery and deep learning object detection to define a
bounding box. LiDAR is used to calculate distance measurements
from the pixel data and thermal data to detect the
bounding box and then track it. Although Zhang et al. are
not fusing the data in order to improve detection and classification,
they show how a combination ofdifferent sensor data
can provide a richer overall system capable ofmultiple functions.
It is worth noting that while many ofthese sensors and
systems are being researched academically, the ideal solution
practically would be to utilize existing technology for
aviation surveillance. This may involve a software upgrade
and use ofexisting sensors or a bolt on capability depending
on the capacity ofthe original system.
COUNTER-MEASURE METHODS
We split counter measures into three main categories, physical
measures, jamming, and cyberattacks. First, we consider
physical counter measures. Brust et al. [114] used a swarm
ofUASs, capable of self-organising when a malicious UAS
is detected, to chase the malicious UAS. They achieve this
using clustering and develop a system resilient to the loss of
communications with the swarm. Rothe et al. [115] presented
a counter UAS method through catching a UAS in
mid-air by using a formation of UAS and a net. Prior work
has concentrated on a single UAS with nets but this limits
the net size. This research was limited to indoor testing but
outdoor experiments are planned for the future. Physical
counter measures must, such as nets must be light enough to
be carried by the UAS.
The second method of counter measure considered is
jamming. Multerer et al. [116] combined an FMCW radar
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for UAS detection and a directional 2.4 GHz jammer to
produce an antidrone system. The jammer aims to disrupt
the control signal between the UAS and the controller and
the tracking function within the system allows the signal
to continue to be jammed. Shi et al. [112] used RF jamming
as a counter measure for malicious UASs in a wider
multisensor system, which we presented earlier. The system
receives the location of the UAS and calculates an
azimuth angle for the jammer antenna to be steered
toward. The goal of the jammer is to break the communication
signal between the UAS and the controller. They
observe the issues of controlling the jamming power and
the unintentional consequences of interfering with other
wireless communications in the same frequency band. Parlin
et al. [117] proposed the protocol aware jamming for
UAS controller signals, which normally employ some
form offrequency hopping. They used an SDR and experiments
proved that a protocol aware jammer was more
effective than sweep jamming and required less transmit
power. Li et al. [118] targeted UAS eavesdroppers by getting
physically close to the malicious UAS and transmitting
a jamming signal to disrupt it. Sliti et al. [119]
proposed three types of attack scenario including jamming
the control link between the UAS and its controller, which
usually instigates the UAS returning to a " home " location.
The " home " location is something preset, and therefore,
an attacker could use this to their advantage. We also note
that a physical solution to the problem may be preferable
and more practical than jamming if you can first track the
controller as this would enable a police or security officer
to visit the tracked location.
Sliti et al. also consider cyberattack methods for countering
the threat of small UAS. They propose a black hole
attack whereby network traffic is disregarded and a replay
attack, which repeats real communication that is happening
between the controller and the UAS. Chen et al. [120]
considered the false data injection onto UAS navigation
algorithms on open source flight control systems. In particular
they show that they can compromise magnetometer
measurements to directly affect the UASs state estimation
and, therefore, seriously compromising navigation, stability,
and power consumption. Kwon et al. [121] specifically
targeted the microair-vehicle communication protocol,
which is an open-source communication protocol for
UASs. It is widely used with ground control-based systems.
They prove an attack that can disable a UAS and its
subsequent mission by exploiting a vulnerability with the
protocol. As always when attack methodologies progress
we see security measures progressing in parallel. One
example is the work of Lei et al. [122], which proposed a
new lightweight authentication protocol for UASs. Westerlund
and Asif [123] investigated Wi-Fi-based vulnerabilities
with two commercially available UASs that
operate on Wi-Fi. Cyber-based attacks, including denial
of service, deauthentication, man in the middle, root
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