
various critical systems and devices of the CI and/or
install malicious software (electronic war), destroy
parts of the CI by e.g. setting fire (physical damage),
etc. In other words, it is like a virus inside a human
body. After completing its mission, the tiny
malicious drone may autonomously leave the CI and
return to its base, or it may be picked up by the
malicious staff member.
As it can be understood, such threats are very
serious and should be efficiently tackled. Our
previous research has focused on the physical
security of CIs. In particular, in [1] a real-time threat
assessment framework has been proposed to protect
CIs from trucks carrying explosive substances. In
[2] an innovative screening architecture has been
introduced to protect CIs from various threats, such
as guns, explosives, and radioactive substances. The
current work extends our previous research by
detecting tiny autonomous malicious drones. The
proposed scheme focuses on indoor spaces of CIs.
More specifically, it is assumed that the CI is
protected by a mini-drone. The mini-drone is
equipped with a harmonic radar and runs the
proposed algorithm, which guides the mini-drone to
scan the whole indoor space by moving on a 3D
grid. It is also assumed that the tiny malicious drone
behaves as a non-linear system. Each time the mini-
drone visits a new node of the grid, it transmits a
signal and analyses the received signal. After
visiting all nodes, the mini-drone creates a non-
linear system location map for the whole indoor
space. The 3D location map contains all non-linear
devices, including the malicious drone. In the next
scans, any changes on the 3D location map indicate
that the malicious drone has moved to a new
location. Experimental results and comparisons to
state-of-the-art approaches exhibit the advantages of
the proposed scheme.
To summarize, this paper offers the following
major contributions:
It examines the case of tiny autonomous
malicious drones, which may not send or
receive signals. This case has not been
thoroughly studied in the literature.
It proposes a novel algorithm, which guides the
mini-drone to scan the whole indoor space and
create a 3D location map.
Through extensive simulations, the study not
only validates the effectiveness of the proposed
algorithm but also compares it with state-of-the-
art approaches, highlighting its advantages and
limitations, thereby contributing valuable
insights for future research in drone detection
technology.
The rest of the paper is organized as follows:
Section 2 provides related work and Section 3
describes the proposed scheme. Simulated results
and extensive comparison to state-of-the-art
methods are presented in Section 4. Finally, Section
5 concludes this paper.
2 Related Work
In the literature, there are some works related to
malicious drones. In particular, [3] introduces an
approach for identifying critical drones by
leveraging distributed features, communication
intensity, and communication scale. Initially, a
dynamic communication prediction network is
constructed for drone swarms. Then, a dynamic
giant connected component-based scale-intensity
centrality method is proposed. In [4] an anti-RF
solution that possesses the capability to identify,
detect, and disrupt the communication link between
a miniature drone and its remote controller is
presented. This countermeasure has been seamlessly
integrated into a Software Defined Radio platform
to secure No Fly Zones (airports, public events,
etc.). In [5] various cybercrime usages of drones are
examined and the requirements of future security
systems are discussed. In [6] a computer vision-
powered monitoring system employs a supervised
machine intelligence model and SqueezeNet, a deep
neural network-based image embedder, to identify a
malevolent UAV carrying an extraneous payload. In
[7] detection of malicious UAVs is achieved by a
machine-learning algorithm. Initially, sensor nodes
deployed in a Wireless Sensor Network gather
environmental data and send them to the UAV. To
ensure data security, a proxy re-encryption scheme
encrypts the feedback packet containing the sensed
input data. Finally, the feedback packet undergoes
decryption at the base station, revealing the actual
input information. In [8] the viability of employing
wireless localization methods for identifying drones
engaged in location spoofing attacks is explored.
GhostBuster, a modular solution designed to detect
rogue RID-enabled drones is introduced and a
comprehensive experimental campaign, utilizing
open-source data derived from real drone flights is
carried out. In [9] a dataset encompassing five
classes, including images of airplanes, birds, drones,
helicopters, and malicious UAVs is utilized.
WSEAS TRANSACTIONS on INFORMATION SCIENCE and APPLICATIONS
DOI: 10.37394/23209.2024.21.42
Athanasios N. Skraparlis, Klimis S. Ntalianis,
Maria S. Ntaliani, Filotheos S. Ntalianis,
Nikos E. Mastorakis
It investigates the protection of indoor CI
spaces by mini-drones equipped with harmonic
radar, an approach that is much more efficient
and flexible compared to the state-of-the-art.