Deep Feature Fusion-Based Model for Real-Time Malicious Drone Identification
Lecture notes on data engineering and communications technologies,
Journal Year:
2025,
Volume and Issue:
unknown, P. 507 - 518
Published: Jan. 1, 2025
Language: Английский
Detecting Indoor Tiny Autonomous Malicious Drones within Critical Infrastructures: An Innovative Algorithm based on Harmonic Radar-Equipped Mini-Drones
WSEAS TRANSACTIONS ON INFORMATION SCIENCE AND APPLICATIONS,
Journal Year:
2024,
Volume and Issue:
21, P. 466 - 479
Published: Oct. 15, 2024
Critical
infrastructures
play
a
central
role
in
the
welfare
of
contemporary
societies
and
they
should
properly
function
24/7.
Since
their
is
so
important,
regularly
become
targets
malicious
parties,
terrorists,
industrial
spies,
even
hostile
governments.
In
this
paper,
scenario
cyber
or
physical
attacks
to
CIs
from
tiny
autonomous
drones
analyzed.
particular,
work
focuses
on
indoor
spaces,
protected
by
mini-drones.
The
mini-drones
are
equipped
with
harmonic
radar
run
novel
algorithm,
which
guides
them
scan
whole
area.
Assuming
that
behave
as
non-linear
systems,
transmit
signals
analyze
received
signals,
creating
system
3D
location
map
for
space.
consecutive
scans,
any
changes
indicate
drone
has
changed
location.
Simulated
results
comparisons
state-of-the-art
approaches
exhibit
cost-effectiveness
time
efficiency
proposed
scheme
well
its
limitations.
Language: Английский
SVM‐SFL based malicious UAV detection in wireless sensor networks
Concurrency and Computation Practice and Experience,
Journal Year:
2024,
Volume and Issue:
36(13)
Published: Feb. 15, 2024
Summary
In
the
modern
era,
unmanned
aerial
vehicle
(UAV)
based
wireless
sensor
networks
(WSN)
are
rising
technologies
in
communication.
Through
UAV,
sensed
data
can
be
forwarded
to
base
station.
However,
increase
network
users
leads
several
malicious
attacks
on
UAVs.
Hence,
it
affects
performance
of
a
WSN
platform
while
transmitting
private
information
through
Therefore,
proposed
study
intends
develop
an
effective
UAV
detection
approach
using
machine‐learning
algorithm.
Initially,
deployed
nodes
utilized
collect
environmental
data.
These
transmit
collected
UAV.
During
transmission,
generate
feed
packet
(authentication
parameter)
and
forward
along
with
information.
The
feedback
is
encrypted
proxy
re‐encryption
scheme
secure
input
packets
then
transmitted
Finally,
decrypted
attains
actual
From
received
data,
classification
performed
support
vector
machine
shuffled
frog
leap
(SVM‐SFL)
approach.
implemented
NS3
Python
tool,
results
analyzed
by
evaluating
matrices.
Compared
other
existing
methods,
obtained
improved
terms
accuracy
(98.61%),
precision
(98.5%),
sensitivity
(98.63%),
F‐measure
(98.62%).
Language: Английский
Hybridizing Long Short-Term Memory and Bi-Directional Long Short-Term Memory Models for Efficient Classification: A Study on Xanthomonas axonopodis pv. phaseoli (XaP) in Two Bean Varieties
Agronomy,
Journal Year:
2024,
Volume and Issue:
14(7), P. 1495 - 1495
Published: July 10, 2024
This
study
was
conducted
on
Xanthomonas
axonopodis
pv,
which
causes
significant
economic
losses
in
the
agricultural
sector.
Here,
we
a
common
bacterial
blight
disease
caused
by
phaseoli
(XaP)
pathogen
Üstün42
and
Akbulut
bean
genera.
In
this
study,
total
of
4000
images,
healthy
diseased,
were
used
for
both
breeds.
These
images
classified
AlexNet,
VGG16,
VGG19
models.
Later,
reclassification
performed
applying
pre-processing
to
raw
images.
According
results
obtained,
accuracy
rates
pre-processed
VGG19,
VGG16
AlexNet
models
determined
as
0.9213,
0.9125
0.8950,
respectively.
The
then
hybridized
with
LSTM
BiLSTM
new
created.
When
performance
these
hybrid
evaluated,
it
found
that
more
successful
than
simple
models,
while
gave
better
LSTM.
particular,
VGG19+BiLSTM
model
attracted
attention
achieving
94.25%
classification
emphasizes
effectiveness
image
processing
techniques
agriculture
field
detection
is
important
dataset
literature
evaluating
Language: Английский