The
Internet
of
Things
(IoT)
has
revolutionized
various
sectors
by
enabling
seamless
interaction
between
devices.
However,
the
proliferation
IoT
devices
also
raised
significant
security
and
privacy
concerns.
Traditional
measures
often
fall
short
in
addressing
these
concerns
due
to
unique
characteristics
networks
such
as
heterogeneity,
scalability,
resource
constraints.
To
address
challenges,
this
survey
paper
first
explores
intersection
quantum
computing,
federated
learning,
6G
wireless
a
novel
approach
enhancing
privacy.
In
order
enable
several
secure
intelligent
applications,
with
its
superior
computational
capabilities,
can
strengthen
encryption
algorithms,
making
data
more
secure.
Federated
decentralized
machine
learning
approach,
allows
learn
shared
model
while
keeping
all
training
on
original
device,
thereby
This
synergy
becomes
even
crucial
when
integrated
high-speed,
low-latency
capabilities
networks,
which
facilitate
real-time,
processing
communication
among
vast
array
Second,
we
discuss
latest
developments,
offering
an
up-to-date
overview
advanced
solutions,
available
datasets,
key
performance
metrics,
summarizing
vital
insights,
trends
realm
securing
systems.
Third,
design
conceptual
framework
for
integrating
computing
adapted
networks.
Finally,
highlight
future
advancements
technologies
suggesting
potential
integration
7G,
implications
security,
paving
way
researchers
practitioners
field
security.
Future Generation Computer Systems,
Journal Year:
2024,
Volume and Issue:
160, P. 577 - 597
Published: June 13, 2024
The
Internet
of
Things
(IoT)
has
revolutionized
various
sectors
by
enabling
seamless
device
interaction.
However,
the
proliferation
IoT
devices
also
raised
significant
security
and
privacy
concerns.
Traditional
measures
often
fail
to
address
these
concerns
due
unique
characteristics
networks,
such
as
heterogeneity,
scalability,
resource
constraints.
This
survey
paper
adopts
a
thematic
exploration
approach
for
comprehensive
analysis
investigate
convergence
quantum
computing,
federated
learning,
6G
wireless
networks.
novel
intersection
is
explored
significantly
improve
within
ecosystem.
To
enable
several
secure,
intelligent
applications,
with
its
superior
computational
capabilities,
can
strengthen
encryption
algorithms,
making
data
more
secure.
Federated
decentralized
machine
learning
approach,
allows
learn
shared
model
while
keeping
all
training
on
original
device,
thereby
enhancing
privacy.
synergy
becomes
even
crucial
when
integrated
high-speed,
low-latency
capabilities
which
facilitate
real-time,
secure
processing
communication
among
many
devices.
Second,
we
discuss
latest
developments,
offering
an
up-to-date
overview
advanced
solutions,
available
datasets,
key
performance
metrics
summarizing
vital
insights,
challenges,
trends
in
securing
systems.
Third,
design
conceptual
framework
integrating
computing
adapted
Finally,
highlight
future
advancements
technologies
networks
summarize
implications
security,
paving
way
researchers
practitioners
field
security.
Cluster Computing,
Journal Year:
2024,
Volume and Issue:
27(7), P. 9065 - 9089
Published: April 16, 2024
Abstract
The
Internet
of
Things
(IoT)
is
a
vast
network
devices
with
sensors
or
actuators
connected
through
wired
wireless
networks.
It
has
transformative
effect
on
integrating
technology
into
people’s
daily
lives.
IoT
covers
essential
areas
such
as
smart
cities,
homes,
and
health-based
industries.
However,
security
privacy
challenges
arise
the
rapid
growth
applications.
Vulnerabilities
node
spoofing,
unauthorized
access
to
data,
cyberattacks
denial
service
(DoS),
eavesdropping,
intrusion
detection
have
emerged
significant
concerns.
Recently,
machine
learning
(ML)
deep
(DL)
methods
significantly
progressed
are
robust
solutions
address
these
issues
in
devices.
This
paper
comprehensively
reviews
research
focusing
ML/DL
approaches.
also
categorizes
recent
studies
based
highlights
their
opportunities,
advantages,
limitations.
These
insights
provide
potential
directions
for
future
challenges.
Frontiers in Communications and Networks,
Journal Year:
2025,
Volume and Issue:
6
Published: Feb. 4, 2025
Introduction
The
Internet
of
Things
(IoT)
is
a
new
technology
that
connects
billions
devices.
Despite
offering
many
advantages,
the
diversified
architecture
and
wide
connectivity
IoT
make
it
vulnerable
to
various
cyberattacks,
potentially
leading
data
breaches
financial
loss.
Preventing
such
attacks
on
ecosystem
essential
ensuring
its
security.
Methods
This
paper
introduces
software-defined
network
(SDN)-enabled
solution
for
vulnerability
discovery
in
systems,
leveraging
deep
learning.
Specifically,
Cuda-deep
neural
(Cu-DNN),
Cuda-bidirectional
long
short-term
memory
(Cu-BLSTM),
Cuda-gated
recurrent
unit
(Cu-DNNGRU)
classifiers
are
utilized
effective
threat
detection.
approach
includes
10-fold
cross-validation
process
ensure
impartiality
findings.
most
recent
publicly
available
CICIDS2021
dataset
was
used
train
hybrid
model.
Results
proposed
method
achieves
an
impressive
recall
rate
99.96%
accuracy
99.87%,
demonstrating
effectiveness.
model
also
compared
benchmark
classifiers,
including
Cuda-Deep
Neural
Network,
Cuda-Gated
Recurrent
Unit,
(Cu-DNNLSTM
Cu-GRULSTM).
Discussion
Our
technique
outperforms
existing
based
evaluation
criteria
as
F1-score,
speed
efficiency,
accuracy,
precision.
shows
strength
detection
highlights
potential
combining
SDN
with
learning
assessment.
