Performance Evaluation of Deep Learning Models for Classifying Cybersecurity Attacks in IoT Networks
Informatics,
Год журнала:
2024,
Номер
11(2), С. 32 - 32
Опубликована: Май 17, 2024
The
Internet
of
Things
(IoT)
presents
great
potential
in
various
fields
such
as
home
automation,
healthcare,
and
industry,
among
others,
but
its
infrastructure,
the
use
open
source
code,
lack
software
updates
make
it
vulnerable
to
cyberattacks
that
can
compromise
access
data
services,
thus
making
an
attractive
target
for
hackers.
complexity
has
increased,
posing
a
greater
threat
public
private
organizations.
This
study
evaluated
performance
deep
learning
models
classifying
cybersecurity
attacks
IoT
networks,
using
CICIoT2023
dataset.
Three
architectures
based
on
DNN,
LSTM,
CNN
were
compared,
highlighting
their
differences
layers
activation
functions.
results
show
architecture
outperformed
others
accuracy
computational
efficiency,
with
rate
99.10%
multiclass
classification
99.40%
binary
classification.
importance
standardization
proper
hyperparameter
selection
is
emphasized.
These
demonstrate
CNN-based
model
emerges
promising
option
detecting
cyber
threats
environments,
supporting
relevance
network
security.
Язык: Английский
An effective IDS using CondenseNet and CoAtNet based approach for SDN-IoT environment
Dimmiti Srinivasa Rao,
Ajith Jubilson Emerson
Computers & Electrical Engineering,
Год журнала:
2025,
Номер
123, С. 110305 - 110305
Опубликована: Апрель 1, 2025
Язык: Английский
Exploring Graph Neural Networks for Robust Network Intrusion Detection
Procedia Computer Science,
Год журнала:
2025,
Номер
258, С. 3630 - 3639
Опубликована: Янв. 1, 2025
Язык: Английский
Advanced Hybrid Techniques for Cyberattack Detection and Defense in IoT Networks
Security and Privacy,
Год журнала:
2024,
Номер
unknown
Опубликована: Окт. 1, 2024
ABSTRACT
The
Internet
of
Things
(IoT)
represents
a
vast
network
devices
connected
to
the
Internet,
making
it
easier
for
users
connect
modern
technology.
However,
complexity
these
networks
and
large
volume
data
pose
significant
challenges
in
protecting
them
from
persistent
cyberattacks,
such
as
distributed
denial‐of‐service
(DDoS)
attacks
spoofing.
It
has
become
necessary
use
intrusion
detection
systems
protect
networks.
Existing
IoT
face
many
problems
limitations,
including
high
false
alarm
rates
delayed
detection.
Also,
datasets
used
training
may
be
outdated
or
sparse,
which
reduces
model's
accuracy,
mechanisms
not
defend
when
any
is
detected.
To
address
new
hybrid
deep
learning
machine
methodology
proposed
that
contributes
detecting
DDoS
spoofing
attacks,
reducing
alarms,
then
implementing
defensive
measures.
In
consists
three
stages:
first
stage
propose
method
feature
selection
consisting
techniques
(correlation
coefficient
sequential
selector);
second
model
by
integrating
neural
with
classifier
(cascaded
long
short‐term
memory
[LSTM]
Naive
Bayes
classifier);
third
stage,
improving
defense
blocking
ports
after
threats
maintaining
integrity.
evaluating
performance
methodology,
(CIC‐DDoS2019,
CIC‐IoT2023,
CIC‐IoV2024)
were
used,
also
balanced
obtain
effective
results.
accuracy
99.91%,
99.88%,
99.77%
was
obtained.
cross‐validation
technique
test
ensure
no
overfitting.
proven
its
provides
powerful
solution
enhance
security
can
applied
fields
other
attacks.
Язык: Английский