Engineering Technology & Applied Science Research,
Год журнала:
2024,
Номер
14(6), С. 19001 - 19008
Опубликована: Дек. 2, 2024
Most
traditional
IP
networks
face
serious
security
and
management
challenges
due
to
their
rapid
increase
in
complexity.
SDN
resolves
these
issues
by
the
separation
of
control
data
planes,
hence
enabling
programmability
for
centralized
with
flexibility.
On
other
hand,
its
architecture
makes
very
prone
DDoS
attacks,
necessitating
use
advanced
efficient
IDSs.
This
study
focuses
on
improving
IDS
performance
environments
through
integration
deep
learning
techniques
novel
feature
selection
methods.
presents
an
Enhanced
Maximum
Relevance
Minimum
Redundancy
(EMRMR)
approach
that
incorporates
a
Mutual
Information
Feature
Selection
(MIFS)
strategy
new
Contextual
Coefficient
Upweighting
(CRCU)
optimize
early
attack
detection.
Experiments
inSDN
dataset
showed
EMRMR
achieved
better
precision,
recall,
F1-score,
accuracy
compared
state-of-the-art
approaches,
especially
when
fewer
features
are
selected.
These
results
highlight
efficiency
proposed
relevant
minimal
computational
overhead,
which
enhances
real-time
capability
environments.
PLoS ONE,
Год журнала:
2025,
Номер
20(1), С. e0312425 - e0312425
Опубликована: Янв. 27, 2025
Software-Defined
Networks
(SDN)
provides
more
control
and
network
operation
over
a
infrastructure
as
an
emerging
revolutionary
paradigm
in
networking.
Operating
the
many
applications
preserving
services
functions,
SDN
controller
is
regarded
operating
system
of
SDN-based
architecture.
The
has
several
security
problems
because
its
intricate
design,
even
with
all
amazing
features.
Denial-of-service
(DoS)
attacks
continuously
impact
users
Internet
service
providers
(ISPs).
Because
centralized
distributed
denial
(DDoS)
on
are
frequent
may
have
widespread
effect
network,
particularly
at
layer.
We
propose
to
implement
both
MLP
(Multilayer
Perceptron)
CNN
(Convolutional
Neural
Networks)
based
conventional
methods
detect
Denial
Services
attack.
These
models
got
complex
optimizer
installed
them
decrease
false
positive
or
DDoS
case
detection
efficiency.
use
SHAP
feature
selection
technique
improve
procedure.
By
assisting
identification
which
features
most
essential
spot
incidents,
approach
aids
process
enhancing
precision
flammability.
Fine-tuning
hyperparameters
help
Bayesian
optimization
obtain
best
model
performance
another
important
thing
that
we
do
our
model.
Two
datasets,
InSDN
CICDDoS-2019,
utilized
assess
effectiveness
proposed
method,
99.95%
for
true
(TP)
CICDDoS-2019
dataset
99.98%
dataset,
results
show
highly
accurate.
Journal of Intelligent Systems,
Год журнала:
2024,
Номер
33(1)
Опубликована: Янв. 1, 2024
Abstract
This
study
aims
to
perform
a
thorough
systematic
review
investigating
and
synthesizing
existing
research
on
defense
strategies
methodologies
in
adversarial
attacks
using
machine
learning
(ML)
deep
methods.
A
methodology
was
conducted
guarantee
literature
analysis
of
the
studies
sources
such
as
ScienceDirect,
Scopus,
IEEE
Xplore,
Web
Science.
question
shaped
retrieve
articles
published
from
2019
April
2024,
which
ultimately
produced
total
704
papers.
rigorous
screening,
deduplication,
matching
inclusion
exclusion
criteria
were
followed,
hence
42
included
quantitative
synthesis.
The
considered
papers
categorized
into
coherent
classification
including
three
categories:
security
enhancement
techniques,
attack
mechanisms,
innovative
mechanisms
solutions.
In
this
article,
we
have
presented
comprehensive
earlier
opened
door
potential
future
by
discussing
depth
four
challenges
motivations
attacks,
while
recommendations
been
discussed.
science
mapping
also
performed
reorganize
summarize
results
address
issues
trustworthiness.
Moreover,
covers
large
variety
network
cybersecurity
applications
subjects,
intrusion
detection
systems,
anomaly
detection,
ML-based
defenses,
cryptographic
techniques.
relevant
conclusions
well
demonstrate
what
achieved
against
attacks.
addition,
revealed
few
emerging
tendencies
deficiencies
area
be
remedied
through
better
more
dependable
mitigation
methods
advanced
persistent
threats.
findings
crucial
implications
for
community
researchers,
practitioners,
policy
makers
artificial
intelligence
applications.
Mathematics,
Год журнала:
2024,
Номер
12(9), С. 1294 - 1294
Опубликована: Апрель 25, 2024
The
early
and
accurate
detection
of
Distributed
Denial
Service
(DDoS)
attacks
is
a
fundamental
area
research
to
safeguard
the
integrity
functionality
organizations’
digital
ecosystems.
Despite
growing
importance
neural
networks
in
recent
years,
use
classical
techniques
remains
relevant
due
their
interpretability,
speed,
resource
efficiency,
satisfactory
performance.
This
article
presents
results
comparative
analysis
six
machine
learning
techniques,
namely,
Random
Forest
(RF),
Decision
Tree
(DT),
AdaBoost
(ADA),
Extreme
Gradient
Boosting
(XGB),
Multilayer
Perceptron
(MLP),
Dense
Neural
Network
(DNN),
for
classifying
DDoS
attacks.
CICDDoS2019
dataset
was
used,
which
underwent
data
preprocessing
remove
outliers,
22
features
were
selected
using
Pearson
correlation
coefficient.
RF
classifier
achieved
best
accuracy
rate
(99.97%),
outperforming
other
classifiers
even
previously
published
network-based
techniques.
These
findings
underscore
feasibility
effectiveness
algorithms
field
attack
detection,
reaffirming
relevance
as
valuable
tool
advanced
cyber
defense.
Bitlis Eren Üniversitesi Fen Bilimleri Dergisi,
Год журнала:
2025,
Номер
14(1), С. 597 - 609
Опубликована: Март 26, 2025
In
this
study,
a
model
on
network
security
is
proposed
and
method
suggested
for
data
protection,
integrity,
communication
continuity.
Network
becoming
more
important
every
day
as
the
digital
world
develops.
It
aimed
at
classifying
labeled
good
bad
in
ready
dataset.
model,
first
of
all,
all
information
dataset
digitized.
Then,
it
normalized
to
range
0-1
made
an
input
architecture.
classify
two-class
with
Residual
CNN
The
accuracy
rate
obtained
after
training
testing
stages
94.9%.
This
shows
that
successfully
results
detection
malicious
packets
attacks
can
be
used
security.
