The
Distributed
Denial
of
Service
(DDoS)
attacks
is
a
critical
cyber
security
threat
that
blocks
the
services
for
users
and
leads
to
important
damage
reputation
effective
customers.
existing
methods
provides
low
classification
accuracy
due
irrelevant
features
in
performance.
Improved
Whale
Optimization
Algorithm
(IWOA)
proposed
selecting
relevant
which
improved
selected
are
detected
classified
by
Optimized
Long
Short-Term
Memory
(OLSTM)
provided
high
detection
rate
DDoS
attacks.
one-hot
encoding
min-max
normalization
techniques
used
data
pre-processing
stage
improve
performance
classification.
CIC-DDoS
2019
dataset
evaluating
method.
IWOA-OLSTM
method
attained
highest
97.12%,
precision
96.74%,
recall
96.27%.
f1-score
96.54%
DR
93.26%
on
than
Convolutional
Neural
Network
(CNN)
-
Bidirectional
(BiLSTM)
CNN-LSTM.
IEEE Access,
Journal Year:
2024,
Volume and Issue:
12, P. 69765 - 69782
Published: Jan. 1, 2024
By
2025,
the
Internet
of
Things
(IoT)
infrastructure
is
projected
to
encompass
over
75
billion
devices,
facilitated
by
increasing
proliferation
intelligent
applications.
The
ecosystem
consists
sensors
that
function
as
data
generators
and
applications
necessitate
financial
transactions
compensate
producers.
Security
a
highly
important
concern.
Employing
blockchain
technology
makes
it
feasible
enhance
security
maintaining
payments
in
ledger
not
just
secure
but
also
translucent,
distributed,
immutable.
This
article
provides
an
introductory
overview
subsequently
delves
into
many
threats
vulnerabilities
arising
within
IoT
framework.
study
provided
blockchain,
focusing
on
its
categorization
properties.
Moreover,
this
examines
necessity
combining
with
(IoT),
addition
reviewing
relevant
literature
studies
conducted
other
scholars.
offers
insight
uses
(IoT).
Computers,
Journal Year:
2025,
Volume and Issue:
14(3), P. 87 - 87
Published: March 3, 2025
The
rapid
growth
of
digital
communications
and
extensive
data
exchange
have
made
computer
networks
integral
to
organizational
operations.
However,
this
increased
connectivity
has
also
expanded
the
attack
surface,
introducing
significant
security
risks.
This
paper
provides
a
comprehensive
review
Intrusion
Detection
System
(IDS)
technologies
for
network
security,
examining
both
traditional
methods
recent
advancements.
covers
IDS
architectures
types,
key
detection
techniques,
datasets
test
environments,
implementations
in
modern
environments
such
as
cloud
computing,
virtualized
networks,
Internet
Things
(IoT),
industrial
control
systems.
It
addresses
current
challenges,
including
scalability,
performance,
reduction
false
positives
negatives.
Special
attention
is
given
integration
advanced
like
Artificial
Intelligence
(AI)
Machine
Learning
(ML),
potential
distributed
blockchain.
By
maintaining
broad-spectrum
analysis,
aims
offer
holistic
view
state-of-the-art
IDSs,
support
diverse
audience,
identify
future
research
development
directions
critical
area
cybersecurity.
Advances in logistics, operations, and management science book series,
Journal Year:
2023,
Volume and Issue:
unknown, P. 36 - 74
Published: Dec. 29, 2023
This
chapter
explores
the
topic
of
a
novel
network-based
intrusion
detection
system
(NIDPS)
that
utilises
concept
graph
theory
to
detect
and
prevent
incoming
threats.
With
technology
progressing
at
rapid
rate,
number
cyber
threats
will
also
increase
accordingly.
Thus,
demand
for
better
network
security
through
NIDPS
is
needed
protect
data
contained
in
networks.
The
primary
objective
this
explore
based
four
different
aspects:
collection,
analysis
engine,
preventive
action,
reporting.
Besides
analysing
existing
NIDS
technologies
market,
various
research
papers
journals
were
explored.
authors'
solution
covers
basic
structure
an
system,
from
collecting
processing
generating
alerts
reports.
Data
collection
methods
like
packet-based,
flow-based,
log-based
collections
terms
scale
viability.
Sensors,
Journal Year:
2025,
Volume and Issue:
25(5), P. 1346 - 1346
Published: Feb. 22, 2025
The
Internet
of
Things
(IoT)
has
revolutionized
many
domains.
Due
to
the
growing
interconnectivity
IoT
networks,
several
security
challenges
persist
that
need
be
addressed.
This
research
presents
application
deep
learning
techniques
for
Distributed
Denial-of-Service
(DDoS)
attack
detection
in
networks.
study
assesses
performance
various
models,
including
Latent
Autoencoders,
LSTM
and
convolutional
neural
networks
(CNNs),
DDoS
environments.
Furthermore,
a
novel
hybrid
model
is
proposed,
integrating
CNNs
feature
extraction,
Long
Short-Term
Memory
(LSTM)
temporal
pattern
recognition,
Autoencoders
dimensionality
reduction.
Experimental
results
on
CICIOT2023
dataset
show
enhanced
proposed
model,
achieving
training
testing
accuracy
96.78%
integrated
with
96.60%
validation
accuracy.
its
efficiency
addressing
complex
patterns
within
Results’
analysis
shows
outperforms
others.
However,
this
limitations
detecting
rare
types
emphasizes
importance
data
imbalance
further
enhancement
capabilities
future.
The Journal of Engineering,
Journal Year:
2025,
Volume and Issue:
2025(1)
Published: Jan. 1, 2025
ABSTRACT
In
a
distribution
grid,
voltage
is
maintained
within
nominal
range
through
Volt‐VAr
function
that
controls
capacitor
banks,
reactive
power
of
distributed
energy
resources
(DER),
and
on‐load
tap
changers
(OLTC).
Availability
communications
helps
with
the
implementation
central
control;
however,
it
also
opens
system
to
cyberattacks,
causing
disturbances.
Previous
work
has
shown
adverse
impacts
false
data
injection
(FDI)
on
very
few
works
have
studied
methods
detect
mitigate
FDI
control.
This
paper
addresses
gaps
in
detection
mitigation
measurement
packets
uses
two‐stage
algorithm
for
cyberattack
since
accuracy
single‐stage
machine
learning
(ML)–based
method
decreases
while
dealing
unseen
data.
The
first
stage
based
verification
measurements
against
circuit
laws,
second
utilizes
tree
search
an
ML
falsified
compares
long
short‐term
memory
(LSTM)
bidirectional
LSTM
(BiLSTM)
as
employed
algorithms.
Finally,
replaces
estimated
output
algorithm.
effectiveness
proposed
tested
several
cases
using
IEEE
13‐bus
test
PSCAD
software.
