The
Industrial
Internet
of
Services
(IIoS)
combines
traditional
industrial
systems
with
digital
technologies,
offering
numerous
advantages
like
improved
efficiency,
productivity,
and
resource
optimization.
However,
the
rapid
growth
IIoS
introduces
significant
cybersecurity
risks.
Cyber
threats
including
DDoS
attacks,
unauthorized
access,
data
breaches,
malware
infections,
pose
a
severe
risk
to
security.
Among
these
threats,
attacks
have
become
concern.
overwhelm
networks
excessive
traffic,
preventing
legitimate
users
from
accessing
network.
Such
can
disrupt
systems,
causing
downtime
inaccessible
services.This
study
aims
analyze
that
target
explore
effectiveness
deep
learning
algorithms
in
detecting
DDoS.
This
research
analyzes
performance
four
algorithms,
ultimately
finding
DNN
GRU
models
achieved
remarkably
high
accuracy
rates
99%.
enhance
ability
identify
potential
Distributed
Denial
Service
(DDoS)
leading
operational
security
optimized
production
processes.
By
employing
findings
this
research,
effectively
detect
hazards,
resulting
enhanced
productivity
streamlined
processes
within
environment.
Mathematics,
Journal Year:
2023,
Volume and Issue:
11(21), P. 4448 - 4448
Published: Oct. 27, 2023
The
Internet
of
Things
(IoT)
refers
to
the
network
interconnected
physical
devices
that
are
embedded
with
software,
sensors,
etc.,
allowing
them
exchange
and
collect
information.
Although
IoT
have
several
advantages
can
improve
people’s
efficacy,
they
also
pose
a
security
risk.
malicious
actor
frequently
attempts
find
new
way
utilize
exploit
specific
resources,
an
device
is
ideal
candidate
for
such
exploitation
owing
massive
number
active
devices.
Especially,
Distributed
Denial
Service
(DDoS)
attacks
include
considerable
like
devices,
which
act
as
bots
transfer
fraudulent
requests
services,
thereby
obstructing
them.
There
needs
be
robust
system
detection
based
on
satisfactory
methods
detecting
identifying
whether
these
occurred
or
not
in
network.
most
widely
used
technique
purposes
artificial
intelligence
(AI),
includes
usage
Deep
Learning
(DL)
Machine
(ML)
cyberattacks.
study
presents
Piecewise
Harris
Hawks
Optimizer
Optimal
Classifier
(PHHO-ODLC)
secure
environment.
fundamental
goal
PHHO-ODLC
algorithm
detect
existence
DDoS
platform.
method
follows
three-stage
process.
At
initial
stage,
PHHO
employed
choose
relevant
features
enhance
classification
performance.
Next,
attention-based
bidirectional
long
short-term
memory
(ABiLSTM)
applied
attack
Finally,
hyperparameter
selection
ABiLSTM
carried
out
by
use
grey
wolf
optimizer
(GWO).
A
widespread
simulation
analysis
was
performed
exhibit
improved
accuracy
technique.
extensive
outcomes
demonstrated
significance
regarding
