2022 IEEE World Conference on Applied Intelligence and Computing (AIC),
Год журнала:
2023,
Номер
unknown
Опубликована: Июль 29, 2023
Efficient
malware
identification
is
essential
to
safe
the
system
resources
and
privacy
of
data
for
cybersecurity
system.
The
use
android
smartphones
has
increased
tremendously
that
attracting
various
types
attacks.
Nowadays,
writers
Artificial
Intelligence
(AI)-enabled
attack
techniques
bypass
detection
malicious
activities.
Hence,
designing
an
efficient,
effective
robust
identify
variants
remains
a
critical
problem
challenge.
However,
number
deep
learning
(DL)
models
applied
in
existing
methods
at
large
scale,
but
these
actually
lacks
interpretability
explain
contribution
each
features
Therefore,
this
paper
propose
Explainable
(XAI)
based
hybrid
Convolutional
Neural
network
(CNN)
Bi-Gated
Recurrent
Unit
(Bi-GRU)
Android
Malware
Detection
(AMD)
System
using
DL
named
as
XAI-AMD-DL.
proposed
model
evaluated
CICAndMal2019
dataset.
results
obtained
by
XAI-AMD-DL
97.98%
accuracy,
97.75
%,
97.76%,
97.75%
precision,
recall
f1score,
respectively
outperforms
models.
Journal of King Saud University - Computer and Information Sciences,
Год журнала:
2023,
Номер
35(9), С. 101784 - 101784
Опубликована: Сен. 28, 2023
We
present
a
method
to
increase
the
dependability
of
cloud-based
applications.
Traditional
Secret
Sharing
Schemes
(SSSs)
typically
fail
counter
challenges
brought
on
by
Private
Channel
Cracking
(PCC)
and
Illegal
Participant
(IP)
attacks.
To
prevent
these
attacks,
we
suggest
closely-coupled
(t,m,n)
secret
sharing
that
combines
m(m⩾t)
shareholders.
A
polynomial-based
(t,m,n)-ITSS
scheme
is
presented,
which
uses
k-round
Random
Number
Selection
(RNS)
process
strengthen
resistance
PCC
assaults.
common
convert
perfect
(t,n)-SS
into
explained,
greatly
enhances
defense
against
attacks
illegal
participation.
The
presented
strategy
can
enhance
Global Journal of Engineering and Technology Advances,
Год журнала:
2023,
Номер
15(1), С. 070 - 089
Опубликована: Апрель 29, 2023
In
the
current
technological
environment,
different
entities
engage
in
intricate
cyber
security
approaches
order
to
counter
damages
and
disruptions
web-based
systems.
The
design
of
protocols
relies
on
guarantee
that
attacks
are
prevented
Prevention
detection
using
techniques
such
as
access
control
tools,
encryption
firewalls
present
limitations
full
protection
Furthermore,
despite
sophistication
systems,
there
still
shortfalls
high
false
positive
negative
threat
rates,
which
is
attributed
poor
adaptation
by
systems
networks
changing
threats
behavior
cyber-criminals.
this
perspective,
survey
paper
discusses
existing
cyber-attack
models,
recommends
models
appropriate
for
It
evident
deep
learning
offer
better
performance
robustness
compared
traditional
machine
other
non-artificial
intelligence-based
techniques.
Deep
learn
extract
features
automatically
without
human
intervention
can
also
handle
big
multidimensional
data
more
conventionally
than
2022 IEEE World Conference on Applied Intelligence and Computing (AIC),
Год журнала:
2023,
Номер
unknown
Опубликована: Июль 29, 2023
Efficient
malware
identification
is
essential
to
safe
the
system
resources
and
privacy
of
data
for
cybersecurity
system.
The
use
android
smartphones
has
increased
tremendously
that
attracting
various
types
attacks.
Nowadays,
writers
Artificial
Intelligence
(AI)-enabled
attack
techniques
bypass
detection
malicious
activities.
Hence,
designing
an
efficient,
effective
robust
identify
variants
remains
a
critical
problem
challenge.
However,
number
deep
learning
(DL)
models
applied
in
existing
methods
at
large
scale,
but
these
actually
lacks
interpretability
explain
contribution
each
features
Therefore,
this
paper
propose
Explainable
(XAI)
based
hybrid
Convolutional
Neural
network
(CNN)
Bi-Gated
Recurrent
Unit
(Bi-GRU)
Android
Malware
Detection
(AMD)
System
using
DL
named
as
XAI-AMD-DL.
proposed
model
evaluated
CICAndMal2019
dataset.
results
obtained
by
XAI-AMD-DL
97.98%
accuracy,
97.75
%,
97.76%,
97.75%
precision,
recall
f1score,
respectively
outperforms
models.
