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1
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.
L
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u
r
es
with
o
u
t
im
p
o
s
in
g
s
ig
n
if
ican
t
o
v
e
r
h
ea
d
o
n
th
ese
r
e
s
o
u
r
ce
-
co
n
s
tr
ain
e
d
d
ev
ices is a
m
ajo
r
ch
allen
g
e
[
7
]
.
Ar
tific
ial
in
tellig
en
ce
(
AI
)
ca
n
p
lay
a
s
ig
n
if
ican
t
r
o
le
in
e
n
h
an
cin
g
th
e
s
ec
u
r
ity
o
f
I
o
T
s
y
s
tem
s
in
s
ev
er
al
way
s
.
AI
alg
o
r
ith
m
s
c
an
an
aly
ze
v
ast
a
m
o
u
n
ts
o
f
d
ata
g
en
er
ate
d
b
y
I
o
T
d
ev
ices
to
d
etec
t
a
b
n
o
r
m
al
p
atter
n
s
o
r
b
e
h
av
io
r
th
at
m
ay
in
d
icate
a
s
ec
u
r
ity
b
r
ea
ch
.
B
y
lear
n
in
g
wh
at
co
n
s
titu
tes
n
o
r
m
al
b
eh
a
v
io
r
f
o
r
d
ev
ices
an
d
n
etwo
r
k
s
,
AI
ca
n
id
en
tif
y
d
ev
iatio
n
s
an
d
p
o
ten
tial
s
ec
u
r
ity
th
r
ea
ts
in
r
ea
l
-
tim
e,
en
ab
lin
g
p
r
o
ac
tiv
e
th
r
ea
t
m
itig
atio
n
[
8
]
.
AI
-
p
o
wer
ed
b
eh
a
v
io
r
al
an
aly
s
is
ca
n
id
en
tify
s
u
s
p
icio
u
s
ac
tiv
ities
o
r
u
n
au
th
o
r
ized
ac
ce
s
s
attem
p
ts
with
in
I
o
T
n
etwo
r
k
s
.
B
y
c
o
n
t
in
u
o
u
s
ly
m
o
n
ito
r
in
g
d
e
v
ice
in
t
er
ac
tio
n
s
an
d
u
s
er
b
eh
av
io
r
s
,
AI
alg
o
r
ith
m
s
ca
n
d
etec
t
an
o
m
alies
in
d
icativ
e
o
f
m
alicio
u
s
ac
tiv
ities
,
s
u
ch
as
u
n
au
th
o
r
ized
d
ev
ice
ac
ce
s
s
o
r
u
n
u
s
u
al
d
ata
tr
an
s
f
e
r
s
[
9
]
.
AI
-
en
ab
led
p
r
e
d
ictiv
e
m
ain
ten
an
ce
tech
n
iq
u
es
ca
n
h
elp
id
en
tify
s
ec
u
r
ity
v
u
ln
er
ab
ilit
ies
a
n
d
wea
k
n
ess
e
s
in
I
o
T
d
ev
ices
b
ef
o
r
e
th
ey
ar
e
ex
p
lo
ited
b
y
attac
k
er
s
.
B
y
an
aly
zin
g
d
ev
ice
p
er
f
o
r
m
an
ce
d
ata
a
n
d
d
etec
tin
g
p
atter
n
s
in
d
icativ
e
o
f
im
p
e
n
d
in
g
f
ailu
r
es
o
r
s
ec
u
r
it
y
b
r
ea
c
h
es,
AI
ca
n
e
n
ab
le
p
r
o
ac
tiv
e
r
em
e
d
iatio
n
ac
tio
n
s
to
m
itig
ate
r
is
k
s
an
d
en
h
a
n
c
e
o
v
er
all
s
y
s
tem
s
ec
u
r
ity
[
1
0
]
.
Ap
ar
t
f
r
o
m
t
h
is
,
it
ca
n
b
e
also
u
s
ed
f
o
r
cy
b
e
r
th
r
ea
t
in
tellig
en
ce
[
1
1
]
,
a
d
ap
tiv
e
au
th
en
ticatio
n
[
1
2
]
,
n
etwo
r
k
t
r
af
f
ic
an
aly
s
is
[
1
3
]
,
s
ec
u
r
ity
au
to
m
atio
n
[
1
4
]
.
Ho
wev
er
,
th
e
r
e
ar
e
v
a
r
io
u
s
c
h
allen
g
es
ass
o
ciate
d
with
a
d
o
p
tin
g
A
I
-
b
ased
m
o
d
el
f
o
r
I
o
T
s
ec
u
r
ity
v
iz
:
i
)
s
ca
lab
le
s
ec
u
r
ity
p
e
r
f
o
r
m
an
ce
is
q
u
ite
d
if
f
icu
lt
in
lar
g
e
an
d
co
m
p
le
x
I
o
T
en
v
ir
o
n
m
en
t,
ii)
o
f
f
er
in
g
u
n
if
o
r
m
an
d
co
n
s
is
ten
t
s
ec
u
r
ity
p
er
f
o
r
m
an
c
e
in
p
r
esen
ce
o
f
h
eter
o
g
en
eo
u
s
I
o
T
d
ev
ices,
iii)
o
f
f
e
r
in
g
ass
u
r
an
ce
t
o
war
d
s
o
p
tim
al
s
ec
u
r
ity
i
n
p
r
esen
ce
o
f
d
y
n
am
ic
en
v
ir
o
n
m
en
t
is
th
e
t
h
e
m
o
s
t
ch
allen
g
in
g
is
s
u
e
f
o
r
e
x
is
tin
g
AI
-
m
o
d
els,
i
v
)
m
ajo
r
ity
o
f
th
e
AI
m
o
d
els
a
r
e
h
ig
h
ly
iter
ativ
e
in
o
p
e
r
atio
n
an
d
d
em
an
d
s
e
x
ten
s
iv
e
r
eso
u
r
ce
s
to
ac
tu
ally
im
p
lem
en
t
th
em
o
n
r
ea
l
-
wo
r
ld
ap
p
lic
atio
n
s
,
v
)
ex
is
tin
g
AI
m
o
d
els
ar
e
k
n
o
w
n
to
o
f
f
er
h
ig
h
er
p
r
ed
ictiv
e
ac
cu
r
ac
y
b
u
t le
s
s
to
war
d
s
co
m
p
u
tatio
n
al
e
f
f
icien
cy
.
T
h
e
r
elate
d
wo
r
k
ca
r
r
ied
o
u
t
i
n
th
is
p
er
s
p
ec
tiv
e
o
f
I
o
T
s
ec
u
r
ity
ar
e
as
f
o
llo
ws:
e
x
is
tin
g
s
y
s
tem
h
as
witn
ess
ed
p
r
o
life
r
ated
u
s
ag
e
o
f
m
ac
h
in
e
lear
n
in
g
(
ML
)
a
p
p
r
o
ac
h
to
war
d
s
I
o
T
s
ec
u
r
ity
.
T
h
e
ad
o
p
tio
n
o
f
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
in
e
h
as
b
ee
n
s
ee
n
in
wo
r
k
o
f
I
o
an
n
o
u
an
d
Vass
ilio
u
[
1
5
]
wh
er
e
t
h
e
p
r
im
e
n
o
tio
n
is
to
war
d
s
class
if
icatio
n
o
f
n
etwo
r
k
-
r
elate
d
attac
k
s
.
Kau
s
h
ik
e
t
a
l.
[
1
6
]
h
av
e
u
s
ed
Naïv
e
B
a
y
esian
ap
p
r
o
a
ch
to
p
er
f
o
r
m
class
if
icatio
n
.
Ad
o
p
t
io
n
o
f
d
ec
is
io
n
tr
ee
is
witn
ess
ed
in
wo
r
k
o
f
Alab
d
u
lk
a
r
im
et
a
l.
[
1
7
]
to
in
co
r
p
o
r
ate
p
r
iv
ac
y
p
r
eser
v
at
io
n
in
h
ea
lth
ca
r
e
s
ec
to
r
.
Fu
r
t
h
er
,
r
an
d
o
m
f
o
r
est
h
as
b
ee
n
p
r
o
v
e
n
to
o
f
f
e
r
an
ef
f
ec
tiv
e
d
etec
tio
n
o
f
m
alwa
r
es
p
r
esen
t
in
I
o
T
n
etwo
r
k
s
as
s
ee
n
in
wo
r
k
o
f
Atitallah
et
a
l
.
[
1
8
]
.
Fro
m
th
e
p
er
s
p
ec
tiv
e
o
f
d
ee
p
lear
n
in
g
(
DL
)
m
eth
o
d
s
,
v
ar
i
o
u
s
s
ec
u
r
i
ty
s
ch
em
es
h
av
e
b
ee
n
ev
o
lv
e
d
.
Velin
ch
k
o
et
a
l.
[
1
9
]
h
a
v
e
ad
v
o
ca
ted
th
e
u
s
ag
e
o
f
ar
tific
ial
n
eu
r
al
n
etwo
r
k
to
war
d
s
v
ar
ied
ap
p
licatio
n
in
I
o
T
.
C
o
n
v
o
lu
tio
n
n
eu
r
a
l
n
etwo
r
k
h
as
b
ee
n
im
p
lem
en
ted
b
y
Alab
s
i
et
a
l.
[
2
0
]
to
war
d
s
d
etec
tio
n
th
e
attac
k
with
its
ex
tr
ac
ted
f
ea
tu
r
es
in
I
o
T
n
etwo
r
k
.
Say
eg
h
et
al.
[
2
1
]
h
av
e
u
s
ed
l
o
n
g
s
h
o
r
t
-
ter
m
m
em
o
r
y
to
w
ar
d
s
im
p
r
o
v
in
g
th
e
in
tr
u
s
io
n
d
etec
tio
n
in
I
o
T
n
et
wo
r
k
s
ass
ess
ed
with
p
u
b
licly
av
ailab
le
d
ataset.
C
h
u
an
d
L
in
[
2
2
]
h
av
e
u
s
ed
g
en
er
ativ
e
ad
v
er
s
ar
ial
n
etwo
r
k
(
GAN)
in
o
r
d
er
to
en
h
an
c
e
th
e
class
if
icatio
n
o
f
an
I
o
T
ad
v
er
s
ar
ies
wh
ile
u
s
ag
e
o
f
r
ein
f
o
r
ce
m
e
n
t
lear
n
i
n
g
(
R
L
)
h
as
b
ee
n
u
s
ed
f
o
r
en
h
a
n
cin
g
s
ec
u
r
ity
as
n
o
ted
in
wo
r
k
o
f
Hu
et
a
l.
[
2
3
]
.
Fu
r
th
er
,
h
y
b
r
i
d
lear
n
i
n
g
a
p
p
r
o
ac
h
es
h
a
v
e
also
b
ee
n
u
s
e
d
in
o
r
d
er
to
in
te
g
r
ate
b
o
th
m
ac
h
in
e
a
n
d
DL
ap
p
r
o
ac
h
es
f
o
r
im
p
r
o
v
in
g
n
et
wo
r
k
s
ec
u
r
ity
in
I
o
T
as
n
o
ted
in
wo
r
k
o
f
Sag
u
et
a
l.
[
2
4
]
,
Vu
et
a
l
.
[
2
5
]
,
an
d
Yar
as
an
d
Den
e
r
[
2
6
]
.
Ap
ar
t
f
r
o
m
AI
-
b
ased
m
o
d
el,
th
e
r
e
ar
e
v
ar
io
u
s
c
r
y
p
to
g
r
ap
h
y
-
b
a
s
ed
s
tu
d
y
m
o
d
els
f
o
cu
s
in
g
o
n
I
o
T
s
ec
u
r
ity
.
A
d
o
p
tio
n
o
f
ad
v
a
n
ce
d
e
n
cr
y
p
ti
o
n
s
tan
d
ar
d
(
AE
S)
is
witn
ess
ed
in
wo
r
k
o
f
R
ek
er
a
h
o
et
a
l
.
[
2
7
]
an
d
Ham
ee
d
i
an
d
B
ay
at
[
2
8
]
to
wa
r
d
s
s
ec
u
r
in
g
en
er
g
y
m
o
n
ito
r
in
g
s
y
s
tem
.
A
d
o
p
tio
n
o
f
ellip
tical
cu
r
v
e
cr
y
p
to
g
r
ap
h
y
(
E
C
C
)
is
s
ee
n
in
wo
r
k
o
f
Ma
tteo
et
a
l.
[
2
9
]
wh
er
e
a
p
r
o
ce
s
s
o
r
h
as
b
ee
n
d
esig
n
ed
in
o
r
d
er
to
s
ec
u
r
e
co
m
m
u
n
icatio
n
in
I
o
T
ap
p
licatio
n
s
.
Dig
ital
s
ig
n
atu
r
e
(
DS)
h
as
also
b
ee
n
ex
te
n
s
iv
e
u
s
ed
to
war
d
s
au
th
en
ticatio
n
in
I
o
T
as
s
ee
n
in
wo
r
k
o
f
B
u
r
g
o
s
an
d
Pu
s
tis
ek
[
3
0
]
.
E
x
is
tin
g
s
tu
d
y
h
as
also
n
o
ted
u
s
ag
e
o
f
h
o
m
o
m
o
r
p
h
ic
en
cr
y
p
tio
n
(
H
E
)
as
p
r
esen
ted
in
wo
r
k
o
f
Alb
ak
r
i
et
a
l.
[
3
1
]
f
o
r
f
ac
ilit
atin
g
h
ig
h
er
s
ec
u
r
e
-
en
ab
led
co
m
m
u
n
icati
o
n
in
I
o
T
g
r
o
u
p
s
.
T
h
e
co
n
tr
ib
u
tio
n
o
f
th
e
p
r
o
p
o
s
ed
s
tu
d
y
is
a
n
o
v
el
co
m
p
u
t
atio
n
ally
in
tellig
en
t
tr
u
s
t
co
m
p
u
tatio
n
al
m
ec
h
an
is
m
h
a
r
n
ess
in
g
p
r
o
b
a
b
ilit
y
m
o
d
ellin
g
a
n
d
AI
m
eth
o
d
o
lo
g
y
f
o
r
d
iag
n
o
s
in
g
leth
al
th
r
ea
ts
in
I
o
T
.
T
h
e
v
alu
e
ad
d
e
d
co
n
tr
ib
u
tio
n
o
f
t
h
e
s
tu
d
y
ar
e
as
f
o
llo
ws:
i)
th
e
s
tu
d
y
p
r
esen
ts
a
n
o
v
el
s
m
a
r
t
city
r
eg
io
n
-
b
ased
s
tu
d
y
m
o
d
el
f
o
r
m
o
n
ito
r
in
g
t
h
e
m
alicio
u
s
b
e
h
av
io
r
o
f
u
n
k
n
o
wn
th
r
ea
ts
,
ii)
th
e
s
tu
d
y
p
r
esen
ts
a
u
n
iq
u
e
r
o
le
m
o
d
ellin
g
o
f
I
o
T
d
ev
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f
o
r
f
ac
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atin
g
f
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r
m
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f
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ad
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ar
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v
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f
o
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m
th
at
is
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k
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wn
to
s
y
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tem
,
iii)
a
n
o
v
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an
d
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a
b
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ased
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ap
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p
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o
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an
d
v
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RL
is
ap
p
lied
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o
r
d
er
to
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
I
n
d
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n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
38
,
No
.
2
,
May
20
25
:
9
8
8
-
9
9
6
990
lev
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etwo
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2.
M
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T
[
3
2
]
,
[
3
3
]
.
T
h
e
p
r
im
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s
tr
en
g
th
o
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th
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a
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d
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f
cr
y
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to
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a
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m
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el;
h
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e
is
s
till
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lar
g
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s
co
p
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o
f
o
p
tim
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co
m
p
u
tatio
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al
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icien
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as
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s
ec
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ity
f
ea
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ass
o
ciate
d
with
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is
m
o
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el.
T
h
er
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th
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p
r
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p
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ased
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tial m
o
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i
b
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in
Fig
u
r
e
1
ar
e
as f
o
llo
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s
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a.
Fo
r
m
atio
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o
f
s
m
ar
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city
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eg
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(
SC
R
)
:
t
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p
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p
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is
f
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r
th
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aller
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s
.
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tr
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v
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ty
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will
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Fro
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Fig
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1
,
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el
[
3
2
]
,
[
3
3
]
.
b.
R
o
les o
f
I
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T
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o
d
es
:
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p
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tem
co
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s
id
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s
th
at
th
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e
ar
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r
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ty
p
es o
f
I
o
T
d
ev
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s
v
iz.
i)
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m
al
d
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ii)
d
ev
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with
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n
d
i
ii)
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ar
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.
T
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e
n
o
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d
e
v
ices
ar
e
m
ea
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t
f
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en
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ata
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ar
d
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to
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wh
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ated
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e.
T
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o
r
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p
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s
th
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im
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I
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T
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.
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,
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m
alicio
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Dif
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m
all
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el,
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e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
N
o
ve
l
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n
tellig
en
t tru
s
t c
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T
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m
o
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d
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R
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Fo
llo
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th
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ed
:
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u
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tatio
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:
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tan
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th
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m
ec
h
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is
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o
f
tr
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s
t
co
m
p
u
tatio
n
,
it
is
es
s
en
tial
to
u
n
d
er
s
tan
d
th
e
o
p
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n
s
p
er
f
o
r
m
ed
b
y
ea
ch
I
o
T
d
ev
ices.
T
h
e
p
r
o
p
o
s
ed
s
ch
em
e
h
y
p
o
th
esizes
tw
o
s
im
ilar
o
p
er
atio
n
s
b
ein
g
ca
r
r
i
ed
o
u
t
b
y
b
o
th
n
o
r
m
al
an
d
ad
v
er
s
ar
y
d
ev
ices
v
i
z.
f
o
r
war
d
in
g
d
ata
an
d
d
r
o
p
p
in
g
t
h
e
d
ata
p
ac
k
et.
T
h
e
s
ch
em
e
ass
ig
n
s
a
u
n
iq
u
e
th
i
r
d
o
p
er
atio
n
t
o
d
is
tin
g
u
is
h
n
o
r
m
al
f
r
o
m
ad
v
er
s
ar
y
d
e
v
ice
wh
er
e
th
e
n
o
r
m
al
n
o
d
e
will
alwa
y
s
p
r
o
p
ag
ate
th
e
id
en
tifie
d
ad
v
e
r
s
ar
y
d
ev
ice
to
g
atew
ay
n
o
d
e
v
ia
ac
ce
s
s
p
o
i
n
t
wh
ile
th
e
ad
v
e
r
s
ar
y
d
ev
ic
e
will
lau
n
ch
a
m
alicio
u
s
p
r
o
g
r
am
an
d
ev
ad
e
f
r
o
m
b
ein
g
d
etec
ted
b
y
m
ig
r
atin
g
its
elf
f
r
o
m
cu
r
r
e
n
t
to
d
if
f
er
en
t
SC
R
with
a
n
ew
s
p
o
o
f
ed
id
en
tity
.
T
h
e
p
r
o
b
a
b
ilit
y
m
o
d
el
is
th
en
ap
p
lied
i)
to
e
v
alu
ate
th
at
th
e
id
en
tifie
d
d
e
v
ice
is
an
ad
v
er
s
ar
y
an
d
ii)
to
ev
alu
ate
th
at
th
e
a
d
v
er
s
ar
y
d
e
v
ice
will
e
ith
er
lau
n
c
h
m
alicio
u
s
p
r
o
g
r
a
m
o
r
it
will
ch
o
o
s
e
to
f
o
r
war
d
th
e
d
ata
o
f
n
o
r
m
al
d
e
v
ice.
Fo
r
th
is
p
u
r
p
o
s
e,
th
e
s
ch
em
e
co
n
s
id
er
s
two
p
ar
am
eter
s
v
iz.
p
ar
am
eter
f
o
r
n
u
m
b
er
o
f
id
en
tifie
d
d
ata
f
o
r
war
d
in
g
D
f
an
d
p
a
r
am
eter
f
o
r
id
e
n
tifie
d
d
ata
d
r
o
p
p
in
g
D
d
.
Hen
ce
,
p
o
s
itiv
e
tr
u
s
t
ca
n
b
e
ca
lcu
lated
b
y
co
m
p
u
tin
g
p
r
o
b
a
b
ilit
y
as
D
f
/
(
D
f
+
D
d
)
wh
ile
n
eg
ativ
e
tr
u
s
t
ca
n
b
e
c
alcu
lated
b
y
co
m
p
u
tin
g
p
r
o
b
a
b
ilit
y
as
D
d
/
(
D
f
+
D
d
)
.
Ho
wev
er
,
th
is
co
m
p
u
tatio
n
is
o
n
ly
v
alid
if
D
f
≠
D
d
,
wh
ich
is
n
o
t
alwa
y
s
th
e
ca
s
e.
Hen
ce
,
th
e
s
ch
e
m
e
co
m
p
u
tes
u
n
ce
r
tain
tr
u
s
t
as
A
/
(
A
1
+
A
2
)
,
wh
er
e
attr
ib
u
te
A
r
ep
r
esen
ts
d
o
t
p
r
o
d
u
ct
o
f
n
etwo
r
k
co
ef
f
icien
t,
D
f
,
an
d
D
d
,
attr
ib
u
te
A
1
r
ep
r
esen
ts
s
q
u
ar
ed
s
u
m
m
atio
n
o
f
D
f
a
n
d
D
d
a
n
d
a
ttrib
u
te
A
2
r
ep
r
esen
ts
s
u
m
m
atio
n
o
f
D
f
a
n
d
D
d
.
T
h
e
co
n
tr
ib
u
tio
n
o
f
t
h
is
tr
u
s
t
co
m
p
u
tatio
n
is
th
at
if
th
e
v
alu
e
o
f
th
is
u
n
ce
r
tain
tr
u
s
t
r
e
d
u
ce
s
,
it
g
iv
es
a
h
ig
h
er
c
o
n
f
ir
m
atio
n
th
at
th
e
tar
g
eted
d
ev
ice
is
ad
v
er
s
ar
y
.
I
t
s
h
o
u
ld
b
e
n
o
ted
t
h
at
th
e
s
y
s
tem
h
as
n
o
p
r
ed
ef
in
e
d
in
f
o
r
m
atio
n
o
f
i
d
en
ti
ty
o
f
tar
g
et
n
o
d
e
to
b
e
n
o
r
m
al
o
r
ad
v
er
s
ar
y
.
-
Allo
ca
tio
n
s
o
f
r
ewa
r
d
/p
en
alt
y
:
I
t
is
to
b
e
n
o
ted
th
at
p
r
o
p
o
s
ed
p
r
o
b
ab
ilit
y
m
o
d
el
is
d
esig
n
ed
co
n
s
id
er
in
g
th
r
ee
f
u
n
cti
o
n
s
ea
ch
f
o
r
n
o
r
m
al
an
d
ad
v
e
r
s
ar
y
n
o
d
e.
T
h
e
f
u
n
ctio
n
s
ex
h
ib
ited
b
y
n
o
r
m
al
n
o
d
e
ar
e
d
ata
f
o
r
war
d
i
n
g
D
f
,
d
ata
d
r
o
p
p
i
n
g
D
d
,
an
d
u
p
d
ati
n
g
attac
k
in
f
o
r
m
atio
n
U
a
.
T
h
e
f
u
n
ctio
n
s
ex
h
ib
ited
b
y
ad
v
er
s
ar
y
n
o
d
e
ar
e
d
ata
f
o
r
war
d
in
g
D
f
,
d
ata
d
r
o
p
p
in
g
D
d
,
an
d
r
eg
io
n
m
ig
r
atio
n
R
m
.
Hen
ce
,
it c
an
b
e
e
m
p
ir
ically
e
x
p
r
ess
ed
as:
(
)
=
(
,
,
)
(
a
dve
r
s
a
r
y
)
=
(
f
,
d
,
m
)
(
1
)
Fro
m
(
1
)
,
th
e
f
ir
s
t
two
f
u
n
cti
o
n
al
attr
ib
u
tes
i.e
.
,
D
f
a
n
d
D
d
ar
e
s
am
e
wh
ile
th
e
th
ir
d
o
n
e
i
.
e.
,
U
a
a
n
d
R
m
is
u
s
ef
u
l
f
o
r
d
etec
tio
n
o
f
t
h
e
n
o
r
m
al
an
d
ad
v
e
r
s
ar
y
n
o
d
e
.
Fu
r
th
er
,
th
e
p
r
o
p
o
s
ed
s
ch
em
e
u
s
es
two
v
ar
iab
les
ca
lled
as
p
r
o
f
it
an
d
r
eso
u
r
ce
s
u
s
ed
ass
o
ciate
d
with
all
th
ese
f
u
n
ctio
n
al
attr
ib
u
tes.
T
h
e
s
ch
em
e
co
n
s
id
er
s
p
r
o
f
it
f
o
r
e
x
ec
u
tin
g
R
m
as
I
1
,
p
r
o
f
it
f
o
r
e
x
ec
u
tin
g
D
f
as
I
2
,
p
r
o
f
it
f
o
r
e
x
ec
u
tin
g
U
a
as
I
3
.
Similar
ly
,
r
eso
u
r
ce
s
u
s
ed
f
o
r
D
d
as
I
4
,
r
eso
u
r
ce
s
f
o
r
D
f
as
I
5
,
r
eso
u
r
ce
s
f
o
r
U
a
as
I
6
,
an
d
r
eso
u
r
ce
s
f
o
r
R
m
as
I
7
.
Fu
r
th
e
r
,
th
e
s
ch
em
e
co
n
s
id
er
s
an
o
u
tlier
d
etec
tio
n
as
I
8
b
y
th
e
n
o
r
m
al
n
o
d
e.
All
th
ese
v
ar
iab
les
ar
e
in
itia
lized
wh
ile
p
er
f
o
r
m
in
g
s
im
u
latio
n
.
T
h
e
ass
ig
n
m
en
t
o
f
r
e
war
d
an
d
p
en
alty
is
s
h
o
wn
in
T
a
b
le
1
.
T
ab
le
1
.
E
m
p
ir
ical
ass
ig
n
m
en
t
o
f
r
ewa
r
d
/p
en
alty
Ta
r
g
e
t
n
o
d
e
i
s a
d
v
e
r
sar
y
Ta
r
g
e
t
n
o
d
e
i
s
n
o
r
m
a
l
C
o
m
b
i
n
a
t
i
o
n
R
e
w
a
r
d
/
p
e
n
a
l
t
y
C
o
m
b
i
n
a
t
i
o
n
R
e
w
a
r
d
/
p
e
n
a
l
t
y
(
A
,
C
)
(I
1
-
I
4
,
-
I
1
-
I
5
)
(
C
,
C
)
(I
2
-
I
5
,
I
2
-
I
5
)
(
A
,
D
)
(
-
I
4
,
0
)
(
C
,
D
)
(
-
I
5
,
0
)
(
A
,
R
)
(I
3
-
I
4
,
I
3
-
I
6
)
(
C
,
R
)
(
-
I
5
,
-
I
8
-
I
6
)
(
C
,
C
)
(
-
I
5
, I
2
-
I
5
)
(
D
,
C
)
(
0
,
-
I
5
)
(
C
,
D
)
(
-
I
5
,
0
)
(
D
,
D
)
(
0
,
0
)
(
C
,
R
)
(
-
I
3
-
I
5
, I
3
-
I
6
)
(
D
,
R
)
(
0
,
-
I
8
-
I
6
)
(
F
,
C
)
(
-
I
7
,
-
I
5
)
(
R
,
C
)
(
-
I
8
-
I
6
,
-
I
5
)
(
F
,
D
)
(
-
I
7
,
0
)
(
R
,
D
)
(
-
I
8
-
I
6
,
0
)
(
F
,
R
)
(
-
I
7
,
-
I
6
)
(
R
,
R
)
(
-
I
8
-
I
6
,
-
I
8
-
I
6
)
d.
AI
-
m
o
d
el
to
war
d
s
m
o
n
ito
r
in
g
o
p
tim
izatio
n
:
f
r
o
m
th
e
d
is
cu
s
s
io
n
o
f
allo
ca
tio
n
o
f
r
ewa
r
d
a
n
d
p
en
alty
,
it
is
n
o
ted
th
at
s
p
ec
if
ic
v
alu
es
ar
e
ass
ig
n
ed
o
n
th
e
b
asis
o
f
u
n
d
er
tak
en
ac
tio
n
s
b
y
th
e
n
o
r
m
a
l
o
r
ad
v
er
s
ar
y
d
ev
ices.
T
h
e
co
n
d
itio
n
f
o
r
m
f
o
r
th
e
id
en
tific
atio
n
is
two
i
.
e.
,
i)
in
cr
ea
s
ed
o
b
s
er
v
atio
n
o
f
c
o
n
s
is
ten
tly
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
38
,
No
.
2
,
May
20
25
:
9
8
8
-
9
9
6
992
r
ed
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cin
g
u
n
ce
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tain
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d
ii)
p
r
o
f
it
o
f
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tr
o
d
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ci
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m
alicio
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s
p
r
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g
r
am
is
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ig
n
if
ican
tly
less
co
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ar
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r
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to
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e
a
s
s
ig
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ed
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o
r
in
tr
o
d
u
cin
g
th
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m
alico
u
s
p
r
o
g
r
a
m
.
All
th
e
o
b
s
er
v
ed
v
alu
es
o
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th
e
m
o
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ito
r
e
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tr
u
s
t
ar
e
th
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n
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u
b
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d
to
AI
m
o
d
el
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s
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R
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f
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m
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g
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y
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am
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ak
in
g
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L
ca
n
b
e
ap
p
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to
attac
k
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etec
tio
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I
o
T
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y
tr
ain
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g
ag
en
ts
to
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ak
e
d
ec
is
io
n
s
in
d
y
n
am
ic
en
v
ir
o
n
m
en
ts
,
ad
ap
tin
g
to
e
v
o
lv
in
g
th
r
ea
ts
an
d
an
o
m
alies.
Her
e'
s
h
o
w
R
L
ca
n
b
e
u
s
ed
f
o
r
t
h
is
p
u
r
p
o
s
e:
-
E
n
v
ir
o
n
m
en
t
m
o
d
elin
g
:
R
L
ag
en
ts
ca
n
b
e
tr
ai
n
ed
t
o
m
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d
el
th
e
en
v
ir
o
n
m
e
n
t
o
f
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o
T
d
ev
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in
clu
d
in
g
n
o
r
m
al
b
e
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av
io
r
p
a
tter
n
s
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n
etwo
r
k
tr
af
f
ic,
a
n
d
i
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ter
ac
tio
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s
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etwe
en
d
ev
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T
h
e
ag
en
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lear
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to
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g
n
ize
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s
f
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m
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av
io
r
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wh
ic
h
m
ay
in
d
icate
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k
s
o
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o
m
alies.
-
State
r
ep
r
esen
tatio
n
:
I
o
T
en
v
i
r
o
n
m
en
ts
o
f
ten
g
e
n
er
ate
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ig
h
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d
im
en
s
io
n
al
an
d
co
m
p
lex
d
at
a
s
tr
ea
m
s
.
R
L
m
o
d
els
ca
n
lear
n
c
o
m
p
a
ct
r
ep
r
esen
tatio
n
s
o
f
t
h
ese
s
tates,
ca
p
tu
r
in
g
r
ele
v
an
t
in
f
o
r
m
atio
n
f
o
r
attac
k
d
etec
tio
n
wh
ile
r
e
d
u
cin
g
th
e
d
im
e
n
s
io
n
ality
o
f
t
h
e
p
r
o
b
lem
.
-
Actio
n
s
elec
tio
n
:
R
L
ag
en
ts
c
h
o
o
s
e
ac
tio
n
s
b
ased
o
n
th
eir
l
ea
r
n
ed
p
o
licies
an
d
th
e
o
b
s
er
v
ed
s
tates
o
f
th
e
en
v
ir
o
n
m
en
t.
I
n
th
e
c
o
n
te
x
t
o
f
I
o
T
s
ec
u
r
ity
,
ac
tio
n
s
m
ay
in
clu
d
e
is
o
latin
g
s
u
s
p
icio
u
s
d
ev
ices,
b
lo
ck
in
g
m
alicio
u
s
tr
af
f
ic,
o
r
aler
tin
g
ad
m
in
is
tr
ato
r
s
.
-
R
ewa
r
d
d
esig
n
:
d
esig
n
in
g
a
p
p
r
o
p
r
iate
r
ewa
r
d
f
u
n
ctio
n
s
is
cr
u
cial
in
R
L
f
o
r
attac
k
d
etec
tio
n
.
R
ewa
r
d
s
ca
n
b
e
b
ased
o
n
th
e
ef
f
ec
tiv
en
ess
o
f
ac
tio
n
s
ta
k
en
b
y
th
e
ag
en
t
in
m
itig
atin
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attac
k
s
,
m
in
im
izin
g
f
alse p
o
s
itiv
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o
r
m
ax
i
m
izin
g
th
e
d
etec
tio
n
o
f
tr
u
e
th
r
ea
ts
.
-
Ad
v
er
s
ar
ial
tr
ain
in
g
:
R
L
ag
e
n
ts
ca
n
b
e
tr
ain
ed
in
ad
v
e
r
s
ar
ial
s
ettin
g
s
,
wh
er
e
t
h
ey
lear
n
to
an
ticip
ate
an
d
d
ef
e
n
d
ag
ain
s
t
attac
k
s
b
y
in
ter
ac
tin
g
with
s
im
u
lated
attac
k
er
s
.
T
h
is
h
elp
s
th
e
ag
en
t
to
b
ec
o
m
e
m
o
r
e
r
o
b
u
s
t a
n
d
ad
ap
tiv
e
t
o
n
o
v
el
attac
k
s
tr
ateg
ies.
-
C
o
n
tin
u
o
u
s
lear
n
in
g
:
I
o
T
en
v
ir
o
n
m
e
n
ts
ar
e
d
y
n
am
ic,
with
n
ew
d
ev
ices
jo
in
i
n
g
a
n
d
leav
in
g
t
h
e
n
etwo
r
k
,
an
d
n
ew
attac
k
tec
h
n
iq
u
es
em
er
g
in
g
o
v
er
tim
e.
R
L
m
o
d
els
ca
n
co
n
tin
u
o
u
s
ly
lear
n
an
d
ad
ap
t to
th
ese
ch
a
n
g
es,
im
p
r
o
v
in
g
t
h
eir
e
f
f
ec
tiv
en
ess
in
d
ete
ctin
g
ev
o
lv
i
n
g
th
r
ea
ts
.
-
Po
licy
im
p
r
o
v
e
m
en
t:
th
r
o
u
g
h
r
ep
ea
ted
in
ter
ac
tio
n
s
with
th
e
en
v
ir
o
n
m
en
t,
R
L
ag
en
ts
ca
n
im
p
r
o
v
e
th
eir
p
o
licies o
v
er
tim
e,
lear
n
i
n
g
f
r
o
m
p
ast ex
p
er
ien
ce
s
an
d
ad
ju
s
tin
g
th
eir
b
eh
av
i
o
r
to
ac
h
iev
e
b
etter
attac
k
d
etec
tio
n
p
e
r
f
o
r
m
an
ce
.
-
Hier
ar
ch
ical
RL
:
h
ie
r
ar
ch
ical
R
L
ca
n
b
e
u
s
ed
to
m
o
d
el
co
m
p
lex
I
o
T
s
ec
u
r
ity
s
y
s
tem
s
wit
h
m
u
ltip
le
lev
els
o
f
ab
s
tr
ac
tio
n
.
T
h
is
allo
ws
f
o
r
ef
f
icien
t
lear
n
in
g
an
d
d
ec
is
io
n
-
m
ak
in
g
at
d
if
f
er
en
t
la
y
er
s
o
f
th
e
I
o
T
in
f
r
astru
ctu
r
e,
f
r
o
m
e
d
g
e
d
ev
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n
etwo
r
k
g
atew
ay
s
a
n
d
clo
u
d
s
er
v
er
s
.
T
h
e
f
in
al
o
u
tco
m
e
o
f
th
e
p
r
o
p
o
s
ed
AI
m
o
d
el
is
a
co
n
f
ir
m
ed
d
etec
tio
n
o
f
a
d
v
er
s
ar
y
m
o
d
el
,
ir
r
esp
ec
tiv
e
o
f
an
y
ty
p
e
o
f
att
ac
k
er
in
I
o
T
.
T
h
e
s
ch
em
e
ca
n
ea
s
ily
p
er
f
o
r
m
d
etec
tio
n
o
f
e
v
en
a
m
in
o
r
d
eg
r
ee
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f
an
o
m
alies
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h
i
b
ted
b
y
a
n
ad
v
er
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ar
y
n
o
d
e
f
o
llo
wed
b
y
u
s
in
g
its
ag
e
n
t
m
o
d
el
th
at
f
in
ally
d
etec
ts
th
e
ad
v
er
s
ar
y
n
o
d
e.
Hen
ce
,
th
e
AI
m
o
d
el
i
n
tr
o
d
u
ce
d
in
t
h
is
s
tu
d
y
ac
tu
ally
co
m
p
lem
e
n
ts
th
e
p
r
o
b
a
b
ilit
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m
o
d
el
to
war
d
s
co
n
f
ir
m
in
g
th
e
id
en
tit
y
o
f
b
o
th
n
o
r
m
al
an
d
a
d
v
er
s
ar
y
d
ev
ices
in
I
o
T
.
T
h
e
p
r
im
e
c
o
n
tr
ib
u
tio
n
o
f
th
is
m
eth
o
d
o
l
o
g
y
is
to
war
d
s
p
er
f
o
r
m
in
g
a
co
s
t
-
ef
f
ec
tiv
e,
h
i
g
h
ly
r
ed
u
ce
d
iter
ativ
e,
an
d
f
aster
d
etec
tio
n
o
f
u
n
k
n
o
wn
f
o
r
m
o
f
a
d
v
er
s
ar
ies
th
at
is
lef
t
u
n
ad
d
r
ess
ed
in
e
x
is
tin
g
liter
atu
r
es
.
T
h
e
n
ex
t
s
ec
tio
n
d
is
cu
s
s
es
ab
o
u
t
th
e
s
tu
d
y
o
u
tco
m
es.
3.
RE
SU
L
T
T
h
is
s
ec
tio
n
d
is
cu
s
s
es
ab
o
u
t
th
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tco
m
e
ac
co
m
p
lis
h
ed
f
r
o
m
th
e
im
p
lem
e
n
tatio
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f
th
e
m
eth
o
d
o
l
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g
y
illu
s
tr
ated
i
n
p
r
io
r
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tio
n
.
T
h
e
ass
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m
en
t
is
ca
r
r
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t
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id
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m
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m
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f
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co
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p
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with
AI
m
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h
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T
ab
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2
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T
ab
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2
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Nu
m
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r
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m
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Ex
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th
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r
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p
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in
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k
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is
tin
g
AI
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m
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d
els
v
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ML
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d
h
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r
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r
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m
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p
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tiv
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ML
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p
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h
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r
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AN
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teg
r
ates
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ied
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2
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s
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o
wca
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Fig
u
r
e
2
with
r
esp
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u
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e
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u
r
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lis
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tco
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r
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m
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el
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r
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en
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tem
Pro
p
with
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n
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tio
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p
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a
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s
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r
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iz.
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S,
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C
C
,
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ess
ag
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th
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ticatio
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MA
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DS,
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d
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ateg
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s
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ig
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e
ctiv
e
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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52
I
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May
20
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h
e
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tco
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en
tu
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s
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o
m
e
o
f
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h
e
k
e
y
f
i
n
d
in
g
s
v
iz.
i
)
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r
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p
o
s
ed
s
ch
em
e
co
n
tr
ib
u
tes
to
war
d
s
6
8
%
ac
cu
r
ac
y
in
t
h
r
e
at
d
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n
wh
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h
is
q
u
ite
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s
ig
n
if
ican
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ac
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p
li
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h
m
en
t
in
co
n
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ex
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elate
d
m
eth
o
d
s
,
ii)
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e
r
esp
o
n
s
e
tim
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o
f
p
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p
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s
ed
s
ch
em
e
is
im
p
r
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v
e
d
to
3
2
%
f
aster
,
iii)
th
e
d
ata
d
e
liv
er
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p
e
r
f
o
r
m
an
ce
is
n
o
ted
w
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7
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d
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ce
d
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o
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tly
m
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r
e
in
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n
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ast
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g
AI
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y
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,
iv
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co
m
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ar
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n
cr
y
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tio
n
-
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r
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d
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tu
r
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.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
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lec
E
n
g
&
C
o
m
p
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ased
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leth
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h
e
p
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p
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e
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d
y
m
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c
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n
tr
ib
u
tes
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d
s
in
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r
p
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atin
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f
o
llo
win
g
n
o
v
el
f
ea
tu
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es:
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e
p
r
o
p
o
s
ed
s
tu
d
y
u
s
es
a
co
m
b
in
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n
o
f
p
r
o
b
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ilit
y
m
o
d
el
f
o
r
in
tellig
en
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tr
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s
t
co
m
p
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tatio
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f
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llo
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p
l
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en
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g
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m
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s
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g
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ii)
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p
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b
ab
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b
ased
t
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s
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co
m
p
u
tatio
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ely
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ased
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iv
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th
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e
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y
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e
d
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in
f
o
r
m
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n
o
f
id
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n
tity
o
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h
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r
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wo
r
k
o
f
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er
s
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s
d
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g
a
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im
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ased
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t
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o
f
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ter
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r
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w
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r
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e
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r
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d
s
im
p
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v
in
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th
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s
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ity
p
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f
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m
a
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p
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s
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with
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r
e
ch
all
en
g
in
g
ass
ess
m
en
t
en
v
ir
o
n
m
en
t.
Mo
r
e
o
p
tim
iza
tio
n
ap
p
r
o
ac
h
es
will
b
e
in
v
esti
g
ated
to
war
d
s
f
in
d
in
g
c
o
r
r
elatio
n
b
etwe
en
ad
v
er
s
ar
y
-
b
ased
ev
en
ts
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d
an
y
ev
en
ts
with
s
u
b
-
o
p
tim
al
p
er
f
o
r
m
an
ce
with
o
u
t
in
tr
u
s
io
n
.
T
h
is
will
ass
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s
t
f
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r
th
er
t
o
ex
ten
d
th
e
ap
p
licab
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lity
o
f
p
r
o
p
o
s
ed
s
ch
e
m
e
to
war
d
s
ad
v
an
ce
d
AI
-
b
ased
c
y
b
er
t
h
r
ea
ts
.
ACK
NO
WL
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DG
E
M
E
NT
S
T
h
e
au
t
h
o
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ex
p
r
ess
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Aca
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h
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&
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n
ag
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m
en
t
E
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ee
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in
g
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My
s
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r
u
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n
d
ia
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o
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th
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o
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m
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p
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r
t,
an
d
d
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lar
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th
at
th
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esear
ch
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n
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tak
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n
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f
in
an
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o
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tr
ib
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tio
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s
f
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m
an
y
e
x
ter
n
al
en
titi
es.
RE
F
E
R
E
NC
E
S
[
1
]
T
.
R
a
j
mo
h
a
n
,
P
.
H
.
N
g
u
y
e
n
,
a
n
d
N
.
F
e
r
r
y
,
“
A
d
e
c
a
d
e
o
f
r
e
sea
r
c
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o
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p
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c
t
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f
o
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T
se
c
u
r
i
t
y
,
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C
y
b
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rse
c
u
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y
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v
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-
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.
[
2
]
S
.
D
a
l
a
l
e
t
a
l
.
,
“
N
e
x
t
-
g
e
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r
a
t
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o
n
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y
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f
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sy
s
t
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m
s
:
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ss
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V
M
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n
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z
e
d
C
H
A
I
D
d
e
c
i
si
o
n
t
r
e
e
,
”
J
.
C
l
o
u
d
C
o
m
p
u
t
.
Ad
v
.
S
y
s
t
.
A
p
p
l
.
,
v
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.
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o
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3
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:
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6
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7
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1
7
-
4.
[
3
]
I
.
H
.
S
a
r
k
e
r
,
A
.
S
.
M
.
K
a
y
e
s
,
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.
B
a
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sh
a
,
H
.
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l
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n
i
,
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.
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a
t
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r
s,
a
n
d
A
.
N
g
,
“
C
y
b
e
r
sec
u
r
i
t
y
d
a
t
a
s
c
i
e
n
c
e
:
a
n
o
v
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r
v
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e
w
f
r
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m
mac
h
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n
e
l
e
a
r
n
i
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g
p
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s
p
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c
t
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v
e
,
”
J
.
B
i
g
D
a
t
a
,
v
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l
.
7
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o
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5
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7
-
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-
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3
1
8
-
5
.
[
4
]
C
.
G
u
o
a
n
d
D
.
Li
,
“
I
O
T
se
c
u
r
i
t
y
p
r
i
v
a
c
y
p
r
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t
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t
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me
c
h
a
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sm
a
n
d
m
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c
h
a
n
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c
a
l
st
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c
t
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n
s
i
m
u
l
a
t
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o
n
o
p
t
i
m
i
z
a
t
i
o
n
,
”
EU
RAS
I
P
J
.
A
d
v
.
S
i
g
n
a
l
Pr
o
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e
ss.
,
v
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l
.
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o
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7
-
3.
[
5
]
M
.
A
a
q
i
b
,
A
.
A
l
i
,
L.
C
h
e
n
,
a
n
d
O
.
N
i
b
o
u
c
h
e
,
“
I
o
T
t
r
u
s
t
a
n
d
r
e
p
u
t
a
t
i
o
n
:
a
su
r
v
e
y
a
n
d
t
a
x
o
n
o
m
y
,
”
J
.
C
l
o
u
d
C
o
m
p
u
t
.
A
d
v
.
S
y
s
t
.
Ap
p
l
.
,
v
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l
.
1
2
,
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o
.
1
,
2
0
2
3
,
d
o
i
:
1
0
.
1
1
8
6
/
s
1
3
6
7
7
-
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2
3
-
0
0
4
1
6
-
8.
[
6
]
D
.
M
o
h
a
me
d
a
n
d
O
.
I
smae
l
,
“
En
h
a
n
c
e
me
n
t
o
f
a
n
I
o
T
h
y
b
r
i
d
i
n
t
r
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si
o
n
d
e
t
e
c
t
i
o
n
s
y
st
e
m
b
a
se
d
o
n
f
o
g
-
to
-
c
l
o
u
d
c
o
m
p
u
t
i
n
g
,
”
J.
C
l
o
u
d
C
o
m
p
u
t
.
A
d
v
.
S
y
st
.
A
p
p
l
.
,
v
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l
.
1
2
,
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o
.
1
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2
0
2
3
,
d
o
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1
0
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1
1
8
6
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s
1
3
6
7
7
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3
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0
0
4
2
0
-
y.
[
7
]
S
.
U
l
l
a
h
a
n
d
R
.
Za
h
i
l
a
h
,
“
C
u
r
v
e
2
5
5
1
9
b
a
se
d
l
i
g
h
t
w
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n
d
-
to
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n
d
e
n
c
r
y
p
t
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n
r
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so
u
r
c
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c
o
n
st
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d
a
u
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o
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o
m
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s
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b
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o
T
d
e
v
i
c
e
s,
”
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y
b
e
rs
e
c
u
ri
t
y
,
v
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l
.
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o
.
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6.
[
8
]
I
.
K
e
s
h
t
a
,
“
A
I
-
d
r
i
v
e
n
I
o
T
f
o
r
smar
t
h
e
a
l
t
h
c
a
r
e
:
se
c
u
r
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t
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a
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d
p
r
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v
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c
y
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ssu
e
s
,
”
I
n
f
o
rm
.
Me
d
.
U
n
l
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d
,
v
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l
.
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.
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.
2
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2
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0
0
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0
3
.
[
9
]
S
.
M
.
T
.
N
i
z
a
mu
d
e
e
n
,
“
I
n
t
e
l
l
i
g
e
n
t
i
n
t
r
u
si
o
n
d
e
t
e
c
t
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o
n
f
r
a
m
e
w
o
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k
f
o
r
mu
l
t
i
-
c
l
o
u
d
s
–
I
o
T
e
n
v
i
r
o
n
me
n
t
u
si
n
g
sw
a
r
m
-
b
a
s
e
d
d
e
e
p
l
e
a
r
n
i
n
g
c
l
a
ss
i
f
i
e
r
,
”
J
.
C
l
o
u
d
C
o
m
p
u
t
.
Ad
v
.
S
y
s
t
.
A
p
p
l
.
,
v
o
l
.
1
2
,
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o
.
1
,
2
0
2
3
,
d
o
i
:
1
0
.
1
1
8
6
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s
1
3
6
7
7
-
023
-
0
0
5
0
9
-
4.
[
1
0
]
P
.
R
a
d
a
n
l
i
e
v
e
t
a
l
.
,
“
C
y
b
e
r
r
i
s
k
a
t
t
h
e
e
d
g
e
:
c
u
r
r
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n
t
a
n
d
f
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t
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r
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t
r
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n
d
s
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y
b
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r
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s
k
a
n
a
l
y
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s
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n
d
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r
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f
i
c
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a
l
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l
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g
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n
c
e
i
n
t
h
e
i
n
d
u
st
r
i
a
l
i
n
t
e
r
n
e
t
o
f
t
h
i
n
g
s
a
n
d
i
n
d
u
st
r
y
4
.
0
s
u
p
p
l
y
c
h
a
i
n
s,
”
C
y
b
e
rs
e
c
u
ri
t
y
,
v
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l
.
3
,
n
o
.
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,
2
0
2
0
,
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o
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:
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1
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0
5
2
-
8.
[
1
1
]
J.
L
i
u
e
t
a
l
.
,
“
Tr
i
C
TI
:
a
n
a
c
t
i
o
n
a
b
l
e
c
y
b
e
r
t
h
r
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a
t
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n
t
e
l
l
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g
e
n
c
e
d
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sc
o
v
e
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y
s
y
st
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m
v
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a
t
r
i
g
g
e
r
-
e
n
h
a
n
c
e
d
n
e
u
r
a
l
n
e
t
w
o
r
k
,
”
C
y
b
e
rs
e
c
u
ri
t
y
,
v
o
l
.
5
,
n
o
.
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,
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,
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0
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3.
[
1
2
]
A
.
S
r
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v
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s
t
a
v
a
,
S
.
K
.
G
u
p
t
a
,
M
.
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a
j
i
m,
N
.
S
a
h
u
,
G
.
A
g
g
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r
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a
l
,
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n
d
B
.
D
.
M
a
z
u
m
d
a
r
,
“
D
S
S
A
M
:
d
i
g
i
t
a
l
l
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s
i
g
n
e
d
se
c
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r
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a
c
k
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o
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d
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m
e
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t
me
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h
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d
f
o
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m
o
b
i
l
e
a
d
h
o
c
n
e
t
w
o
r
k
,
”
EU
RA
S
I
P
J
.
W
i
rel
.
C
o
m
m
u
n
.
N
e
t
w
.
,
v
o
l
.
2
0
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1
,
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o
.
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,
2
0
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1
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-
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0
1
8
9
4
-
7.
[
1
3
]
B
.
S
u
n
,
R
.
G
e
n
g
,
L.
Zh
a
n
g
,
S
.
Li
,
T.
S
h
e
n
,
a
n
d
L
.
M
a
,
“
S
e
c
u
r
i
n
g
6
G
-
e
n
a
b
l
e
d
I
o
T
/
I
o
V
n
e
t
w
o
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k
s
b
y
m
a
c
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n
e
l
e
a
r
n
i
n
g
a
n
d
d
a
t
a
f
u
si
o
n
,
”
EU
RA
S
I
P
J
.
Wi
r
e
l
.
C
o
m
m
u
n
.
N
e
t
w
.
,
v
o
l
.
2
0
2
2
,
n
o
.
1
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2
0
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2
,
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o
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:
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0
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6
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6
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8
-
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1
9
3
-
5.
[
1
4
]
N
.
Zh
o
u
e
t
a
l
.
,
“
C
o
n
t
a
i
n
e
r
o
r
c
h
e
s
t
r
a
t
i
o
n
o
n
H
P
C
s
y
st
e
ms
t
h
r
o
u
g
h
K
u
b
e
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n
e
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c
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tac
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:
ra
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k
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g
m
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c
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m
.
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