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52
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J
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1
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20
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i
tes
m
im
ick
in
g
leg
itima
te
o
n
es.
I
n
d
r
asir
i
et
a
l.
[6
]
in
tr
o
d
u
ce
d
a
h
y
b
r
id
l
o
n
g
s
h
o
r
t
-
ter
m
m
em
o
r
y
(
L
STM
)
a
n
d
g
ated
r
ec
u
r
r
e
n
t
u
n
it
(
GR
U
)
m
o
d
el
f
o
r
p
h
is
h
in
g
UR
L
d
etec
tio
n
.
Desp
ite
its
p
o
ten
tial,
it
f
ac
es
ch
allen
g
es
in
co
m
p
u
tatio
n
al
co
m
p
lex
ity
a
n
d
tr
ain
i
n
g
d
ata
r
eq
u
ir
e
m
en
ts
.
Ah
m
ed
et
a
l.
[7
]
p
r
esen
ted
a
n
eu
r
al
n
etwo
r
k
m
o
d
el
o
p
tim
ize
d
f
o
r
f
ea
tu
r
e
s
elec
tio
n
in
p
h
is
h
in
g
d
etec
tio
n
,
w
h
ic
h
m
ay
n
o
t
g
e
n
er
alize
well
to
n
ew
p
h
is
h
in
g
attac
k
s
an
d
r
e
q
u
ir
es
f
r
eq
u
en
t
r
etr
ai
n
in
g
.
Kar
a
et
a
l.
[8
]
p
r
o
v
i
d
ed
a
s
u
r
v
ey
o
f
ML
tech
n
i
q
u
es
f
o
r
m
alicio
u
s
UR
L
d
etec
tio
n
,
p
o
ten
tially
m
is
s
in
g
th
e
latest
m
eth
o
d
s
o
r
em
e
r
g
in
g
th
r
ea
ts
d
u
e
to
th
e
r
a
p
id
ly
ev
o
lv
in
g
n
atu
r
e
o
f
c
y
b
er
s
ec
u
r
ity
.
Alth
o
b
aiti
et
a
l.
[9
]
em
p
lo
y
e
d
d
ee
p
lear
n
in
g
f
o
r
UR
L
r
ep
r
esen
tatio
n
,
f
ac
in
g
c
h
allen
g
es
with
s
ig
n
if
ican
t
co
m
p
u
tatio
n
al
r
eso
u
r
ce
s
an
d
le
n
g
th
y
tr
ain
i
n
g
tim
es.
Ar
iy
ad
asa
et
a
l.
[
1
0
]
p
r
o
p
o
s
e
d
u
s
in
g
lex
ical
f
ea
tu
r
es
an
d
o
n
lin
e
lear
n
i
n
g
f
o
r
p
h
is
h
in
g
d
e
tectio
n
,
wh
ich
m
ig
h
t
n
o
t
ef
f
ec
tiv
ely
d
etec
t
ze
r
o
-
d
a
y
attac
k
s
o
r
s
o
p
h
is
ticated
s
tr
ateg
ies.
Sah
in
g
o
z
et
a
l.
[
1
1
]
ex
am
in
ed
th
e
ev
o
lu
tio
n
o
f
p
h
is
h
in
g
attac
k
s
b
u
t
m
a
y
lack
s
p
ec
if
ic
tech
n
ical
s
o
lu
tio
n
s
o
r
ad
d
r
ess
th
e
o
p
er
a
tio
n
al
ch
allen
g
es
o
f
im
p
lem
en
tin
g
an
ti
-
p
h
is
h
in
g
m
ea
s
u
r
es.
T
h
e
a
b
o
v
e
r
elate
d
wo
r
k
s
s
tr
iv
e
h
a
r
d
to
d
etec
t
t
h
e
p
h
is
h
in
g
attac
k
s
b
u
t
f
ailed
to
d
etec
t
th
e
ze
r
o
-
d
a
y
a
ttack
s
.
Hen
ce
,
o
u
r
a
p
p
r
o
ac
h
wo
r
k
m
ain
ly
f
o
cu
s
es
o
n
d
etec
tin
g
ze
r
o
-
d
ay
attac
k
s
b
ased
o
n
UR
L
m
etad
ata.
2
.
1
.
Resea
rc
h g
a
ps
I
d
en
tify
in
g
r
esear
ch
g
ap
s
in
ML
f
o
r
p
h
is
h
in
g
attac
k
d
etec
tio
n
is
v
ital
f
o
r
im
p
r
o
v
in
g
cy
b
er
s
ec
u
r
ity
.
Key
ar
ea
s
n
ee
d
in
g
f
u
r
t
h
er
e
x
p
lo
r
atio
n
in
clu
d
e
th
e
d
ev
elo
p
m
en
t
o
f
co
m
p
r
eh
e
n
s
iv
e
d
atas
ets
th
at
ca
p
tu
r
e
th
e
latest
p
h
is
h
in
g
tactics,
en
h
an
cin
g
th
e
ad
a
p
tab
ilit
y
an
d
s
ca
lab
ilit
y
o
f
ML
m
o
d
els
to
r
e
al
-
wo
r
ld
co
n
d
itio
n
s
,
an
d
in
teg
r
atin
g
th
ese
m
o
d
els
with
in
ex
is
tin
g
c
y
b
er
s
ec
u
r
ity
f
r
am
ewo
r
k
s
.
Ad
d
itio
n
ally
,
ad
d
r
ess
in
g
th
e
c
h
allen
g
e
o
f
f
alse
p
o
s
itiv
es
an
d
n
eg
ativ
es
in
d
etec
tio
n
is
cr
u
cial
f
o
r
m
ain
tain
in
g
u
s
er
tr
u
s
t
an
d
th
e
ef
f
ec
tiv
en
ess
o
f
s
ec
u
r
ity
m
ea
s
u
r
es.
T
ac
k
lin
g
t
h
ese
g
ap
s
p
r
o
m
is
es
to
b
o
o
s
t
th
e
ac
cu
r
ac
y
an
d
r
eliab
ili
ty
o
f
p
h
is
h
in
g
d
etec
tio
n
,
co
n
tr
ib
u
tin
g
to
a
s
af
er
d
ig
ital
en
v
ir
o
n
m
en
t [
1
2
]
.
2
.
2
.
Appl
ica
t
io
ns
ML
s
ig
n
if
ican
tly
b
o
ls
ter
s
c
y
b
er
s
ec
u
r
ity
b
y
d
etec
tin
g
p
h
is
h
in
g
attac
k
s
t
h
r
o
u
g
h
UR
L
an
aly
s
is
,
b
en
ef
itin
g
in
d
iv
id
u
al
u
s
er
s
,
o
r
g
an
izatio
n
s
,
f
in
a
n
cial
in
s
titu
tio
n
s
,
clo
u
d
s
er
v
ices,
e
-
c
o
m
m
er
ce
p
latf
o
r
m
s
,
a
n
d
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
A
n
o
p
tima
l m
a
c
h
in
e
lea
r
n
in
g
-
b
a
s
ed
a
lg
o
r
ith
m
fo
r
d
etec
tin
g
p
h
is
h
in
g
… (
N
a
n
d
ee
s
h
a
Ha
lli
myso
r
e
Dev
a
r
a
j
)
633
cy
b
er
s
ec
u
r
ity
t
r
ain
in
g
p
r
o
g
r
a
m
s
.
Fo
r
in
d
iv
i
d
u
als,
ML
alg
o
r
ith
m
s
in
teg
r
ated
in
to
we
b
b
r
o
wser
s
an
d
e
m
ail
clien
ts
’
aler
t u
s
er
s
to
h
ar
m
f
u
l
UR
L
s
,
p
r
ev
en
tin
g
p
h
is
h
in
g
f
r
a
u
d
s
.
Or
g
an
izatio
n
s
an
d
f
in
an
ci
al
in
s
titu
tio
n
s
u
til
ize
th
ese
s
y
s
tem
s
wi
th
in
th
eir
n
et
wo
r
k
s
ec
u
r
ity
to
p
r
o
tect
ag
ai
n
s
t
p
h
is
h
in
g
,
s
af
eg
u
ar
d
in
g
tr
a
n
s
ac
tio
n
s
an
d
s
en
s
itiv
e
d
ata.
E
-
co
m
m
er
ce
p
latf
o
r
m
s
u
s
e
th
ese
alg
o
r
ith
m
s
to
b
l
o
ck
p
h
is
h
in
g
UR
L
s
th
at
m
im
ic
leg
itima
te
s
ites
,
p
r
ev
en
tin
g
f
r
au
d
.
Ad
d
itio
n
all
y
,
ML
ap
p
l
icatio
n
s
in
p
h
is
h
i
n
g
d
etec
tio
n
o
f
f
e
r
s
ca
lab
le,
e
f
f
ec
tiv
e
c
y
b
er
s
ec
u
r
ity
s
o
lu
tio
n
s
ac
r
o
s
s
v
ar
io
u
s
s
ec
to
r
s
[
1
3
]
.
3.
M
E
T
H
O
D
Fig
u
r
e
2
s
h
o
ws
th
e
p
r
o
p
o
s
ed
m
eth
o
d
o
l
o
g
y
e
n
ca
p
s
u
lates
a
f
iv
e
-
tier
ed
ap
p
r
o
ac
h
to
d
etec
ti
n
g
p
h
is
h
in
g
UR
L
s
u
s
in
g
an
o
p
tim
al
m
ac
h
in
e
lear
n
in
g
-
b
ased
alg
o
r
it
h
m
(
Om
L
A
)
[
14
]
.
T
h
is
en
h
an
ce
d
m
eth
o
d
o
lo
g
y
in
teg
r
ates
ad
v
an
ce
d
d
ata
h
an
d
lin
g
b
y
u
tili
zin
g
a
r
ic
h
er
d
atas
et
t
h
at
in
clu
d
es
r
ea
l
-
tim
e
p
h
is
h
in
g
attac
k
d
ata
an
d
h
is
to
r
y
,
ex
p
a
n
d
in
g
b
ey
o
n
d
tr
a
d
itio
n
al
UR
L
an
aly
s
is
.
Fu
r
th
e
r
d
ee
p
lear
n
in
g
tech
n
iq
u
es,
p
a
r
ticu
la
r
ly
R
NNs,
wil
l
b
e
in
tr
o
d
u
ce
d
f
o
r
m
o
r
e
s
o
p
h
is
ticated
p
atter
n
r
ec
o
g
n
itio
n
in
UR
L
s
[
1
4]
,
e
n
h
an
cin
g
th
e
m
o
d
el
’
s
d
etec
ti
o
n
ca
p
ab
ilit
ies wh
ich
en
s
u
r
es
a
r
o
b
u
s
t d
ef
en
s
e
m
ec
h
a
n
is
m
ag
ain
s
t so
p
h
is
ticated
p
h
is
h
in
g
th
r
ea
ts
.
T
h
e
v
alid
atio
n
o
f
Om
L
A
will
ad
o
p
t
a
m
o
r
e
r
ig
o
r
o
u
s
ap
p
r
o
ac
h
,
em
p
lo
y
in
g
co
m
p
r
eh
en
s
iv
e
b
en
ch
m
ar
k
in
g
ag
ai
n
s
t
b
o
th
tr
ad
itio
n
al
an
d
cu
ttin
g
-
ed
g
e
m
eth
o
d
s
.
T
h
is
will
en
s
u
r
e
i
ts
ef
f
ec
tiv
en
ess
an
d
r
eliab
ilit
y
in
d
etec
tin
g
p
h
is
h
i
n
g
UR
L
s
,
with
a
f
o
cu
s
o
n
r
ed
u
cin
g
f
alse
p
o
s
itiv
es
an
d
a
d
ap
tin
g
to
ev
o
lv
in
g
p
h
is
h
in
g
s
tr
ateg
ies
[
1
5
]
.
B
y
in
teg
r
atin
g
t
h
ese
en
h
a
n
ce
m
e
n
ts
,
th
e
m
eth
o
d
o
l
o
g
y
s
ec
tio
n
o
u
tlin
es
a
f
o
r
war
d
-
th
in
k
in
g
a
p
p
r
o
ac
h
th
at
n
o
t o
n
l
y
ad
d
r
ess
es c
u
r
r
en
t c
h
alle
n
g
es in
p
h
is
h
in
g
d
etec
tio
n
b
u
t a
ls
o
s
ets th
e
g
r
o
u
n
d
wo
r
k
f
o
r
f
u
tu
r
e
in
n
o
v
atio
n
s
in
cy
b
e
r
s
ec
u
r
ity
m
ea
s
u
r
es.
Fig
u
r
e
2
.
T
h
e
m
eth
o
d
o
lo
g
y
f
o
r
a
f
iv
e
-
tier
e
d
ap
p
r
o
ac
h
t
o
d
et
ec
tin
g
p
h
is
h
in
g
UR
L
s
4.
P
H
I
SH
E
R
AN
D
URL
Attack
er
s
u
s
e
a
wid
e
v
ar
iety
o
f
ev
asio
n
s
tr
ateg
ies
in
o
r
d
er
to
av
o
id
b
ein
g
id
en
tifie
d
b
y
s
ec
u
r
ity
m
ea
s
u
r
es
o
r
s
y
s
tem
ad
m
in
is
t
r
ato
r
s
.
T
h
is
allo
ws
th
em
to
s
teal
in
f
o
r
m
a
tio
n
with
o
u
t
b
ei
n
g
d
is
co
v
e
r
ed
[
1
6
]
.
T
h
e
f
o
llo
win
g
s
ec
tio
n
will
p
r
o
v
id
e
a
m
o
r
e
in
-
d
ep
th
an
aly
s
is
o
f
a
f
ew
o
f
th
ese
v
a
r
io
u
s
ap
p
r
o
ac
h
es
t
o
im
p
lem
en
tatio
n
.
I
n
th
e
f
ir
s
t
p
lace
,
it
is
n
ec
es
s
ar
y
to
h
av
e
a
r
u
d
im
en
tar
y
c
o
m
p
r
e
h
en
s
io
n
o
f
th
e
co
m
p
o
n
e
n
ts
th
at
m
ak
e
u
p
UR
L
s
in
o
r
d
er
to
ac
h
iev
e
a
g
r
asp
o
f
th
e
m
eth
o
d
o
lo
g
y
th
at
is
u
tili
ze
d
b
y
m
alicio
u
s
ac
to
r
s
[
1
7
]
.
A
g
r
ap
h
ical
illu
s
tr
atio
n
o
f
atta
ck
p
r
o
ce
s
s
p
h
ases
is
p
r
esen
ted
in
Fig
u
r
e
3
.
I
t
is
co
m
m
o
n
f
o
r
th
e
f
i
r
s
t
s
eg
m
en
t
o
f
a
UR
L
to
b
e
th
e
p
r
o
to
co
l
n
am
e
o
f
th
e
p
ag
e,
wh
ich
id
en
tifie
s
th
e
m
et
h
o
d
b
y
wh
ich
th
e
p
ag
e
ca
n
b
e
r
ea
ch
ed
.
A
Su
b
-
d
o
m
ai
n
an
d
a
s
ec
o
n
d
-
lev
el
d
o
m
ain
(
SLD)
n
am
e
ar
e
t
h
e
co
m
p
o
n
en
ts
th
at
m
ak
e
u
p
th
e
s
ec
o
n
d
s
eg
m
en
t
,
wh
ich
is
co
m
p
r
is
ed
o
f
th
e
in
s
t
itu
tio
n
’
s
titl
e
in
th
e
s
er
v
er
h
o
s
tin
g
.
Fo
llo
win
g
th
at,
th
e
to
p
-
le
v
el
d
o
m
ain
(
T
L
D
)
n
am
e
is
u
s
ed
to
d
en
o
te
th
e
d
o
m
ain
s
th
at
ar
e
lo
ca
ted
in
th
e
DNS
r
o
o
t
zo
n
e
o
f
th
e
i
n
ter
n
et
.
T
h
e
n
am
e
o
f
th
e
p
ag
e
an
d
t
h
e
in
ter
n
al
s
er
v
er
a
d
d
r
ess
ar
e
th
e
co
m
p
o
n
en
ts
th
at
m
ak
e
u
p
th
e
p
ath
o
f
th
e
p
a
g
e.
E
v
en
if
th
e
SLD
f
r
eq
u
en
tly
d
is
clo
s
es
th
e
n
atu
r
e
o
f
th
e
ac
tiv
ity
o
r
th
e
co
m
p
a
n
y
n
am
e,
a
h
o
s
tile
ac
to
r
ca
n
ea
s
ily
p
u
r
ch
ase
it
an
d
u
s
e
it
f
o
r
p
h
is
h
in
g
p
u
r
p
o
s
es
to
g
ain
ac
ce
s
s
to
s
en
s
itiv
e
in
f
o
r
m
atio
n
.
B
ec
au
s
e
o
f
th
e
co
m
b
in
atio
n
o
f
th
e
TLD
an
d
th
e
SLD
,
ea
ch
UR
L
h
as
th
e
ap
p
ea
r
an
ce
o
f
b
ein
g
u
n
iq
u
e
b
ec
a
u
s
e
o
f
th
is
.
C
o
m
p
an
ies
th
at
p
r
o
v
id
e
cy
b
er
s
ec
u
r
ity
d
ev
o
te
a
s
u
b
s
tan
tial
am
o
u
n
t
o
f
r
eso
u
r
ce
s
in
o
r
d
er
t
o
id
en
tify
th
e
f
a
k
e
d
o
m
ain
s
th
at
ar
e
u
s
ed
in
p
h
is
h
in
g
attac
k
s
.
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.
36
,
No
.
1
,
Octo
b
er
20
24
:
63
1
-
6
3
8
634
Fig
u
r
e
3
.
F
u
n
d
am
en
tal
p
h
ases
in
attac
k
p
r
o
ce
s
s
W
h
en
ev
er
it
is
d
is
co
v
er
ed
th
at
a
ce
r
tain
web
a
d
d
r
ess
is
b
ein
g
u
s
ed
f
o
r
t
h
e
p
u
r
p
o
s
e
o
f
p
h
is
h
in
g
,
th
e
I
P
a
d
d
r
ess
th
at
is
lin
k
ed
with
th
at
w
eb
ad
d
r
ess
m
ay
b
e
s
im
p
ly
b
lack
lis
ted
.
T
h
is
wil
l
p
r
ev
e
n
t
u
s
er
s
f
r
o
m
ac
ce
s
s
in
g
th
e
web
s
ites
th
at
ar
e
h
o
s
ted
with
in
th
e
d
o
m
ain
.
Am
o
n
g
th
e
m
o
s
t
ess
en
tial
tactic
s
,
th
e
u
s
e
o
f
ar
b
itra
r
y
ch
ar
ac
ter
s
,
th
e
co
m
b
in
atio
n
o
f
ce
r
tain
w
o
r
d
s
,
c
y
b
er
s
q
u
attin
g
,
ty
p
o
s
q
u
attin
g
,
a
n
d
o
th
er
m
e
th
o
d
s
ar
e
am
o
n
g
t
h
e
m
o
s
t
cr
itical
ap
p
r
o
ac
h
es
[
1
8
]
.
B
ec
au
s
e
o
f
th
is
,
th
e
d
etec
tio
n
alg
o
r
ith
m
s
n
ee
d
to
tak
e
i
n
to
co
n
s
id
er
atio
n
th
e
ass
au
lt m
eth
o
d
s
th
at
wer
e
d
is
cu
s
s
ed
b
ef
o
r
e.
5.
DIFF
I
CU
L
T
I
E
S T
O
O
VE
R
CO
M
E
Desp
ite
th
e
f
ac
t
th
at
th
er
e
h
as
b
ee
n
tr
em
en
d
o
u
s
p
r
o
g
r
ess
m
ad
e
o
v
er
th
e
c
o
u
r
s
e
o
f
th
e
l
ast
d
ec
ad
e
in
id
en
tify
in
g
th
e
m
alicio
u
s
UR
L
s
.
B
u
t
s
till
th
er
e
is
a
s
co
p
e
f
o
r
im
p
r
o
v
em
en
ts
th
at
h
av
e
n
o
t
b
ee
n
r
eso
lv
e
d
.
T
h
e
is
s
u
es
h
av
e
b
ee
n
id
en
tifie
d
b
y
c
o
n
d
u
ctin
g
lite
r
atu
r
e
s
u
r
v
ey
th
o
r
o
u
g
h
ly
.
T
h
ese
is
s
u
es
in
clu
d
in
g
b
u
t
n
o
t
lim
ited
to
th
e
f
o
llo
win
g
s
itu
atio
n
s
:
5
.
1
.
An
eno
r
m
o
us
qu
a
ntit
y
o
f
URLs
T
h
e
v
ast
a
n
d
d
y
n
am
ic
n
atu
r
e
o
f
UR
L
d
ata,
wh
ich
p
r
esen
ts
a
s
ig
n
if
ican
t
c
h
allen
g
e
in
t
r
ain
in
g
m
o
d
els
f
o
r
e
f
f
ec
tiv
e
p
h
is
h
in
g
d
etec
tio
n
[
1
9
]
.
T
h
is
is
s
u
e
is
co
m
p
o
u
n
d
ed
b
y
t
h
e
d
if
f
icu
lty
o
f
s
elec
tin
g
tr
ai
n
in
g
d
ata
t
h
at
ac
cu
r
ately
r
ep
r
esen
ts
b
o
th
h
ar
m
f
u
l
an
d
b
e
n
i
g
n
UR
L
s
,
cr
u
ci
al
f
o
r
th
e
ef
f
ec
tiv
e
n
ess
o
f
ML
m
o
d
els
in
d
etec
tin
g
f
ak
e
UR
L
s
[
2
0
]
.
An
o
th
er
cr
itical
ch
allen
g
e
is
ac
q
u
is
itio
n
o
f
f
ea
tu
r
es
an
d
lab
els
f
o
r
tr
ain
in
g
m
ac
h
in
e
-
lear
n
in
g
m
o
d
els.
I
t
also
n
o
tes
th
e
s
ca
r
city
o
f
lab
eled
d
ata,
e
s
s
en
tial
f
o
r
s
u
p
er
v
is
ed
lea
r
n
in
g
m
et
h
o
d
s
[
2
1
]
.
T
h
is
ap
p
r
o
ac
h
aim
s
to
d
e
v
elo
p
a
r
o
b
u
s
t
m
o
d
el
ca
p
ab
le
o
f
d
is
tin
g
u
is
h
in
g
b
etwe
en
p
h
i
s
h
in
g
an
d
le
g
i
tim
ate
UR
L
s
ef
f
ec
tiv
ely
[
22
].
5
.
2
.
Dif
f
icultie
s
t
ha
t
persis
t
Fu
r
th
er
m
o
r
e
,
p
h
is
h
er
s
m
ak
e
u
s
e
o
f
UR
L
s
h
o
r
te
n
in
g
s
er
v
ices
wh
ich
p
r
o
v
id
e
a
n
ef
f
icien
t
m
eth
o
d
o
f
d
is
g
u
is
in
g
h
ar
m
f
u
l
UR
L
s
,
wh
ich
ca
n
m
a
k
e
it
m
o
r
e
c
h
allen
g
in
g
f
o
r
c
o
m
p
u
te
r
ized
s
y
s
tem
s
to
r
ec
o
g
n
ize
a
n
d
d
etec
t
tin
y
UR
L
s
[
2
3
]
.
I
t
is
q
u
ite
p
r
o
b
ab
le
th
at
th
e
r
e
will
alwa
y
s
b
e
a
v
ar
iety
o
f
lim
it
s
co
n
n
ec
ted
with
th
e
d
etec
tio
n
o
f
u
n
s
af
e
UR
L
s
.
R
esear
ch
th
at
is
co
n
d
u
cte
d
o
v
e
r
an
ex
ten
d
e
d
p
er
i
o
d
o
f
tim
e
wi
ll
b
e
f
o
cu
s
ed
o
n
th
e
d
ev
elo
p
m
e
n
t o
f
e
f
f
ec
tiv
e
s
y
s
tem
s
wh
ich
ca
n
ab
le
to
r
ec
o
g
n
iz
e
an
d
d
etec
t
ze
r
o
-
d
a
y
attac
k
s
[
24
].
5
.
3
.
E
f
f
ec
t
s
o
f
m
a
licio
us
nes
s
As
m
ac
h
in
e
-
lear
n
in
g
m
o
d
els
g
et
p
o
p
u
lar
ity
in
r
ec
o
g
n
izin
g
an
d
class
if
y
in
g
s
u
s
p
icio
u
s
UR
L
s
,
it
is
lo
g
ical
to
p
r
e
d
ict
th
at
m
alicio
u
s
ac
to
r
s
m
ay
ad
o
p
t
s
o
p
h
is
tic
ated
m
eth
o
d
s
in
o
r
d
e
r
to
b
o
o
s
t
th
e
s
u
cc
ess
o
f
th
eir
ass
au
lts
.
A
ttack
er
s
ar
e
alwa
y
s
u
s
in
g
in
tr
icate
m
eth
o
d
s
to
lu
r
e
u
s
er
’
s
in
f
o
r
m
atio
n
.
T
h
is
is
b
ec
au
s
e
ad
v
er
s
ar
ial
s
tr
ateg
ies ar
e
d
esig
n
ed
to
m
a
k
e
attac
k
s
m
o
r
e
ef
f
ec
tiv
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
A
n
o
p
tima
l m
a
c
h
in
e
lea
r
n
in
g
-
b
a
s
ed
a
lg
o
r
ith
m
fo
r
d
etec
tin
g
p
h
is
h
in
g
… (
N
a
n
d
ee
s
h
a
Ha
lli
myso
r
e
Dev
a
r
a
j
)
635
6.
P
RO
P
O
SE
D
M
O
D
E
L
F
O
R
T
H
E
P
H
I
SH
I
NG
DE
T
E
CT
I
O
N
USI
NG
M
ACH
I
N
E
L
E
ARNING
T
h
e
p
r
o
ce
s
s
o
f
p
h
is
h
in
g
d
etec
tio
n
i
s
d
ep
icted
in
Fig
u
r
e
4
,
wh
ich
d
em
o
n
s
tr
ates
th
e
m
o
d
el.
T
h
e
s
u
g
g
ested
m
o
d
el
b
eg
in
s
with
th
e
d
is
co
v
er
y
o
f
a
d
ataset
th
at
is
co
m
p
r
is
ed
o
f
d
o
m
ain
attr
ib
u
tes
an
d
f
ea
tu
r
es
th
at
ar
e
b
ased
o
n
UR
L
s
.
T
h
e
d
ataset
is
co
n
s
tr
u
cted
with
t
h
e
h
elp
o
f
web
c
r
awle
r
wh
ic
h
is
r
esp
o
n
s
ib
le
f
o
r
co
llect
in
g
leg
itima
te
web
s
ite
UR
L
’
s
an
d
p
h
is
h
in
g
UR
L
’
s
.
Ar
o
u
n
d
1
8
4
3
6
UR
L
s
wer
e
d
ep
o
s
ited
in
a
d
ataset
am
o
n
g
8
6
6
7
ar
e
leg
itima
te
U
R
L
s
co
llected
f
r
o
m
web
cr
awl
er
s
p
ec
if
ic
to
k
ey
wo
r
d
s
r
elate
d
to
h
ea
lth
c
ar
e,
s
o
cial
m
ed
ia,
b
a
n
k
in
g
s
ec
to
r
a
n
d
e
d
u
ca
tio
n
al
r
elate
d
web
s
it
es
a
n
d
9
7
6
9
UR
L
s
ar
e
p
h
is
h
in
g
UR
L
s
co
llected
f
r
o
m
Ph
is
h
T
an
k
an
d
Op
en
Ph
is
h
web
s
ites
.
Acc
o
r
d
in
g
to
t
h
e
an
ti
-
p
h
is
h
in
g
w
o
r
k
in
g
g
r
o
u
p
(
APW
G)
[
2
5
]
,
m
o
s
t
tar
g
eted
s
ec
to
r
s
o
f
p
h
is
h
in
g
at
tack
s
ar
e
r
elate
d
to
th
e
a
b
o
v
e
k
ey
wo
r
d
.
Hen
ce
,
co
llectin
g
UR
L
s
r
elate
d
to
th
es
e
k
ey
wo
r
d
s
is
m
o
r
e
im
p
o
r
tan
t
an
d
c
r
awle
r
is
b
u
ilt
to
f
etch
t
h
e
UR
L
’
s
u
p
to
th
e
d
ep
t
h
o
f
two
.
B
ec
au
s
e
i
f
we
f
u
r
th
er
cr
awl
th
e
web
p
a
g
es m
o
r
e
th
an
th
e
d
ep
t
h
o
f
two
,
u
lti
m
ately
it b
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o
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e
s
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m
ilar
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o
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wo
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b
a
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ata
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UR
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m
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ated
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Op
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th
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p
h
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h
in
g
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b
s
ites
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ly
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n
u
m
b
er
o
f
h
o
u
r
s
o
r
d
a
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s
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h
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p
r
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p
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k
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f
o
cu
s
in
g
o
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co
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l
tim
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d
ata
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d
b
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n
th
e
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ewly
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n
s
tr
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ataset.
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ce
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d
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p
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t r
at
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ataset.
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d
i
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m
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u
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ased
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7.
P
RO
P
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D
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CAL M
O
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Acc
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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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d
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J
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g
&
C
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p
Sci
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Vo
l.
36
,
No
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1
,
Octo
b
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20
24
:
63
1
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3
8
636
Pre
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(
p
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s
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:
i
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d
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s
th
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c
o
r
r
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t
n
ess
ac
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p
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class
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t
ass
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s
th
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p
r
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p
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tio
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th
at
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e
ac
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co
r
r
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t is r
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b
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e
(
2
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.
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=
+
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2
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R
ec
all
(
s
en
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itiv
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r
tr
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p
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m
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m
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F1
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8.
RE
SU
L
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8
.
1
.
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x
perim
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Fig
u
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5
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Per
f
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m
an
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s
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b
etwe
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an
d
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v
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m
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s
T
ab
le
2
p
r
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th
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s
im
u
latio
n
p
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am
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s
th
at
wer
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in
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with
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Fig
u
r
e
6
.
C
o
m
p
a
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ativ
e
an
aly
s
e
s
o
f
co
n
v
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n
tio
n
al
a
n
d
p
r
o
p
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s
ed
m
eth
o
d
s
with
r
esp
ec
t to
s
ca
lab
le
p
ar
am
eter
s
9.
CO
NCLU
SI
O
N
T
h
e
p
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p
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s
ed
wo
r
k
h
ig
h
lig
h
t
s
th
e
ad
v
a
n
ce
m
en
ts
in
co
m
b
atin
g
cy
b
e
r
s
ec
u
r
ity
t
h
r
ea
ts
,
f
o
cu
s
in
g
o
n
p
h
is
h
in
g
attac
k
d
etec
tio
n
th
r
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g
h
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L
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.
T
h
e
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is
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g
in
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d
to
an
al
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UR
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s
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y
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in
g
th
ei
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h
is
to
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y
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in
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n
g
o
p
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al
d
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d
web
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af
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ic,
to
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en
tify
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tial
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h
is
h
in
g
ac
ti
v
ities
.
C
o
m
p
ar
ed
to
tr
ad
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n
al
m
eth
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d
s
lik
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RF
,
SVM
,
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d
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,
th
e
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s
h
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p
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ates
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s
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ates
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em
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k
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f
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,
with
d
etec
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n
an
d
r
esp
o
n
s
e
tim
es
s
ig
n
if
ican
tly
b
etter
th
an
th
o
s
e
o
f
co
n
v
e
n
tio
n
al
m
eth
o
d
s
.
T
h
is
im
p
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v
e
m
en
t
is
cr
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in
th
e
f
ast
-
m
o
v
in
g
d
i
g
ital
en
v
ir
o
n
m
en
t,
wh
er
e
th
e
r
ap
id
id
en
tific
a
tio
n
an
d
m
itig
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n
o
f
p
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is
h
in
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UR
L
s
ca
n
p
r
ev
en
t
s
u
b
s
tan
tial
d
ata
b
r
ea
ch
es
an
d
f
in
an
cial
lo
s
s
es.
B
y
u
tili
zin
g
ad
v
an
ce
d
ML
tech
n
iq
u
es,
th
e
Om
L
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r
ep
r
esen
ts
a
s
ig
n
if
ican
t
s
tep
f
o
r
war
d
in
en
h
an
ci
n
g
cy
b
er
s
ec
u
r
ity
d
e
f
en
s
es
ag
a
in
s
t
p
h
is
h
in
g
attac
k
s
.
Fu
tu
r
e
en
h
a
n
ce
m
en
ts
to
Om
L
A
will
f
o
cu
s
o
n
in
teg
r
atin
g
d
e
ep
lear
n
in
g
f
o
r
im
p
r
o
v
ed
ac
c
u
r
ac
y
,
ex
p
an
d
i
n
g
t
h
e
d
ataset
f
o
r
a
b
r
o
ad
e
r
th
r
ea
t
a
n
aly
s
is
.
I
n
ad
d
itio
n
,
th
e
p
r
o
p
o
s
ed
wo
r
k
m
a
k
es
u
s
e
o
f
th
ir
d
-
p
ar
ty
s
er
v
ices
wh
ic
h
is
tim
e
co
n
s
u
m
in
g
.
Av
o
id
i
n
g
t
h
ese
in
f
o
r
m
atio
n
r
esu
lts
in
b
etter
r
ed
u
ce
d
an
d
r
esp
o
n
s
e
tim
e
f
o
r
r
eso
u
r
ce
co
n
s
tr
ain
ed
d
e
v
ices.
C
o
llab
o
r
atio
n
s
with
cy
b
er
s
ec
u
r
ity
e
x
p
er
ts
will
en
s
u
r
e
Om
L
A
r
em
ain
s
cu
ttin
g
-
ed
g
e
,
p
r
o
v
id
i
n
g
a
s
tr
o
n
g
er
d
ef
en
s
e
a
g
ain
s
t p
h
is
h
in
g
attac
k
s
.
RE
F
E
R
E
NC
E
S
[
1
]
S
.
A
si
r
i
,
Y
.
X
i
a
o
,
S
.
A
l
z
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5
.
[
1
6
]
B
.
G
o
g
o
i
,
T
.
A
h
m
e
d
a
n
d
A
.
D
u
t
t
a
,
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a
p
p
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a
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b
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m
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,
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d
i
a
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o
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e
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S
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N
)
,
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2
0
2
2
.
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8
6
2
9
0
9
.
[
1
7
]
A
.
N
.
N
j
o
y
a
,
V
.
L.
T
.
N
g
o
n
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g
,
F
.
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h
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k
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d
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a
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h
a
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me
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l
e
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g
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n
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EE
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c
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e
ss,
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l
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p
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C
C
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.
2
0
2
3
.
3
3
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7
6
9
2
.
[
1
8
]
A
.
B
a
s
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t
,
M
.
Z
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f
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r
,
A
.
R
.
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v
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d
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.
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i
l
,
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n
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b
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k
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E
EE
2
3
r
d
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n
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t
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e
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N
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p
p
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-
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2
0
2
0
.
9
3
1
8
2
1
0
.
[
1
9
]
A
.
N
.
S
.
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h
a
r
a
n
,
Y
.
-
H
.
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h
e
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a
n
d
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.
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h
e
n
,
“
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h
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g
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t
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m
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w
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t
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R
L
a
n
a
l
y
s
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s
,
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0
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l
d
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C
)
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d
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n
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a
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p
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8
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I
C
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0
3
6
.
2
0
2
2
.
9
8
4
8
8
9
5
.
[
2
0
]
R
.
R
a
j
a
n
d
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.
S
.
K
a
n
g
,
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p
a
m
a
n
d
n
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n
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sp
a
m
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R
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t
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t
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m
a
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l
e
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g
a
p
p
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o
a
c
h
,
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0
2
2
3
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d
I
n
t
e
r
n
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t
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l
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n
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e
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c
h
n
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g
y
(
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N
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ET)
,
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e
l
g
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u
m,
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n
d
i
a
,
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p
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4
5
3
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.
2
0
2
2
.
9
8
2
5
1
9
7
.
[
2
1
]
M
.
A
b
u
t
a
h
a
,
M
.
A
b
a
b
n
e
h
,
K
.
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m
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A
.
-
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.
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a
d
d
a
r
,
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d
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t
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b
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se
d
o
n
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R
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l
e
x
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c
a
l
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n
a
l
y
si
s,”
2
0
2
1
1
2
t
h
I
n
t
e
r
n
a
t
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l
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m
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c
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t
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n
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y
st
e
m
s
(
I
C
I
C
S
)
,
V
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l
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n
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a
,
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p
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n
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p
p
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C
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S
5
2
4
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7
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2
0
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.
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4
6
4
5
3
9
.
[
2
2
]
S
.
G
h
a
r
e
e
b
,
M
.
M
a
h
y
o
u
b
a
n
d
J.
M
u
st
a
f
i
n
a
,
“
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n
a
l
y
si
s
o
f
f
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n
u
s
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g
m
a
c
h
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n
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l
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r
n
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n
g
,
”
2
0
2
3
1
5
t
h
I
n
t
e
r
n
a
t
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o
n
a
l
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v
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m
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t
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g
(
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,
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8
2
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9
9
6
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7
.
[
2
3
]
X
.
Li
u
a
n
d
J.
F
u
,
“
S
P
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a
l
k
:
s
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m
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d
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t
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c
t
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n
,
”
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n
I
EE
E
Ac
c
e
ss
,
v
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l
.
8
,
p
p
.
8
7
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5
,
2
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d
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:
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0
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1
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9
/
A
C
C
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.
2
0
2
0
.
2
9
9
2
3
8
1
.
[
2
4
]
R. R.
Ro
ut
,
G
.
L
i
ng
am
a
n
d
D
.
V
.
L
. N
.
S
om
a
y
aj
ul
u, “D
et
ec
ti
o
n
of
m
al
ic
io
us
s
oci
al
b
ot
s
us
in
g le
ar
ni
n
g
a
u
to
m
at
a
w
it
h
U
RL
f
ea
tu
r
e
s
i
n
Tw
i
t
t
e
r
n
e
t
w
o
r
k
,
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i
n
I
EEE
T
r
a
n
s
a
c
t
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s
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n
C
o
m
p
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t
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y
st
e
m
s,
v
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l
.
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,
n
o
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4
,
p
p
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1
0
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,
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u
g
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2
0
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9
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C
S
S
.
2
0
2
0
.
2
9
9
2
2
2
3
.
[
2
5
]
A
n
t
i
-
p
h
i
s
h
i
n
g
w
o
r
k
i
n
g
g
r
o
u
p
(
A
P
W
G
)
r
e
p
o
r
t
o
n
p
h
i
s
h
i
n
g
a
c
t
i
v
i
t
y
t
r
e
n
d
s.
A
v
a
i
l
a
b
l
e
a
t
h
t
t
p
s:
/
/
a
p
w
g
.
o
r
g
/
t
r
e
n
d
sr
e
p
o
r
t
s
/
a
c
c
e
ss
e
d
o
n
07
-
J
a
n
-
2
0
2
4
.
B
I
O
G
RAP
H
I
E
S O
F
AUTH
O
RS
Na
n
d
e
e
sha
H
a
ll
i
m
y
so
r
e
De
v
a
r
a
j
p
re
se
n
tl
y
wo
rk
in
g
a
s
a
ss
istan
t
p
r
o
fe
ss
o
r
i
n
De
p
t
.
Of
Co
m
p
u
ter
S
c
ien
c
e
a
n
d
En
g
in
e
e
rin
g
,
JSS
S
c
ien
c
e
a
n
d
Tec
h
n
o
lo
g
y
U
n
iv
e
rsit
y
,
M
y
su
ru
,
Ka
rn
a
tak
a
,
In
d
ia.
He
re
c
e
iv
e
d
M
a
ste
r
o
f
tec
h
n
o
lo
g
y
fr
o
m
S
ri
Ja
y
a
c
h
a
m
a
ra
jen
d
ra
Co
ll
e
g
e
o
f
En
g
i
n
e
e
rin
g
.
C
u
rre
n
tl
y
,
h
e
is
p
u
rsu
in
g
P
h
.
D
.
in
c
y
b
e
r
se
c
u
rit
y
JSS
S
c
ien
c
e
a
n
d
Tec
h
n
o
lo
g
y
Un
iv
e
rsity
,
M
y
s
u
ru
.
His
g
e
n
e
ra
l
re
se
a
rc
h
in
tere
st i
s in
th
e
a
re
a
o
f
in
fo
rm
a
ti
o
n
a
n
d
c
y
b
e
r
s
e
c
u
rit
y
,
URL
p
h
ish
in
g
d
e
tec
ti
o
n
,
we
b
s
e
c
u
rit
y
,
m
o
b
il
e
se
c
u
rit
y
,
o
n
l
in
e
so
c
ial
n
e
two
rk
,
a
n
d
m
a
c
h
in
e
lea
rn
in
g
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
h
d
n
a
n
d
e
e
sh
@jss
stu
n
iv
.
in
.
Pra
sa
n
n
a
B
a
n
ti
g
a
n
a
h
a
ll
i
Th
i
m
a
p
p
a
re
c
e
iv
e
d
P
h
.
D.
d
e
g
re
e
fro
m
Visv
e
sv
a
ra
y
a
Tec
h
n
o
l
o
g
ica
l
U
n
iv
e
rsit
y
,
Ka
rn
a
t
a
k
a
,
In
d
ia
i
n
t
h
e
a
re
a
o
f
Cl
o
u
d
S
e
c
u
rit
y
.
He
h
a
s p
u
b
li
sh
e
d
m
o
r
e
th
a
n
6
0
re
se
a
rc
h
a
rti
c
les
in
In
tern
a
ti
o
n
a
l
Jo
u
rn
a
ls
a
n
d
Co
n
fe
re
n
c
e
s
o
f
h
ig
h
re
p
u
te
in
c
lu
d
i
n
g
IEE
E,
El
se
v
ier,
a
n
d
S
p
ri
n
g
e
r
.
H
e
is
se
rv
in
g
a
s
re
v
iew
e
r
o
f
El
se
v
ier,
IEE
E
a
n
d
m
a
n
y
re
p
u
te
d
Jo
u
rn
a
ls.
Also
,
h
e
is
a
l
ifetime
m
e
m
b
e
r
o
f
Co
m
p
u
ter
S
o
c
iety
o
f
In
d
ia
(CS
I).
At
p
re
se
n
t,
h
e
is
wo
rk
i
n
g
a
s
As
so
c
iate
P
r
o
fe
ss
o
r
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