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id
in
g
a
th
o
r
o
u
g
h
ev
alu
atio
n
o
f
th
e
RF
alg
o
r
ith
m
in
th
e
co
n
tex
t
o
f
An
d
r
o
id
m
alwa
r
e
d
etec
tio
n
,
h
ig
h
lig
h
tin
g
its
s
tr
en
g
th
s
an
d
p
o
ten
tial lim
itatio
n
s
.
W
e
will
b
eg
in
b
y
elu
cid
atin
g
th
e
wo
r
k
i
n
g
p
r
in
ci
p
les
o
f
th
e
R
F
alg
o
r
ith
m
a
n
d
w
h
y
it
is
p
ar
ticu
lar
ly
s
u
ited
f
o
r
m
alwa
r
e
d
etec
tio
n
.
Fo
llo
win
g
th
is
,
we
wil
l
p
r
ese
n
t
an
ex
ten
s
iv
e
An
d
r
o
id
m
al
war
e
d
ataset
u
s
ed
in
o
u
r
a
n
aly
s
is
.
Su
b
s
eq
u
e
n
t
s
ec
tio
n
s
will
d
etail
o
u
r
m
eth
o
d
o
l
o
g
y
f
o
r
tr
ain
in
g
a
n
d
test
in
g
t
h
e
alg
o
r
ith
m
,
alo
n
g
with
a
co
m
p
ar
ativ
e
an
aly
s
is
o
f
its
p
er
f
o
r
m
a
n
ce
a
g
ain
s
t
o
t
h
er
s
tate
-
of
-
t
h
e
-
ar
t
alg
o
r
ith
m
s
u
s
in
g
k
ey
m
etr
ics
s
u
ch
as
ac
cu
r
ac
y
,
r
ec
all
(
s
en
s
i
tiv
ity
)
,
an
d
th
e
F
-
s
co
r
e.
T
h
r
o
u
g
h
th
is
s
tr
u
ctu
r
ed
ap
p
r
o
ac
h
,
o
u
r
r
esear
ch
will
n
o
t
o
n
ly
d
em
o
n
s
tr
ate
t
h
e
ef
f
icac
y
o
f
th
e
RF
alg
o
r
ith
m
i
n
d
e
tectin
g
An
d
r
o
id
m
alwa
r
e
b
u
t
also
estab
lis
h
its
r
elev
an
ce
an
d
s
u
p
er
io
r
ity
i
n
th
is
d
o
m
ain
,
p
r
o
v
id
in
g
v
al
u
ab
le
in
s
ig
h
ts
f
o
r
f
u
tu
r
e
r
es
ea
r
ch
an
d
p
r
ac
tical
im
p
lem
en
tatio
n
s
.
2.
M
E
T
H
O
D
T
h
e
RF
alg
o
r
ith
m
is
o
n
e
o
f
th
e
m
o
s
t
p
o
p
u
lar
an
d
ef
f
e
ctiv
e
m
ac
h
in
e
lear
n
i
n
g
tec
h
n
iq
u
es
f
o
r
class
if
icatio
n
an
d
r
eg
r
ess
io
n
[
13
]
,
[
1
4
]
.
T
h
is
alg
o
r
ith
m
r
elies
o
n
th
e
u
s
e
o
f
a
s
et
o
f
DT
,
wh
er
e
ea
ch
tr
ee
is
r
an
d
o
m
l
y
co
n
s
tr
u
cted
f
r
o
m
a
s
u
b
s
et
o
f
th
e
tr
ain
in
g
d
ata
[
1
5
]
.
B
y
co
m
b
in
in
g
th
e
p
r
ed
ictio
n
s
o
f
m
u
ltip
le
DT
,
th
e
RF
r
ed
u
ce
s
o
v
er
lear
n
in
g
a
n
d
im
p
r
o
v
es th
e
r
o
b
u
s
tn
ess
o
f
th
e
o
v
er
all
m
o
d
el
[
1
6
]
.
2
.
1
.
Dec
is
io
n t
re
es
T
h
e
ess
en
ce
o
f
th
e
RF
alg
o
r
it
h
m
s
tem
s
f
r
o
m
DT
,
n
ec
ess
itatin
g
a
c
o
m
p
r
e
h
en
s
iv
e
u
n
d
e
r
s
tan
d
in
g
o
f
th
eir
m
ec
h
a
n
ics
f
o
r
a
th
o
r
o
u
g
h
co
m
p
r
eh
e
n
s
io
n
o
f
th
eir
o
p
e
r
atio
n
.
DT
s
a
r
e
e
x
ten
s
iv
ely
em
p
lo
y
ed
in
m
ac
h
in
e
lear
n
in
g
,
r
ely
in
g
o
n
h
eu
r
is
ti
cs
d
er
iv
ed
f
r
o
m
s
u
p
er
v
is
ed
lear
n
in
g
m
eth
o
d
o
lo
g
ies.
T
h
eir
ar
ch
itectu
r
e
is
h
ier
ar
ch
ical,
co
m
p
r
is
in
g
n
o
d
e
s
an
d
leav
es
in
ter
co
n
n
ec
ted
b
y
b
r
a
n
ch
es.
Gr
ap
h
ically
,
th
e
r
o
o
t
s
its
ato
p
th
e
h
ier
ar
ch
y
,
wh
ile
th
e
leav
es f
o
r
m
th
e
b
o
tto
m
lay
e
r
.
I
n
ter
n
al
n
o
d
es,
ter
m
ed
d
ec
is
io
n
n
o
d
es,
m
ay
en
co
m
p
ass
o
n
e
o
r
m
u
ltip
le
r
u
les,
r
ef
er
r
e
d
to
a
s
test
s
o
r
co
n
d
itio
n
s
[
1
7
]
.
T
h
e
p
o
ten
tial
v
alu
es
a
v
ar
ia
b
le
ca
n
tak
e
in
a
DT
ar
e
ter
m
ed
in
s
tan
ce
s
o
r
attr
ib
u
tes.
T
er
m
in
al
n
o
d
e
s
h
o
ld
th
e
p
r
ed
icted
class
,
also
k
n
o
wn
as
th
e
tar
g
et
v
ar
iab
le.
Af
ter
co
n
s
tr
u
ctio
n
,
a
DT
ca
n
b
e
r
ep
r
esen
ted
as
a
co
llectio
n
o
f
d
ec
is
io
n
r
u
les.
I
n
ca
s
es
wh
er
e
th
e
v
ar
iab
le
to
b
e
p
r
ed
icted
is
ca
teg
o
r
ical,
th
e
tr
ee
m
an
if
ests
as
a
class
if
icatio
n
tr
ee
.
C
o
n
v
er
s
ely
,
if
th
e
v
ar
iab
le
is
q
u
an
titativ
e,
it
as
s
u
m
es
th
e
s
h
ap
e
o
f
a
r
eg
r
ess
io
n
tr
ee
.
Fig
u
r
e
1
illu
s
tr
ates th
e
lay
o
u
t o
f
a
DT
[
1
8
]
.
Fig
u
r
e
1
.
T
h
e
s
tr
u
ctu
r
e
o
f
a
DT
Ma
k
in
g
p
r
e
d
ictio
n
s
u
s
in
g
DT
in
v
o
lv
es
a
s
y
s
tem
atic
tr
av
er
s
al
o
f
th
e
tr
ee
s
tr
u
ctu
r
e
b
a
s
ed
o
n
th
e
attr
ib
u
tes
o
f
th
e
in
p
u
t
d
ata.
B
eg
in
n
in
g
at
th
e
r
o
o
t
n
o
d
e,
t
h
e
alg
o
r
ith
m
ev
alu
ates
attr
ib
u
tes
at
ea
ch
in
ter
n
al
n
o
d
e,
s
elec
tin
g
th
e
a
p
p
r
o
p
r
iat
e
b
r
an
ch
b
ased
o
n
th
e
attr
ib
u
t
e
’
s
v
alu
e
in
th
e
in
p
u
t
d
ata.
T
h
is
p
r
o
ce
s
s
co
n
tin
u
es
r
ec
u
r
s
iv
ely
u
n
til
a
ter
m
in
al
n
o
d
e
is
r
ea
ch
e
d
.
At
th
e
ter
m
in
al
n
o
d
e
,
th
e
p
r
e
d
ictio
n
is
m
a
d
e
b
y
d
ete
r
m
in
in
g
th
e
m
ajo
r
ity
class
f
o
r
class
if
icatio
n
task
s
o
r
th
e
av
er
ag
e
v
alu
e
f
o
r
r
eg
r
ess
io
n
task
s
,
b
ased
o
n
th
e
tr
ain
in
g
in
s
tan
ce
s
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
6
,
No
.
3
,
Dec
em
b
er
20
24
:
1
8
76
-
1
8
83
1878
ass
o
ciate
d
with
th
at
n
o
d
e.
T
h
is
iter
ativ
e
p
r
o
ce
s
s
en
s
u
r
es
th
at
ea
ch
i
n
p
u
t
d
ata
in
s
tan
ce
is
g
u
id
ed
th
r
o
u
g
h
th
e
DT
,
u
ltima
tely
r
esu
ltin
g
in
a
p
r
ed
ictio
n
b
ased
o
n
th
e
lear
n
ed
p
atter
n
s
with
in
th
e
d
ata
[
1
9
]
.
DT
alg
o
r
ith
m
s
lik
e
I
ter
ativ
e
Dich
o
to
m
is
er
3
(
I
D3
)
,
C
4
.
5
,
an
d
class
if
icatio
n
an
d
r
eg
r
e
s
s
io
n
tr
ee
s
(
C
AR
T
)
ar
e
ess
en
tial
to
o
ls
in
m
ac
h
in
e
lear
n
in
g
,
wid
e
ly
u
s
ed
f
o
r
b
o
th
class
if
icatio
n
an
d
r
e
g
r
ess
io
n
.
T
h
e
I
D3
alg
o
r
ith
m
,
o
n
e
o
f
th
e
p
io
n
ee
r
s
in
t
h
is
ar
ea
,
b
u
ild
s
DT
f
o
r
class
if
icatio
n
b
y
s
elec
tin
g
attr
ib
u
tes
th
at
m
ax
im
ize
in
f
o
r
m
atio
n
g
ain
[
2
0
]
.
C
4
.
5
,
wh
ic
h
b
u
ild
s
u
p
o
n
I
D3
,
b
r
o
u
g
h
t
s
ev
e
r
al
im
p
r
o
v
em
en
ts
,
s
u
ch
as
th
e
ab
ilit
y
to
h
an
d
le
b
o
t
h
co
n
tin
u
o
u
s
an
d
d
is
cr
ete
d
ata,
m
an
ag
e
m
is
s
in
g
v
alu
es,
a
n
d
i
m
p
lem
en
t
p
r
u
n
in
g
tech
n
iq
u
es
to
a
v
o
id
o
v
er
f
itti
n
g
[
2
1
]
.
C
AR
T
is
an
o
th
er
p
r
o
m
in
en
t
alg
o
r
it
h
m
,
d
is
tin
ct
f
r
o
m
I
D3
an
d
C
4
.
5
in
th
at
it
co
n
s
tr
u
cts
b
in
ar
y
tr
ee
s
,
s
p
litt
in
g
th
e
d
ata
in
to
two
g
r
o
u
p
s
at
ea
ch
n
o
d
e
b
ased
o
n
a
n
attr
ib
u
te
’
s
v
alu
e
.
Fo
r
class
if
icatio
n
task
s
,
C
A
R
T
u
s
es
G
in
i
im
p
u
r
ity
to
ch
o
o
s
e
th
e
b
est
s
p
lits
,
wh
i
le
f
o
r
r
eg
r
ess
io
n
,
it
r
elies
o
n
m
ea
n
s
q
u
ar
ed
er
r
o
r
[
2
2
]
.
Desp
ite
th
eir
u
n
iq
u
e
f
ea
tu
r
es,
th
ese
alg
o
r
ith
m
s
all
aim
to
r
ec
u
r
s
iv
ely
d
iv
id
e
th
e
f
ea
t
u
r
e
s
p
ac
e
to
g
en
er
ate
d
ec
is
io
n
r
u
les th
at
ef
f
ec
tiv
ely
p
r
e
d
ict
o
u
tco
m
es.
Desp
ite
th
eir
ef
f
ec
tiv
en
ess
,
DT
h
av
e
ce
r
tain
lim
itatio
n
s
th
at
p
r
o
m
p
ted
th
e
d
ev
elo
p
m
en
t
o
f
en
s
em
b
le
m
eth
o
d
s
lik
e
RF
s
.
On
e
k
e
y
li
m
itatio
n
o
f
DT
is
th
eir
te
n
d
en
cy
to
o
v
e
r
f
it
th
e
tr
ain
in
g
d
ata,
esp
ec
ially
wh
en
t
h
e
tr
ee
s
b
ec
o
m
e
d
ee
p
an
d
c
o
m
p
l
ex
.
Ov
e
r
f
itti
n
g
o
cc
u
r
s
wh
en
t
h
e
m
o
d
el
ca
p
tu
r
es
n
o
is
e
o
r
ir
r
elev
an
t
p
atter
n
s
in
th
e
d
ata,
lead
in
g
to
p
o
o
r
g
en
e
r
aliza
tio
n
o
f
u
n
s
ee
n
d
ata.
DT
s
ar
e
also
s
u
s
ce
p
tib
le
to
in
s
tab
i
lity
,
m
ea
n
in
g
s
m
all
ch
an
g
es
in
th
e
tr
ain
in
g
d
ata
ca
n
r
esu
lt
in
s
ig
n
if
ican
tly
d
if
f
er
en
t
tr
ee
s
tr
u
ctu
r
es.
Ad
d
itio
n
ally
,
DT
s
m
ay
s
tr
u
g
g
le
with
ca
p
tu
r
in
g
c
o
m
p
lex
r
elatio
n
s
h
ip
s
i
n
th
e
d
ata,
p
ar
ticu
lar
ly
wh
e
n
f
ea
t
u
r
es
i
n
ter
ac
t
n
o
n
lin
ea
r
ly
.
T
o
m
itig
ate
th
ese
is
s
u
es,
RF
wer
e
in
tr
o
d
u
ce
d
as
a
n
en
s
em
b
le
lear
n
in
g
tech
n
iq
u
e.
B
y
a
g
g
r
eg
atin
g
m
u
ltip
le
DT
tr
ain
ed
o
n
r
an
d
o
m
s
u
b
s
ets
o
f
th
e
d
ata
a
n
d
f
ea
tu
r
es,
RF
ca
n
r
ed
u
ce
o
v
er
f
itti
n
g
,
im
p
r
o
v
e
s
tab
ilit
y
,
an
d
ca
p
tu
r
e
m
o
r
e
co
m
p
lex
p
atter
n
s
in
th
e
d
ata,
th
er
eb
y
en
h
a
n
cin
g
p
r
ed
ic
tiv
e
p
er
f
o
r
m
an
ce
[
2
3
]
.
2
.
2
.
Ra
nd
o
m
f
o
re
s
t
a
lg
o
rit
h
m
T
h
e
RF
is
a
s
u
p
er
v
is
ed
lear
n
in
g
alg
o
r
ith
m
u
s
ed
f
o
r
class
if
icatio
n
,
r
eg
r
ess
io
n
,
an
d
o
th
e
r
p
r
ed
ictiv
e
an
aly
s
is
task
s
.
I
ts
b
asic
p
r
in
ci
p
le
is
b
ased
o
n
th
e
id
ea
o
f
co
m
b
in
in
g
s
ev
er
al
i
n
d
iv
id
u
al
DT
to
f
o
r
m
a
r
o
b
u
s
t
an
d
d
iv
er
s
e
«
f
o
r
est
»
.
E
ac
h
DT
is
tr
ain
ed
o
n
a
r
an
d
o
m
s
am
p
le
o
f
tr
ain
in
g
d
ata
an
d
u
s
es
a
r
a
n
d
o
m
s
u
b
s
et
o
f
th
e
av
ailab
le
f
ea
tu
r
es
to
m
ak
e
d
ec
is
io
n
s
.
W
h
en
it
’
s
t
im
e
to
m
ak
e
p
r
ed
ictio
n
s
,
ea
ch
tr
ee
in
th
e
f
o
r
est
g
iv
es
its
o
wn
p
r
ed
ictio
n
,
an
d
th
e
f
i
n
al
p
r
e
d
i
ctio
n
is
d
eter
m
in
e
d
b
y
a
m
aj
o
r
ity
v
o
te
o
r
a
n
av
e
r
ag
e,
d
ep
e
n
d
in
g
o
n
wh
et
h
er
th
e
task
is
clas
s
if
icatio
n
o
r
r
eg
r
ess
io
n
[
24
].
T
h
e
RF
alg
o
r
ith
m
c
o
m
p
r
is
es
s
ev
er
al
s
tep
s
to
b
u
il
d
an
en
s
em
b
le
o
f
DT
f
o
r
r
o
b
u
s
t p
r
ed
ictio
n
s
.
Firstl
y
,
it
r
an
d
o
m
ly
s
elec
ts
s
u
b
s
ets
o
f
th
e
tr
ain
in
g
d
ata
th
r
o
u
g
h
a
p
r
o
ce
s
s
ca
lled
b
o
o
ts
tr
ap
p
in
g
,
cr
ea
tin
g
m
u
ltip
le
tr
ain
in
g
s
ets
f
o
r
ea
ch
tr
ee
.
Nex
t,
DT
ar
e
co
n
s
tr
u
cte
d
o
n
ea
c
h
b
o
o
ts
tr
ap
p
ed
d
ataset
u
s
in
g
a
s
u
b
s
et
o
f
r
an
d
o
m
l
y
s
elec
ted
f
ea
tu
r
es
at
ea
ch
n
o
d
e.
T
h
is
r
an
d
o
m
n
ess
en
s
u
r
es
d
iv
er
s
ity
am
o
n
g
th
e
i
n
d
iv
id
u
al
tr
ee
s
in
th
e
f
o
r
est.
Du
r
in
g
tr
ee
co
n
s
tr
u
ctio
n
,
th
e
alg
o
r
ith
m
r
ec
u
r
s
iv
ely
p
ar
titi
o
n
s
th
e
f
ea
tu
r
e
s
p
ac
e
b
ased
o
n
th
e
s
elec
ted
f
ea
tu
r
es,
o
p
tim
izin
g
s
p
lits
to
m
ax
im
ize
in
f
o
r
m
atio
n
g
ain
o
r
d
ec
r
ea
s
e
im
p
u
r
ity
.
On
ce
all
tr
ee
s
ar
e
g
r
o
wn
,
p
r
ed
ictio
n
s
ar
e
m
ad
e
b
y
ag
g
r
eg
atin
g
t
h
e
o
u
tp
u
ts
o
f
i
n
d
iv
id
u
al
tr
ee
s
.
Fo
r
class
if
icatio
n
task
s
,
th
e
m
o
s
t
f
r
eq
u
e
n
t
class
am
o
n
g
th
e
p
r
ed
ictio
n
s
is
ch
o
s
en
,
wh
ile
f
o
r
r
eg
r
ess
io
n
task
s
,
th
e
av
er
ag
e
o
f
th
e
p
r
ed
icted
v
alu
es
is
co
m
p
u
ted
.
T
h
e
f
in
al
p
r
e
d
ictio
n
r
ef
lects
th
e
c
o
llectiv
e
d
ec
is
io
n
o
f
th
e
e
n
s
em
b
le,
p
r
o
v
i
d
in
g
r
o
b
u
s
t
an
d
ac
c
u
r
ate
p
r
ed
ictio
n
s
wh
ile
m
itig
atin
g
th
e
r
is
k
o
f
o
v
er
f
itti
n
g
in
h
er
e
n
t
in
in
d
iv
id
u
al
DT
s
[
2
5
]
.
Fig
u
r
e
2
illu
s
tr
ates
th
e
wo
r
k
in
g
s
o
f
th
e
RF
alg
o
r
ith
m
.
Fig
u
r
e
2
.
RF
alg
o
r
ith
m
f
u
n
ctio
n
n
in
g
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
d
r
o
id
ma
lw
a
r
e
d
etec
tio
n
u
s
in
g
th
e
r
a
n
d
o
m
fo
r
est a
lg
o
r
ith
m
(
A
n
a
s
E
l A
tta
o
u
i
)
1879
2
.
3
.
Dif
f
er
ent
us
es o
f
t
he
ra
nd
o
m
f
o
re
s
t
a
lg
o
rit
hm
Nu
m
er
o
u
s
r
esear
ch
en
d
ea
v
o
r
s
h
av
e
u
n
d
er
s
co
r
ed
th
e
s
u
p
er
io
r
ef
f
icac
y
o
f
th
e
RF
alg
o
r
ith
m
,
p
ar
ticu
lar
ly
ev
id
en
t
in
its
ap
p
licatio
n
f
o
r
r
o
ad
tr
af
f
ic
ac
cid
e
n
t
(
R
T
A)
p
r
ed
ictio
n
,
as
d
em
o
n
s
tr
ated
in
a
s
tu
d
y
r
ef
er
en
ce
d
as
[
2
6
]
.
I
n
r
eg
io
n
s
ch
ar
ac
ter
ized
b
y
h
ig
h
i
n
cid
en
ce
r
ates
o
f
R
T
As,
s
u
ch
as
lo
w
-
an
d
m
id
d
le
-
in
co
m
e
co
u
n
tr
ies,
th
e
ac
cu
r
a
te
f
o
r
ec
asti
n
g
o
f
th
ese
ev
en
t
s
h
o
ld
s
p
ar
am
o
u
n
t
im
p
o
r
tan
ce
.
C
o
m
p
ar
in
g
th
e
p
er
f
o
r
m
an
ce
o
f
two
p
r
ev
alen
t
m
o
d
elin
g
tec
h
n
iq
u
es,
R
F
an
d
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
i
n
e
(
S
VM
)
,
u
tili
zin
g
d
ata
s
o
u
r
ce
d
f
r
o
m
law
en
f
o
r
ce
m
en
t
ag
en
cies,
th
e
s
tu
d
y
r
ev
ea
led
th
e
RF
alg
o
r
ith
m
’
s
u
n
p
ar
alle
led
p
er
f
o
r
m
a
n
ce
in
s
h
o
r
t
-
ter
m
R
T
A
p
r
ed
ictio
n
.
W
ith
a
co
ef
f
icien
t
o
f
d
eter
m
i
n
atio
n
(
R
2
)
r
ea
c
h
in
g
a
n
im
p
r
ess
iv
e
0
.
9
1
,
th
e
R
F
m
o
d
el
s
ig
n
if
ican
tly
o
u
ts
h
o
n
e
t
h
e
SVM
m
o
d
el,
wh
ich
ac
h
iev
ed
an
R
2
v
alu
e
o
f
0
.
8
6
.
T
h
is
d
is
p
ar
ity
h
ig
h
lig
h
ts
th
e
RF
r
o
b
u
s
tn
ess
in
r
ea
l
-
tim
e
f
o
r
ec
asti
n
g
,
o
f
f
er
i
n
g
in
v
alu
ab
le
in
s
ig
h
ts
f
o
r
tr
af
f
ic
m
an
a
g
em
en
t
an
d
p
u
b
lic
s
af
ety
in
itiativ
es a
im
ed
at
m
itig
atin
g
th
e
im
p
ac
t o
f
r
o
ad
ac
ci
d
en
ts
.
Dem
id
o
v
a
et
a
l.
[
2
7
]
p
r
es
en
ts
an
i
n
n
o
v
ativ
e
a
p
p
r
o
ac
h
aim
ed
at
r
ef
in
in
g
t
h
e
cl
ass
if
icatio
n
p
er
f
o
r
m
an
ce
o
f
t
h
e
SVM
b
y
in
co
r
p
o
r
atin
g
it
with
th
e
R
F
class
if
ier
.
T
h
is
n
o
v
el
h
y
b
r
i
d
izatio
n
s
tr
ateg
y
f
o
c
u
s
es
o
n
en
h
a
n
cin
g
SVM’
s
class
if
i
ca
tio
n
d
ec
is
io
n
s
,
p
ar
ticu
lar
ly
f
o
r
o
b
jects
s
itu
ated
in
s
p
ec
if
ic
s
u
b
ar
ea
s
n
ea
r
th
e
class
-
s
ep
ar
atin
g
h
y
p
er
p
lan
e,
en
co
m
p
ass
in
g
b
o
th
ac
cu
r
atel
y
an
d
in
ac
cu
r
ately
class
if
ied
en
titi
es.
No
tab
ly
,
th
e
r
esear
ch
s
u
g
g
ests
th
at
em
p
lo
y
in
g
th
is
h
y
b
r
id
class
if
icatio
n
ap
p
r
o
ac
h
co
u
l
d
ex
t
en
d
th
e
im
p
r
o
v
ed
class
if
icatio
n
q
u
ality
to
n
ew
o
b
jects
b
ey
o
n
d
th
e
i
n
itial
d
ataset.
Fu
r
th
er
m
o
r
e,
c
o
m
p
ar
ativ
e
an
aly
s
es
u
n
d
er
s
co
r
e
th
e
ef
f
ec
tiv
en
ess
o
f
in
teg
r
ati
n
g
au
x
iliar
y
class
if
ier
s
with
SVM
to
au
g
m
en
t
class
if
icati
o
n
o
u
tco
m
es.
T
h
is
f
in
d
in
g
n
o
t
o
n
l
y
u
n
d
er
s
co
r
es
th
e
ad
a
p
tab
ilit
y
o
f
RF
wh
en
in
teg
r
ated
with
o
th
er
m
ac
h
in
e
l
ea
r
n
in
g
alg
o
r
ith
m
s
b
u
t
also
em
p
h
asizes
its
ca
p
ac
ity
to
elev
ate
th
eir
ac
cu
r
ac
y
an
d
ef
f
icien
cy
,
th
er
e
b
y
en
h
an
cin
g
o
v
er
all
class
if
icatio
n
p
er
f
o
r
m
an
ce
.
Su
p
s
er
m
p
o
l
et
a
l.
[
2
8
]
d
elv
es
in
to
th
e
ass
es
s
m
en
t
o
f
co
m
p
an
ies’
f
in
an
cial
p
er
f
o
r
m
an
ce
f
o
llo
win
g
th
eir
p
u
b
lic
lis
tin
g
o
n
th
e
s
to
ck
m
ar
k
et.
E
m
p
lo
y
in
g
a
class
-
b
ased
m
eth
o
d
o
lo
g
y
,
th
e
r
esear
ch
s
ee
k
s
to
g
au
g
e
th
e
in
f
lu
en
ce
o
f
two
cr
itical
f
ac
to
r
s
:
th
e
in
ter
n
al
ca
p
ab
ilit
ies
o
f
co
m
p
an
ies
b
ef
o
r
e
th
eir
in
itial
p
u
b
lic
o
f
f
er
in
g
(
I
PO)
an
d
th
e
ca
p
ital
r
aised
d
u
r
in
g
th
e
I
PO
p
r
o
ce
s
s
.
Utili
zin
g
a
b
len
d
o
f
s
tatis
tical
an
aly
s
is
,
p
ar
ticu
lar
ly
lo
g
is
tic
r
eg
r
ess
io
n
,
an
d
m
ac
h
in
e
lear
n
in
g
tech
n
iq
u
es,
n
o
t
ab
ly
th
e
RF
alg
o
r
ith
m
,
th
e
s
tu
d
y
en
d
ea
v
o
r
s
to
ac
cu
r
ately
p
r
e
d
ict
f
in
an
cial
p
er
f
o
r
m
an
ce
.
No
tewo
r
t
h
y
f
in
d
in
g
s
f
r
o
m
th
e
in
v
esti
g
atio
n
h
ig
h
lig
h
t
th
e
RF
alg
o
r
ith
m
’
s
s
u
p
er
io
r
p
er
f
o
r
m
a
n
ce
o
v
er
lo
g
is
tic
r
eg
r
ess
io
n
in
f
o
r
ec
asti
n
g
f
in
an
cial
o
u
tc
o
m
e
s
,
u
n
d
er
s
co
r
in
g
th
e
ef
f
ec
tiv
en
ess
o
f
m
ac
h
i
n
e
lear
n
in
g
m
eth
o
d
o
lo
g
ies in
au
g
m
e
n
tin
g
p
r
e
d
ictiv
e
p
r
ec
is
io
n
in
f
in
a
n
cial
an
aly
s
is
.
L
astl
y
,
Ho
ła
an
d
C
za
r
n
ec
k
i
[
2
9
]
d
elv
es
i
n
to
th
e
ap
p
licatio
n
o
f
m
ac
h
i
n
e
lear
n
i
n
g
al
g
o
r
ith
m
s
,
n
o
tab
ly
th
e
RF
alg
o
r
ith
m
an
d
s
u
p
p
o
r
t v
ec
to
r
m
ac
h
in
e,
f
o
r
th
e
n
o
n
-
d
estru
ctiv
e
ass
ess
m
en
t
o
f
m
o
is
t
u
r
e
co
n
ten
t
in
b
r
ick
walls
with
in
h
is
to
r
ic
s
tr
u
ctu
r
e
s
.
L
ev
er
ag
in
g
a
d
ataset
co
m
p
r
is
in
g
2
9
0
s
am
p
le
s
ets
an
d
7
p
r
ed
icto
r
v
ar
iab
les,
en
co
m
p
ass
in
g
f
ac
to
r
s
lik
e
co
n
s
tr
u
ctio
n
y
ea
r
,
m
o
is
tu
r
e
lev
els,
an
d
s
alt
co
n
ce
n
t
r
atio
n
,
t
h
e
r
esear
ch
u
n
v
eils
co
m
p
ellin
g
in
s
ig
h
ts
.
No
tab
ly
,
th
e
RF
alg
o
r
ith
m
em
er
g
es
as
t
h
e
f
r
o
n
tr
u
n
n
er
,
s
h
o
wca
s
in
g
s
u
p
er
io
r
p
er
f
o
r
m
an
c
e
m
etr
ics.
W
ith
a
co
ef
f
icien
t
o
f
d
eter
m
in
atio
n
(
R
2
)
r
ea
ch
in
g
an
im
p
r
ess
iv
e
0
.
9
6
8
an
d
m
in
im
al
v
alu
es
f
o
r
r
o
o
t
m
ea
n
s
q
u
a
r
e
er
r
o
r
(
R
MSE
)
,
m
ea
n
ab
s
o
lu
te
er
r
o
r
(
MA
E
)
,
an
d
m
ea
n
a
b
s
o
lu
te
p
e
r
ce
n
tag
e
er
r
o
r
(
MA
PE)
at
1
.
0
8
0
%,
0
.
7
9
1
,
an
d
1
4
.
2
8
%,
r
esp
ec
tiv
ely
,
th
e
RF
alg
o
r
ith
m
estab
lis
h
es
it
s
elf
as
th
e
o
p
tim
al
to
o
l
f
o
r
p
r
ec
is
e,
non
-
d
estru
ctiv
e
ev
alu
atio
n
o
f
m
ass
m
o
is
tu
r
e
co
n
ten
t
i
n
h
is
t
o
r
ic
b
r
ic
k
walls.
T
h
ese
f
in
d
in
g
s
n
o
t
o
n
ly
b
o
ls
ter
th
e
ef
f
icac
y
o
f
m
ac
h
i
n
e
lear
n
i
n
g
in
h
e
r
itag
e
p
r
eser
v
atio
n
b
u
t
also
u
n
d
er
s
co
r
e
t
h
e
s
ig
n
if
ica
n
ce
o
f
d
ata
-
d
r
iv
e
n
ap
p
r
o
ac
h
es in
ar
ch
itectu
r
al
c
o
n
s
er
v
atio
n
ef
f
o
r
ts
.
T
h
e
s
tu
d
ies
r
ef
er
en
ce
d
a
b
o
v
e
co
llectiv
ely
u
n
d
er
s
co
r
e
th
e
v
er
s
atility
an
d
ef
f
ec
tiv
en
ess
o
f
th
e
RF
alg
o
r
ith
m
ac
r
o
s
s
d
iv
er
s
e
f
ield
s
o
f
ap
p
licatio
n
.
T
h
eir
f
in
d
in
g
s
co
n
s
is
ten
tly
h
ig
h
lig
h
t
th
e
alg
o
r
ith
m
’
s
ab
ilit
y
to
d
eliv
er
s
u
p
er
io
r
r
esu
lts
co
m
p
a
r
ed
to
alter
n
ativ
e
m
eth
o
d
s
.
T
h
is
co
m
p
ellin
g
ev
id
en
ce
s
er
v
es a
s
th
e
b
asis
f
o
r
o
u
r
d
ec
is
io
n
to
in
co
r
p
o
r
ate
th
e
RF
alg
o
r
ith
m
in
to
o
u
r
s
tu
d
y
o
n
m
alwa
r
e
d
etec
tio
n
.
B
y
lev
er
a
g
in
g
its
p
r
o
v
e
n
tr
ac
k
r
ec
o
r
d
o
f
p
e
r
f
o
r
m
an
ce
an
d
a
d
ap
tab
ilit
y
,
we
aim
to
e
n
h
an
ce
th
e
a
cc
u
r
ac
y
an
d
e
f
f
icien
cy
o
f
o
u
r
m
alwa
r
e
d
etec
tio
n
s
y
s
tem
.
T
h
is
s
tr
ateg
i
c
ch
o
ice
alig
n
s
with
th
e
b
r
o
ad
er
tr
en
d
in
th
e
r
esear
ch
co
m
m
u
n
ity
,
af
f
ir
m
in
g
t
h
e
RF
s
ta
tu
s
as a
p
r
ef
er
r
ed
s
o
lu
ti
o
n
f
o
r
a
wid
e
r
a
n
g
e
o
f
m
ac
h
in
e
lear
n
in
g
task
s
.
2
.
4
.
Da
t
a
us
ed
T
h
e
d
ataset
u
s
ed
in
th
is
s
tu
d
y
co
n
s
is
ts
o
f
f
ea
tu
r
e
v
ec
to
r
s
with
2
1
5
attr
ib
u
tes
ex
tr
ac
t
ed
f
r
o
m
a
co
m
p
r
eh
e
n
s
iv
e
s
et
o
f
1
5
,
0
3
6
a
p
p
licatio
n
s
.
Am
o
n
g
th
ese,
5
,
5
6
0
ap
p
licatio
n
s
,
s
o
u
r
ce
d
f
r
o
m
th
e
Dr
eb
in
p
r
o
ject,
wer
e
id
en
tifie
d
as
m
alwa
r
e,
wh
ile
th
e
r
em
ain
in
g
9
,
4
7
6
we
r
e
class
if
ied
as
b
en
ig
n
.
T
h
is
d
ataset
s
er
v
ed
as
th
e
f
o
u
n
d
atio
n
f
o
r
d
ev
el
o
p
in
g
an
d
ev
alu
atin
g
a
n
in
n
o
v
ativ
e
m
u
ltil
ev
el
class
if
ier
f
u
s
io
n
s
tr
ateg
y
f
o
r
An
d
r
o
id
m
alwa
r
e
d
etec
tio
n
[
3
0
]
.
T
h
e
f
ea
tu
r
es
in
clu
d
e
API
ca
ll
s
ig
n
atu
r
es,
in
ten
ts
,
m
an
if
est
p
er
m
is
s
io
n
s
,
an
d
co
m
m
an
d
p
er
m
is
s
io
n
s
,
an
d
F
ig
u
r
e
3
s
h
o
ws th
e
co
u
n
t o
f
ea
c
h
f
ea
tu
r
e
t
y
p
e
in
t
h
e
d
ataset
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
6
,
No
.
3
,
Dec
em
b
er
20
24
:
1
8
76
-
1
8
83
1880
Fig
u
r
e
3
.
T
h
e
n
u
m
b
er
o
f
f
ea
t
u
r
es ty
p
e
in
th
e
d
ataset
2
.
5
.
M
ea
s
urm
ent
m
e
t
rics
Af
ter
p
r
ep
ar
in
g
th
e
d
ata
b
ef
o
r
eh
a
n
d
,
we
ad
v
an
ce
d
to
im
p
lem
en
t
o
u
r
alg
o
r
ith
m
o
n
a
d
ataset
r
ep
r
esen
tin
g
8
0
%
o
f
th
e
to
tal,
d
esig
n
ated
as
th
e
tr
ain
in
g
s
et
.
Su
b
s
eq
u
e
n
tly
,
to
g
au
g
e
th
e
e
f
f
ec
tiv
en
ess
o
f
o
u
r
m
o
d
el,
we
u
tili
ze
d
th
e
r
em
ai
n
in
g
2
0
%
as
a
test
s
et
f
o
r
m
ak
in
g
p
r
e
d
ictio
n
s
.
Fo
llo
win
g
th
is
,
we
co
m
p
u
ted
v
ar
io
u
s
m
etr
ics,
s
u
ch
as a
cc
u
r
ac
y
,
s
en
s
itiv
ity
,
an
d
F
-
s
co
r
e,
to
ev
alu
ate
th
e
e
f
f
icac
y
o
f
o
u
r
p
r
ed
ictio
n
s
.
Acc
u
r
ac
y
r
e
p
r
esen
ts
th
e
p
er
ce
n
tag
e
o
f
co
r
r
ec
tly
id
e
n
tifie
d
p
o
s
itiv
e
ca
s
es
am
o
n
g
all
p
r
ed
icted
p
o
s
itiv
e
ca
s
es,
r
ef
lectin
g
h
o
w
well
th
e
m
o
d
el
r
ec
o
g
n
izes
tr
u
e
p
o
s
itiv
es.
Sen
s
itiv
ity
,
al
s
o
k
n
o
w
n
as
r
ec
all,
m
ea
s
u
r
es
th
e
p
r
o
p
o
r
tio
n
o
f
ac
tu
al
tr
u
e
p
o
s
itiv
es
th
at
th
e
m
o
d
el
s
u
cc
ess
f
u
lly
p
r
ed
icts
,
in
d
i
ca
tin
g
th
e
m
o
d
el
’
s
ef
f
ec
tiv
en
ess
in
d
etec
tin
g
all
tr
u
e
p
o
s
itiv
es
in
th
e
d
ataset.
T
h
e
F
-
s
co
r
e,
o
n
th
e
o
th
er
h
a
n
d
,
m
er
g
es
ac
cu
r
ac
y
an
d
s
en
s
itiv
ity
in
t
o
a
s
in
g
le
m
etr
ic,
b
alan
ci
n
g
th
e
m
o
d
el
’
s
ab
ilit
y
t
o
r
e
d
u
ce
f
alse
p
o
s
itiv
es
wh
ile
ef
f
ec
tiv
ely
id
en
tify
in
g
t
r
u
e
p
o
s
itiv
es.
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
e
im
p
lem
en
tatio
n
o
f
th
e
RF
alg
o
r
ith
m
y
ield
ed
im
p
r
ess
iv
e
r
esu
lts
,
ev
id
en
t
f
r
o
m
th
e
p
er
f
o
r
m
a
n
ce
m
etr
ics
o
b
tain
ed
.
Ac
r
o
s
s
1
0
0
iter
atio
n
s
,
p
r
e
d
ictio
n
ac
c
u
r
ac
y
r
an
g
ed
f
r
o
m
a
co
m
m
e
n
d
ab
le
m
in
im
u
m
o
f
98.
4
7
%
to
a
r
em
ar
k
ab
le
m
ax
i
m
u
m
o
f
9
9
.
24
%,
with
a
n
av
er
ag
e
o
f
9
8
.
8
5
%,
as
d
ep
icted
in
F
ig
u
r
e
4
.
Similar
ly
,
th
e
s
en
s
itiv
ity
o
f
th
e
r
esu
lts
d
em
o
n
s
tr
ated
r
esil
ien
ce
,
with
a
r
an
g
e
f
r
o
m
a
m
in
im
u
m
o
f
9
8
.
3
7
%
to
a
m
ax
im
u
m
o
f
9
9
.
16
%,
an
d
an
av
e
r
ag
e
o
f
9
8
.
60
%,
as
illu
s
tr
ated
i
n
F
ig
u
r
e
5
.
Fu
r
th
er
m
o
r
e,
th
e
F
-
s
co
r
e
ex
h
ib
ited
co
n
s
is
ten
t
p
er
f
o
r
m
an
ce
,
r
an
g
i
n
g
f
r
o
m
a
m
in
im
u
m
o
f
9
8
.
37
%
to
a
m
a
x
im
u
m
o
f
9
9
.
16
%,
with
an
av
e
r
ag
e
o
f
98.
60
%,
as
s
h
o
wn
in
F
ig
u
r
e
6
.
T
h
ese
c
o
n
s
is
ten
t
an
d
r
o
b
u
s
t
p
er
f
o
r
m
an
ce
s
u
n
d
e
r
s
co
r
e
th
e
ef
f
icac
y
o
f
th
e
alg
o
r
ith
m
in
a
d
d
r
ess
in
g
th
e
p
r
o
b
lem
at
h
a
n
d
.
Fig
u
r
e
4
.
Acc
u
r
ac
y
in
1
0
0
iter
atio
n
s
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
d
r
o
id
ma
lw
a
r
e
d
etec
tio
n
u
s
in
g
th
e
r
a
n
d
o
m
fo
r
est a
lg
o
r
ith
m
(
A
n
a
s
E
l A
tta
o
u
i
)
1881
Fig
u
r
e
5
.
Sen
s
ib
ilit
y
(
r
ec
all)
i
n
1
0
0
iter
atio
n
s
Fig
u
r
e
6
.
F
-
s
co
r
e
i
n
1
0
0
iter
ati
o
n
s
C
o
m
p
ar
in
g
o
u
r
r
esu
lts
with
th
o
s
e
f
r
o
m
s
tu
d
ies
[
8
]
-
[
1
1
]
,
t
h
e
RF
alg
o
r
ith
m
co
n
s
is
ten
tly
d
em
o
n
s
tr
ates
s
u
p
er
io
r
p
er
f
o
r
m
a
n
ce
.
T
h
e
m
u
ltip
le
lin
ea
r
r
eg
r
ess
io
n
m
eth
o
d
s
in
[
8
]
ac
h
iev
ed
a
h
ig
h
ac
cu
r
ac
y
o
f
9
8
.
5
3
%,
y
et
th
is
was
s
lig
h
tly
lo
wer
th
a
n
th
e
av
er
a
g
e
ac
c
u
r
ac
y
o
f
th
e
RF
.
Similar
ly
,
th
e
MCADS
s
y
s
tem
in
[
9
]
,
with
its
in
n
o
v
ativ
e
two
-
la
y
er
s
tr
u
ctu
r
e,
ac
h
iev
ed
an
ac
c
u
r
ac
y
o
f
9
8
.
1
2
%,
wh
ic
h
is
s
till
b
elo
w
th
e
RF
av
er
ag
e.
T
h
e
f
r
am
ewo
r
k
p
r
esen
ted
i
n
[
1
0
]
ac
h
iev
ed
o
v
er
9
8
%
ac
c
u
r
ac
y
f
o
r
m
u
lticlas
s
d
etec
tio
n
,
b
u
t
its
ac
cu
r
ac
y
f
o
r
g
en
er
al
m
alwa
r
e
d
etec
tio
n
was
s
l
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r
eliab
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y
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
6
,
No
.
3
,
Dec
em
b
er
20
24
:
1
8
76
-
1
8
83
1882
4.
CO
NCLU
SI
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ly
ex
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ed
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p
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alg
o
r
ith
m
f
o
r
An
d
r
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d
m
alwa
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d
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ass
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o
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etailed
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tech
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ter
m
s
o
f
ac
cu
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ac
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h
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s
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p
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m
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ce
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alg
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s
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b
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ak
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it
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tify
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atin
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A
n
d
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id
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alwa
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e.
Acc
u
r
ate
m
alwa
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e
d
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tio
n
is
ess
en
tial
f
o
r
s
af
eg
u
ar
d
in
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s
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p
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ac
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d
s
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ity
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ly
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h
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d
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th
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b
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to
p
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is
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id
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alicio
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s
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is
cr
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in
p
r
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tectin
g
u
s
er
s
f
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m
p
o
ten
tial
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ar
m
.
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h
e
s
tu
d
y
h
ig
h
lig
h
ted
th
e
s
ig
n
if
ican
t
b
en
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its
o
f
em
p
lo
y
in
g
ad
v
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d
d
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tio
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alg
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s
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n
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u
te
to
en
h
a
n
ce
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it
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m
ea
s
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d
m
o
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e
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p
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tectio
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g
ain
s
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lv
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th
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t
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s
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tly
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l
v
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n
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f
m
alwa
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ch
ar
ac
ter
is
tics
,
th
r
ea
ts
ar
e
p
er
p
etu
ally
ch
an
g
in
g
.
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h
is
o
n
g
o
in
g
e
v
o
lu
tio
n
r
e
q
u
ir
es
m
alwa
r
e
d
etec
tio
n
s
y
s
tem
s
to
n
o
t
o
n
l
y
a
d
d
r
ess
cu
r
r
en
t
th
r
ea
ts
b
u
t
also
an
ticip
ate
an
d
ad
a
p
t
to
f
u
tu
r
e
ch
allen
g
es.
Ma
in
tain
in
g
ef
f
ec
tiv
en
ess
o
v
er
tim
e
h
in
g
es
o
n
th
e
ab
ilit
y
t
o
co
n
tin
u
o
u
s
ly
u
p
d
ate
an
d
r
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in
e
d
etec
tio
n
m
eth
o
d
o
lo
g
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s
.
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n
lig
h
t
o
f
th
ese
f
in
d
in
g
s
,
th
e
s
tu
d
y
em
p
h
asizes
th
e
im
p
o
r
tan
ce
o
f
co
n
tin
u
al
r
esear
ch
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d
d
ev
elo
p
m
en
t in
th
e
f
ield
o
f
m
alwa
r
e
d
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tio
n
.
I
t is e
s
s
en
tial to
in
v
est in
an
d
ad
v
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ce
d
etec
tio
n
tech
n
o
l
o
g
ies
to
en
s
u
r
e
th
e
y
r
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ai
n
ef
f
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e
in
i
d
en
tify
in
g
n
ew
an
d
em
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g
in
g
m
alwa
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e
v
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ian
ts
.
Ov
er
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th
is
s
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y
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ig
h
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h
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e
p
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o
tal
r
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h
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g
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in
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o
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n
to
s
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ah
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o
f
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o
lv
in
g
cy
b
er
th
r
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ts
.
RE
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NC
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[
1
]
D
.
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A
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M
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S
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