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co
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r
e,
its
p
er
s
is
ten
ce
m
ec
h
an
is
m
s
,
an
d
m
eth
o
d
s
o
f
s
p
r
ea
d
in
g
,
as
well
as
th
e
h
ar
m
it
ca
u
s
es
to
co
n
n
ec
ted
n
etwo
r
k
s
an
d
s
y
s
tem
s
[
6
]
.
T
h
is
n
ec
ess
itates
a
co
n
tr
o
lled
ex
ec
u
tio
n
e
n
v
ir
o
n
m
e
n
t
an
d
s
o
lid
s
u
b
ject
k
n
o
wled
g
e.
T
h
r
o
u
g
h
s
tatic
an
a
ly
s
is
tech
n
iq
u
es,
p
o
r
ta
b
le
ex
ec
u
tab
le
(
PE)
f
iles
ar
e
d
is
ass
em
b
led
f
u
lly
,
a
n
d
t
h
eir
h
ex
ad
ec
im
al
c
o
d
es
ar
e
e
x
am
i
n
ed
to
co
m
p
r
eh
en
d
th
e
b
eh
a
v
io
r
an
d
ef
f
ec
ts
f
o
r
th
e
m
al
war
e.
Pro
f
icien
cy
in
ass
em
b
ly
co
d
e,
al
o
n
g
with
a
co
m
p
r
eh
e
n
s
iv
e
u
n
d
er
s
tan
d
in
g
o
f
th
e
m
alwa
r
e
a
n
d
its
f
u
n
ctio
n
in
g
,
is
cr
u
cial
f
o
r
th
is
p
r
o
ce
s
s
,
wh
ich
also
r
eq
u
ir
es tim
e
an
d
m
em
o
r
y
r
eso
u
r
ce
s
.
Desp
ite
th
ese
tech
n
iq
u
es,
ef
f
ec
tiv
ely
ad
d
r
ess
in
g
n
ew
v
ir
u
s
es
is
b
ec
o
m
in
g
in
cr
e
asin
g
ly
ch
allen
g
i
n
g
[
7
]
.
E
x
is
ti
n
g
liter
atu
r
e
alr
ea
d
y
o
f
f
er
s
v
ar
io
u
s
ap
p
r
o
ac
h
es
f
o
r
m
alwa
r
e
id
en
tific
atio
n
an
d
cla
s
s
if
icatio
n
.
T
h
e
in
itial
s
tep
in
m
alwa
r
e
a
n
aly
s
is
in
v
o
lv
es
co
n
d
u
ctin
g
b
o
th
s
tatic
an
d
d
y
n
am
ic
an
aly
s
es.
Sta
tic
an
aly
s
is
ev
alu
ates
o
r
d
is
as
s
em
b
les
th
e
lo
g
ic
o
f
th
e
co
d
e
with
o
u
t
ex
ec
u
tin
g
it,
ex
tr
ac
tin
g
f
ea
tu
r
es
lik
e
ap
p
licatio
n
p
r
o
g
r
a
m
m
in
g
in
ter
f
ac
e
(
API
)
s
eq
u
en
ce
s
,
o
p
co
d
es,
s
y
s
tem
ca
lls
,
an
d
o
th
e
r
r
ele
v
an
t
i
n
f
o
r
m
atio
n
.
Dy
n
a
m
ic
an
aly
s
is
,
o
n
th
e
o
th
er
h
an
d
,
in
v
o
lv
es
ex
ec
u
tin
g
th
e
m
alicio
u
s
co
d
e
with
in
a
s
ec
u
r
e
en
v
ir
o
n
m
en
t
to
o
b
s
er
v
e
its
b
eh
av
io
r
.
W
h
ile
s
tatic
d
etec
tio
n
m
eth
o
d
s
r
el
y
o
n
s
ig
n
at
u
r
es
th
at
ar
e
n
o
t
u
n
iv
er
s
all
y
a
p
p
licab
le,
r
en
d
e
r
in
g
th
e
m
in
ef
f
ec
tiv
e
f
o
r
d
etec
tin
g
n
ew
th
r
ea
ts
,
d
y
n
am
ic
b
eh
a
v
io
r
-
b
ased
ap
p
r
o
ac
h
es
in
cr
ea
s
e
d
etec
tio
n
ac
cu
r
ac
y
at
th
e
co
s
t
o
f
s
ig
n
if
ican
t
o
v
er
h
ea
d
.
T
h
e
m
ain
p
u
r
p
o
s
e
o
f
th
is
r
esear
ch
wo
r
k
is
to
f
ig
u
r
e
o
u
t
th
e
m
alwa
r
es
th
at
g
et
n
ewly
in
tr
o
d
u
ce
d
i
n
th
e
s
y
s
tem
.
I
n
o
r
d
er
to
ac
h
ie
v
e
th
is
o
b
jectiv
e,
th
is
wo
r
k
m
ak
es
th
e
f
o
llo
win
g
k
ey
co
n
tr
ib
u
tio
n
s
:
−
I
n
tr
o
d
u
ctio
n
o
f
a
n
o
v
el
en
s
em
b
le
-
b
ased
ap
p
r
o
ac
h
th
at
co
m
b
in
es
th
e
s
tr
en
g
th
s
o
f
th
e
b
est
class
if
ier
s
an
d
clu
s
ter
in
g
tech
n
iq
u
es f
o
r
m
al
war
e
id
en
tific
atio
n
an
d
class
if
icatio
n
.
−
E
v
alu
atio
n
o
f
t
h
e
p
r
o
p
o
s
ed
ap
p
r
o
ac
h
’
s
b
eh
av
io
r
a
n
d
ef
f
icien
cy
f
o
r
h
a
n
d
lin
g
u
n
k
n
o
wn
m
al
war
e,
p
r
o
v
i
d
in
g
g
u
id
an
ce
f
o
r
u
s
in
g
th
e
p
r
o
p
o
s
ed
ap
p
r
o
ac
h
f
o
r
m
alwa
r
e
class
if
icatio
n
o
n
W
in
d
o
ws p
latf
o
r
m
s
.
Sig
n
if
ican
t
r
esear
ch
h
as
b
ee
n
co
n
d
u
cte
d
o
n
m
alwa
r
e
an
aly
s
is
an
d
d
etec
tio
n
u
s
in
g
s
tatic,
d
y
n
am
ic,
an
d
m
ac
h
in
e
lear
n
in
g
(
ML
)
tech
n
i
q
u
es
[
8
]
.
Naz
an
d
Sin
g
h
[
9
]
g
av
e
a
t
h
o
r
o
u
g
h
ex
p
lan
atio
n
o
f
h
o
w
ML
is
ap
p
lied
to
W
in
d
o
ws
m
alwa
r
e
d
etec
tio
n
.
Ma
lwar
e
d
etec
tio
n
an
d
cla
s
s
if
icatio
n
m
eth
o
d
s
ca
n
b
e
ca
teg
o
r
ized
in
to
f
iv
e
g
r
o
u
p
s
:
d
ee
p
lear
n
in
g
(
DL
)
,
m
o
d
el
v
er
if
icatio
n
,
s
ig
n
atu
r
e,
b
e
h
av
io
r
,
a
n
d
h
e
u
r
is
tics
m
eth
o
d
s
.
A
s
ig
n
atu
r
e
-
b
ased
ap
p
r
o
ac
h
f
o
r
m
alwa
r
e
id
e
n
tific
atio
n
.
Sig
n
at
u
r
e
-
b
ased
m
e
th
o
d
s
,
r
el
y
in
g
o
n
p
atter
n
-
m
a
tch
in
g
u
s
in
g
b
y
te
s
eq
u
en
ce
s
k
n
o
wn
as
s
ig
n
atu
r
es
,
ar
e
wid
ely
u
s
ed
f
o
r
m
a
lwar
e
d
etec
tio
n
.
Ho
wev
er
,
th
ese
m
et
h
o
d
s
ar
e
s
u
s
ce
p
tib
le
to
m
in
o
r
ch
an
g
es
in
m
alicio
u
s
co
d
e,
p
o
s
in
g
a
ch
allen
g
e
in
id
en
tify
in
g
m
o
d
if
ied
o
r
p
r
ev
io
u
s
ly
u
n
k
n
o
wn
m
alwa
r
e.
Ob
f
u
s
ca
tio
n
tech
n
o
l
o
g
ies
ca
n
ev
ad
e
s
ig
n
atu
r
e
-
b
as
ed
tech
n
i
q
u
es,
b
u
t
th
ey
r
eq
u
ir
e
p
r
io
r
k
n
o
wled
g
e
o
f
m
alwa
r
e
s
am
p
les
[
1
0
]
.
Dar
s
h
an
an
d
J
aid
h
ar
[
1
1
]
p
r
o
p
o
s
ed
a
h
y
b
r
id
s
tr
ateg
y
th
at
co
m
b
i
n
es
a
lin
ea
r
s
u
p
p
o
r
t
v
ec
to
r
class
if
icatio
n
alg
o
r
ith
m
with
th
e
s
tatic
an
d
d
y
n
am
ic
f
e
atu
r
es
o
f
PE
f
iles
in
o
r
d
er
to
p
r
ec
is
ely
id
en
tify
th
e
u
n
k
n
o
wn
v
ir
u
s
.
T
h
e
m
o
d
el
was
tr
ain
ed
o
n
a
litt
le
d
atase
t,
wh
ich
h
in
d
er
ed
its
ab
ili
ty
t
o
ac
h
iev
e
ex
ce
llen
t
ac
cu
r
ac
y
.
J
av
ee
d
et
a
l.
[
1
2
]
h
av
e
d
e
v
elo
p
ed
an
in
tr
u
s
io
n
d
etec
tio
n
s
y
s
tem
s
(
I
DS)
p
o
wer
ed
b
y
DL
th
a
t c
ar
r
y
th
e
laten
t
to
id
en
tif
y
in
tr
u
s
io
n
s
w
ith
im
p
r
o
v
ed
ac
c
u
r
ac
y
.
An
u
n
d
er
s
tan
d
ab
le
a
n
d
r
eliab
le
I
DS
f
o
r
i
n
d
u
s
tr
y
5
.
0
is
p
r
esen
ted
in
th
is
s
tu
d
y
.
T
o
im
p
r
o
v
e
th
e
in
tr
u
s
io
n
d
etec
tio
n
p
r
o
ce
d
u
r
e
in
I
n
d
u
s
tr
y
5
.
0
,
th
e
s
u
g
g
ested
I
DS
is
b
u
ilt
b
y
m
e
r
g
in
g
a
b
id
ir
ec
tio
n
al
-
g
a
ted
r
ec
u
r
r
en
t
u
n
it
(
B
i
-
GR
U)
,
b
id
ir
ec
tio
n
al
l
o
n
g
s
h
o
r
t
-
ter
m
m
em
o
r
y
n
etwo
r
k
s
(
B
iLST
M)
,
an
d
f
u
lly
c
o
n
n
ec
te
d
lay
er
s
.
T
h
en
,
th
ey
u
s
ed
th
e
s
h
ap
ley
a
d
d
itiv
e
e
x
p
lan
atio
n
s
(
SHAP)
m
ec
h
an
is
m
to
ev
alu
ate
an
d
co
m
p
r
eh
en
d
t
h
e
ch
ar
ac
ter
is
tics
th
at
m
o
s
t
s
ig
n
if
ican
tly
in
f
lu
en
ce
d
t
h
e
ch
o
ice
o
f
th
e
s
u
g
g
ested
cy
b
er
-
r
esil
ien
t
I
DS.
T
h
e
two
m
ain
ap
p
r
o
ac
h
es
th
at
wer
e
p
r
o
p
o
s
ed
:
u
s
in
g
t
h
e
s
ig
n
atu
r
e
a
n
d
th
e
h
eu
r
is
tic
r
u
le
d
is
co
v
er
ed
,
we
ca
n
ac
cu
r
ately
d
etec
t
k
n
o
wn
m
alwa
r
e.
Als
m
ad
i
an
d
Alq
u
d
ah
[
1
3
]
h
ig
h
lig
h
t
cu
r
r
en
t
m
eth
o
d
s
f
o
r
id
e
n
tify
in
g
a
n
d
e
v
alu
atin
g
m
alicio
u
s
p
r
o
g
r
am
m
es.
R
esear
ch
b
y
Kim
et
a
l.
[
1
4
]
,
th
e
b
eh
a
v
io
r
-
b
ased
ap
p
r
o
ac
h
wa
s
h
ig
h
lig
h
ted
,
u
tili
zin
g
d
y
n
am
i
c
an
aly
s
is
tech
n
iq
u
es
to
ex
tr
ac
t
b
e
h
av
io
r
al
asp
ec
ts
s
u
ch
as
in
s
tr
u
ctio
n
s
eq
u
en
ce
s
,
n
etwo
r
k
ac
tiv
ities
,
an
d
s
y
s
tem
ca
lls
.
L
ad
an
d
A
d
am
u
th
e
[
1
5
]
ca
teg
o
r
is
e
th
e
r
is
k
y
co
d
es
PE
f
iles
at
th
e
ea
r
lier
s
tatic
an
aly
s
is
p
h
ase
in
o
r
d
er
t
o
d
ec
id
e
wh
at
p
r
ev
e
n
tiv
e
s
tep
s
s
h
o
u
ld
b
e
im
p
lem
e
n
ted
i
n
later
p
h
as
es.
I
m
r
an
et
a
l.
[
1
6
]
p
r
o
p
o
s
ed
a
s
im
ilar
ity
-
b
ased
tech
n
iq
u
e
u
s
in
g
h
i
d
d
en
Ma
r
k
o
v
m
o
d
els
to
class
if
y
m
alwa
r
e
b
ased
o
n
API
ca
ll
p
atter
n
s
.
Ho
wev
er
,
d
y
n
am
ic
an
aly
s
is
m
ay
f
ac
e
lim
itatio
n
s
as
m
alwa
r
e
ca
n
ch
an
g
e
it
s
b
eh
av
io
r
wh
e
n
ex
ec
u
ted
i
n
v
ir
tu
al
s
ettin
g
s
.
Ma
n
e
et
a
l.
[
1
7
]
m
e
n
tio
n
ed
t
h
e
f
ea
tu
r
es
th
at
b
est
d
escr
ib
e
th
e
p
r
o
v
id
e
d
tr
ain
in
g
d
ata
ar
e
lear
n
ed
u
s
in
g
a
d
ee
p
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
A
n
o
ve
l e
n
s
emb
le
-
b
a
s
ed
a
p
p
r
o
a
ch
fo
r
Win
d
o
w
s
ma
lw
a
r
e
d
etec
tio
n
(
V
ika
s
V
erma
)
329
n
eu
r
al
n
etwo
r
k
(
DNN)
.
T
h
is
u
s
es
ex
ec
u
tab
le
f
iles
th
at
ar
e
p
o
r
tab
le
to
teac
h
th
e
DNN
th
e
f
ea
tu
r
es.
T
h
is
n
etwo
r
k
-
b
ased
s
o
lu
tio
n
s
,
th
u
s
,
h
as lo
w
f
alse p
o
s
itiv
e
r
ates a
n
d
is
ef
f
ic
ien
t a
t d
etec
tin
g
b
o
th
n
o
v
el
an
d
k
n
o
w
n
m
alwa
r
e.
A
tech
n
iq
u
e
was
p
r
o
p
o
s
ed
in
[
1
8
]
f
o
r
id
en
tif
y
in
g
ass
au
lts
o
n
h
o
m
e
e
q
u
ip
m
e
n
t.
T
h
e
f
u
n
ctio
n
in
g
o
f
h
o
m
e
in
ter
n
et
o
f
th
in
g
s
(
I
o
T
)
d
ev
ices a
s
well
a
s
o
th
er
o
b
s
er
v
ed
ac
tiv
ities
ar
e
s
eq
u
en
ce
s
o
f
u
s
er
ev
en
ts
th
at
ar
e
u
s
ed
to
r
ep
r
esen
t
u
s
er
b
eh
a
v
io
r
in
th
is
m
eth
o
d
[
1
9
]
.
T
h
is
tech
n
iq
u
e
lear
n
s
ev
e
n
t
s
eq
u
en
ce
s
f
o
r
ea
ch
cir
cu
m
s
tan
ce
,
tak
in
g
in
to
ac
co
u
n
t
th
at
u
s
er
s
b
eh
av
e
d
if
f
er
en
t
ly
b
ased
o
n
th
e
en
v
ir
o
n
m
en
t
o
f
th
e
h
o
m
e,
s
u
ch
as
th
e
tim
e
an
d
tem
p
er
at
u
r
e.
T
h
is
m
eth
o
d
g
e
n
er
ates
alter
n
ati
v
e
ev
en
t
s
eq
u
en
ce
s
b
y
d
eleti
n
g
s
o
m
e
e
v
en
ts
an
d
lear
n
in
g
t
h
e
c
o
m
m
o
n
ly
o
b
s
er
v
ed
s
eq
u
e
n
ce
s
in
o
r
d
er
to
r
e
d
u
ce
th
e
in
f
lu
e
n
ce
o
f
e
v
en
ts
f
r
o
m
o
t
h
er
u
s
er
s
in
th
e
h
o
u
s
e
th
at
ar
e
in
clu
d
e
d
in
th
e
m
o
n
ito
r
ed
s
eq
u
e
n
ce
.
T
ar
iq
a
n
d
T
ar
iq
[
2
0
]
id
e
n
tifie
d
th
at
lim
ited
r
eso
u
r
ce
s
o
f
in
ter
n
et
o
f
m
ed
ical
th
in
g
s
(
I
o
MT
)
d
ev
ices
ar
e
p
r
eser
v
ed
u
s
in
g
th
is
p
a
p
er
'
s
s
ca
lab
le,
h
y
b
r
i
d
ML
s
y
s
tem
,
wh
ic
h
s
u
cc
ess
f
u
lly
d
etec
ts
I
o
MT
r
an
s
o
m
war
e
th
r
ea
ts
.
T
h
is
s
o
p
h
is
ticated
an
aly
s
is
is
ess
en
tial
f
o
r
ac
c
u
r
ately
id
en
tify
in
g
an
d
elim
in
atin
g
r
an
s
o
m
war
e
th
r
ea
ts
,
p
r
o
v
id
i
n
g
a
s
tr
o
n
g
d
ef
en
s
e
f
o
r
th
e
s
ec
u
r
ity
o
f
th
e
I
o
MT
ec
o
s
y
s
tem
.
T
h
e
p
r
o
g
r
ess
an
d
c
u
r
r
en
t
d
e
v
elo
p
m
en
ts
in
m
alwa
r
e
an
aly
s
is
an
d
d
etec
tio
n
tech
n
iq
u
es
h
av
e
b
ee
n
th
o
r
o
u
g
h
ly
s
tu
d
ied
in
[
2
1
]
.
T
h
is
s
tu
d
y
,
in
p
ar
ticu
lar
,
co
n
ce
n
tr
ates
o
n
v
iewp
o
in
ts
th
at
wer
e
eith
er
m
o
s
tly
o
v
er
lo
o
k
ed
o
r
s
p
ar
in
g
ly
ex
am
i
n
ed
in
ea
r
lier
s
u
r
v
ey
s
.
T
wo
ex
am
p
les
in
clu
d
e
ex
am
in
in
g
th
e
u
tili
ty
o
f
ea
ch
d
ata
ty
p
e
b
ased
o
n
th
e
a
n
aly
s
is
m
eth
o
d
s
u
s
ed
an
d
p
r
o
v
id
i
n
g
a
co
m
p
r
eh
e
n
s
i
v
e
tax
o
n
o
m
y
f
o
r
m
alwa
r
e
d
etec
tio
n
tech
n
iq
u
es
th
at
p
r
o
v
id
es
a
m
o
r
e
d
etailed
p
r
esen
tatio
n
o
f
th
e
d
etec
tio
n
m
eth
o
d
o
lo
g
ies
th
an
b
eh
av
io
r
al
-
b
ased
,
s
ig
n
atu
r
e
-
b
ased
,
a
n
d
h
eu
r
is
tic
-
b
ased
ap
p
r
o
ac
h
es.
I
d
o
u
g
lid
e
t
a
l.
[
2
2
]
p
r
esen
ted
a
u
n
iq
u
e
in
tr
u
s
io
n
d
etec
tio
n
m
o
d
el
with
th
e
p
u
r
p
o
s
e
o
f
p
r
o
tectin
g
i
n
d
u
s
tr
y
4
.
0
s
y
s
tem
s
f
r
o
m
ev
er
-
ev
o
lv
in
g
cy
b
e
r
th
r
ea
ts
.
T
h
is
i
s
ac
co
m
p
lis
h
ed
b
y
u
tili
zin
g
ML
an
d
DL
tech
n
iq
u
es f
o
r
d
y
n
am
ic
ad
ap
tab
ilit
y
.
An
o
th
er
s
im
ilar
ap
p
r
o
ac
h
was o
u
tlin
ed
in
[
2
3
]
,
an
in
n
o
v
ativ
e
way
to
d
esig
n
in
g
an
in
tellig
en
t I
DS f
o
r
a
s
m
ar
t
co
n
s
u
m
er
elec
tr
o
n
ics
(
C
E
)
n
etwo
r
k
u
s
in
g
s
o
f
twar
e
-
d
ef
in
ed
n
etwo
r
k
in
g
(
SDN)
-
o
r
ch
estra
ted
DL
.
T
h
e
SDN
ar
ch
itectu
r
e,
wh
ich
p
er
m
its
s
ta
tic
n
etwo
r
k
in
f
r
astru
ctu
r
e
r
ec
o
n
f
i
g
u
r
atio
n
a
n
d
m
an
ag
es
th
e
d
is
p
er
s
ed
ar
ch
itectu
r
e
o
f
s
m
ar
t
C
E
n
etwo
r
k
s
b
y
s
ep
ar
atin
g
th
e
d
ata
p
lan
es
an
d
co
n
tr
o
l
p
lan
es,
was
in
itially
g
iv
en
co
n
s
id
er
atio
n
in
th
is
s
tr
ateg
y
.
Sh
ar
m
a
an
d
Mish
r
a
[
1
9
]
h
av
e
p
r
o
v
id
e
d
a
th
eo
r
etica
l
an
aly
s
is
ce
n
tr
ed
o
n
th
e
en
s
em
b
le
ap
p
r
o
ac
h
i
n
ad
d
itio
n
to
s
ev
e
r
al
en
lig
h
te
n
in
g
i
n
s
ig
h
ts
th
at
p
r
o
v
id
e
g
u
id
an
c
e
f
o
r
d
ev
elo
p
in
g
a
"e
x
ce
llen
t
an
d
d
iv
e
r
s
e"
d
etec
to
r
.
Am
ar
n
ath
a
n
d
Gu
r
u
lak
s
h
m
an
an
[
2
4
]
u
n
d
er
lin
e
t
h
e
f
ac
t
th
at
th
e
v
ar
io
u
s
ap
p
licatio
n
s
in
clu
d
e
p
r
o
b
lem
d
iag
n
o
s
tics
,
r
ec
o
m
m
en
d
atio
n
s
y
s
tem
s
,
an
d
r
is
k
ass
es
s
m
en
t.
W
h
en
it
co
m
es
to
m
o
d
elin
g
s
eq
u
en
tial d
ata,
g
ate
d
r
ec
u
r
r
en
t u
n
its
,
also
k
n
o
wn
as G
R
U,
wh
ich
ar
e
a
v
ar
iatio
n
o
f
r
ec
u
r
r
en
t n
e
u
r
al
n
etwo
r
ks
(
R
NN)
,
p
r
o
v
e
to
b
e
an
ex
tr
em
el
y
u
s
ef
u
l
to
o
l
[
2
5
]
.
W
h
en
it
co
m
es
to
th
e
s
u
cc
ess
o
f
an
I
DS,
tr
ain
i
n
g
an
d
test
in
g
ar
e
b
o
th
e
x
tr
em
ely
im
p
o
r
tan
t c
o
m
p
o
n
en
ts
.
Ng
u
y
en
an
d
R
ed
d
i
[
2
6
]
h
av
e
e
x
p
lo
r
ed
v
a
r
io
u
s
way
s
to
e
n
h
an
ce
an
o
m
al
y
-
b
ased
I
DS b
y
in
c
o
r
p
o
r
atin
g
ML
tech
n
iq
u
es.
W
ith
th
e
in
cr
ea
s
in
g
s
o
p
h
is
ticatio
n
o
f
cy
b
er
attac
k
s
,
s
tatis
tical
m
eth
o
d
s
alo
n
e
ar
e
in
s
u
f
f
icien
t,
lead
in
g
to
th
e
ad
o
p
tio
n
o
f
ML
an
d
DL
tech
n
iq
u
es
in
d
if
f
e
r
e
n
t
d
o
m
ain
s
.
M
L
h
as
s
h
o
wn
p
r
o
m
is
e
in
p
r
o
v
id
in
g
s
tr
o
n
g
r
esis
tan
ce
ag
ain
s
t
attac
k
er
s
,
an
d
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
i
n
e
(
SVM)
,
K
-
m
ea
n
s
,
a
n
d
ar
tific
ial
n
eu
r
al
n
etwo
r
k
s
(
ANN)
ar
e
p
r
e
v
alen
t
alg
o
r
ith
m
s
in
I
DS
r
esear
ch
.
An
e
x
am
p
le
o
f
a
d
y
n
am
ic
p
r
o
to
t
y
p
e
n
et
wo
r
k
th
at
is
b
ased
o
n
s
am
p
l
e
ad
ap
tatio
n
is
p
r
o
p
o
s
e
d
in
[
2
7
]
f
o
r
th
e
p
u
r
p
o
s
e
o
f
f
ew
-
s
h
o
t
m
alwa
r
e
d
etec
tio
n
.
I
n
itially
,
a
d
y
n
am
ic
co
n
v
o
l
u
tio
n
al
n
e
u
r
al
n
etwo
r
k
is
cr
ea
ted
in
o
r
d
er
to
ca
r
r
y
o
u
t
d
y
n
a
m
ic
f
ea
tu
r
e
em
b
e
d
d
in
g
b
ased
o
n
s
am
p
le
ad
ap
tatio
n
an
d
to
ex
tr
ac
t
d
ee
p
er
s
em
an
tic
in
f
o
r
m
atio
n
f
r
o
m
ea
ch
s
am
p
le.
Din
g
an
d
W
an
g
[
2
8
]
p
r
o
p
o
s
ed
th
at
eld
er
ly
liv
es
co
u
ld
b
e
s
av
ed
b
y
f
all
d
etec
tio
n
s
y
s
tem
s
(
FD
S)
t
h
at
in
f
o
r
m
f
a
m
ily
m
em
b
er
s
o
r
ca
r
e
tak
er
s
.
A
R
NN
m
o
d
el
is
u
s
ed
to
ca
teg
o
r
ize
h
u
m
an
m
o
tio
n
s
an
d
au
t
o
m
atica
lly
d
eter
m
in
e
t
h
e
f
all
s
tate.
Sin
g
h
an
d
Sin
g
h
[
2
9
]
f
o
c
u
s
es
o
n
o
p
tim
i
z
in
g
ML
p
a
r
am
et
er
s
to
ac
h
iev
e
h
ig
h
ac
c
u
r
ac
y
b
in
ar
y
f
ile
class
if
icatio
n
in
to
h
ar
m
f
u
l
an
d
b
en
ig
n
f
iles
.
T
h
e
d
ep
th
o
f
th
e
tr
ee
,
s
p
litt
in
g
cr
iter
ia,
n
-
esti
m
ato
r
s
,
lear
n
in
g
r
ate,
k
er
n
el
f
u
n
ctio
n
,
k
v
alu
e,
l
o
s
s
f
u
n
ctio
n
,
a
n
d
o
th
e
r
cr
itical
p
ar
a
m
eter
s
ar
e
ev
alu
ate
d
b
y
API
ca
lls
in
o
r
d
er
f
o
r
ML
tech
n
iq
u
es
to
g
en
er
ate
h
ig
h
ly
a
cc
u
r
ate
m
alwa
r
e
class
if
ier
r
esu
lts
.
I
n
tellig
en
t
m
alwa
r
e
d
etec
ti
o
n
tech
n
i
q
u
es
wer
e
s
u
g
g
ested
[
3
0
]
to
id
en
tif
y
p
o
ly
m
o
r
p
h
ic
an
d
p
r
ev
io
u
s
ly
u
n
id
en
tifie
d
m
alwa
r
e
v
ar
ian
ts
.
T
h
e
p
r
o
p
o
s
ed
s
y
s
te
m
u
s
ed
th
e
FP
-
g
r
o
wth
m
eth
o
d
to
d
er
iv
e
r
u
les
f
r
o
m
API
s
eq
u
en
ce
s
an
d
d
eter
m
in
e
th
e
h
ar
m
f
u
ln
ess
o
f
p
r
o
g
r
a
m
f
iles
.
T
h
e
s
y
s
tem
f
o
cu
s
ed
o
n
W
in
d
o
ws
ex
ec
u
tab
le
f
o
r
m
at
f
iles
.
F
o
r
th
e
p
u
r
p
o
s
e
o
f
a
d
ap
tiv
e
o
f
f
lo
a
d
in
g
,
B
y
u
n
et
a
l.
[
3
1
]
s
u
g
g
ested
a
h
y
b
r
id
p
r
e
d
ictio
n
m
o
d
el
th
at
m
ak
es
u
s
e
o
f
k
-
n
ea
r
est
n
eig
h
b
o
r
s
(
KNN)
an
d
SVM
m
ac
h
in
e
tr
ai
n
in
g
.
C
o
n
s
eq
u
en
t
ly
,
th
e
s
en
s
o
r
in
f
o
r
m
atio
n
th
a
t
is
v
er
y
lik
ely
to
b
e
th
e
ca
u
s
e
o
f
th
e
r
ea
l
d
e
v
ice
p
r
o
b
lem
s
m
a
y
b
e
p
ick
ed
a
n
d
t
r
an
s
m
itted
,
wh
ich
u
ltima
tely
r
esu
lts
in
in
cr
ea
s
ed
o
f
f
lo
a
d
in
g
p
er
f
o
r
m
an
ce
.
Nu
m
er
o
u
s
s
tu
d
ies
ar
e
b
ein
g
co
n
d
u
cte
d
to
a
d
d
r
ess
th
e
g
r
o
wth
o
f
d
a
n
g
er
o
u
s
s
o
f
twar
e
a
n
d
ex
am
i
n
e
m
alwa
r
e
[
3
2
]
.
Ho
we
v
er
,
t
h
e
g
e
n
er
aliza
b
ilit
y
o
f
m
o
d
els
b
ased
o
n
ANNs
m
ay
n
o
t
alwa
y
s
b
e
g
u
ar
an
teed
.
E
x
is
tin
g
ap
p
r
o
ac
h
es
p
r
im
a
r
ily
f
o
cu
s
o
n
id
en
tify
in
g
m
alwa
r
e
th
at
is
alr
ea
d
y
k
n
o
wn
in
t
h
e
liter
atu
r
e.
I
n
th
e
ca
s
e
o
f
n
ewly
id
en
tifie
d
m
alwa
r
e,
wh
ich
m
a
y
b
e
m
is
s
ed
b
y
d
y
n
am
i
c
an
d
ML
-
b
ased
ap
p
r
o
ac
h
es,
th
ese
m
eth
o
d
s
ex
h
ib
it
lo
wer
ac
cu
r
ac
y
in
class
if
y
in
g
m
al
war
e
in
to
th
e
co
r
r
ec
t
ca
teg
o
r
y
an
d
o
f
ten
r
e
q
u
ir
e
h
ig
h
ex
ec
u
tio
n
tim
e
f
o
r
class
if
icatio
n
.
T
h
er
ef
o
r
e,
it
ca
n
b
e
co
n
clu
d
e
d
f
r
o
m
th
e
tec
h
n
ic
al
an
aly
s
is
o
f
ex
is
tin
g
ap
p
r
o
ac
h
es
th
a
t
it
is
cr
u
cial
to
r
em
em
b
er
t
h
at
th
e
s
u
cc
ess
o
f
an
y
d
etec
tio
n
s
tr
ateg
y
d
e
p
en
d
s
o
n
a
n
u
m
b
er
o
f
v
ar
ia
b
les,
in
clu
d
in
g
th
e
f
r
eq
u
e
n
cy
o
f
u
p
d
ates,
th
e
q
u
al
ity
o
f
th
e
d
ata,
th
e
d
ep
lo
y
m
en
t
en
v
ir
o
n
m
en
t,
a
n
d
th
e
s
o
p
h
is
t
icatio
n
o
f
m
alwa
r
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
9
3
8
I
n
t J Ar
tif
I
n
tell
,
Vo
l.
1
4
,
No
.
1
,
Feb
r
u
ar
y
2
0
2
5
:
327
-
3
3
6
330
th
r
ea
ts
.
Or
g
an
izatio
n
s
f
r
eq
u
e
n
tly
u
s
e
m
a
n
y
la
y
er
s
o
f
d
ef
e
n
s
e,
in
teg
r
atin
g
v
ar
i
o
u
s
d
etec
tio
n
tech
n
iq
u
es,
to
en
s
u
r
e
th
o
r
o
u
g
h
m
alwa
r
e
p
r
o
t
ec
tio
n
.
C
o
n
tin
u
o
u
s
r
esear
ch
an
d
d
ev
elo
p
m
en
t
ar
e
n
ec
ess
ar
y
to
r
em
ain
ah
ea
d
o
f
n
ew
th
r
ea
ts
as th
e
cy
b
er
s
ec
u
r
i
ty
lan
d
s
ca
p
e
ch
a
n
g
es.
T
h
e
f
o
llo
win
g
is
th
e
o
r
g
an
izat
io
n
al
s
tr
u
ctu
r
e
o
f
th
e
r
e
m
ain
in
g
s
ec
tio
n
s
o
f
th
is
wo
r
k
.
I
n
s
ec
tio
n
2
,
th
e
r
eq
u
ir
ed
p
r
elim
in
ar
y
p
r
o
ce
d
u
r
es
th
at
wer
e
u
tili
ze
d
in
th
is
in
v
esti
g
atio
n
ar
e
p
r
esen
ted
.
T
h
ese
p
r
elim
in
ar
y
p
r
o
ce
d
u
r
es
in
clu
d
e
s
p
ec
if
ics
o
n
th
e
ap
p
r
o
ac
h
es
f
o
r
d
ata
s
et
c
o
llectin
g
,
p
r
e
p
r
o
ce
s
s
in
g
,
a
n
d
f
ea
tu
r
e
ex
tr
ac
tio
n
.
I
n
s
ec
tio
n
3
,
we
p
r
o
p
o
s
e
an
en
s
em
b
le
ap
p
r
o
ac
h
f
o
r
m
alwa
r
e
id
en
tific
atio
n
an
d
class
if
icatio
n
.
Su
b
s
eq
u
en
tly
,
s
ec
tio
n
4
co
n
d
u
cts
a
th
o
r
o
u
g
h
ev
alu
atio
n
a
n
d
an
al
y
s
is
o
f
th
e
p
r
o
p
o
s
ed
en
s
em
b
le
ap
p
r
o
ac
h
’
s
p
er
f
o
r
m
an
ce
.
Fin
ally
,
co
n
clu
d
i
n
g
th
o
u
g
h
ts
ar
e
p
r
esen
ted
in
s
ec
tio
n
5
o
f
th
e
s
tu
d
y
.
2.
P
RE
L
I
M
I
NAR
I
E
S
T
h
e
p
r
o
ce
s
s
o
f
d
eter
m
i
n
in
g
w
h
eth
er
a
s
u
s
p
icio
u
s
en
tity
is
m
alwa
r
e
o
r
b
en
ig
n
in
v
o
lv
es
s
ev
er
al
s
tep
s
,
in
clu
d
in
g
m
alwa
r
e
d
ata
co
l
lectio
n
,
p
r
e
-
p
r
o
ce
s
s
in
g
,
f
ea
t
u
r
e
ex
t
r
ac
tio
n
,
tr
an
s
f
o
r
m
atio
n
,
s
elec
tio
n
,
an
d
class
if
icatio
n
.
An
o
v
er
v
iew
o
f
th
e
m
eth
o
d
o
lo
g
y
em
p
l
o
y
e
d
i
n
th
is
wo
r
k
is
p
r
esen
ted
in
Fig
u
r
e
1.
−
Data
co
llectio
n
:
Sam
p
les
ar
e
co
llected
f
r
o
m
W
in
d
o
ws
-
b
ase
d
p
latf
o
r
m
s
in
v
ar
io
u
s
f
o
r
m
s
,
s
u
ch
as
im
ag
es,
b
in
ar
y
f
iles
,
b
y
tes,
a
n
d
o
p
co
d
e
s
.
Fo
r
th
is
s
tu
d
y
,
th
e
m
alwa
r
e
class
if
icatio
n
d
ataset
p
r
o
v
id
ed
b
y
Mic
r
o
s
o
f
t
is
u
tili
ze
d
,
wh
ich
ca
n
b
e
o
b
tain
e
d
f
r
o
m
th
e
s
o
u
r
ce
[
2
8
]
.
I
n
o
r
d
e
r
to
p
er
f
o
r
m
th
e
an
al
y
s
is
p
ar
t
,
t
r
ain
in
g
d
ata
an
d
test
in
g
d
ata
o
f
8
0
:2
0
is
u
s
ed
.
−
Data
p
r
e
-
p
r
o
ce
s
s
in
g
:
Un
wan
t
ed
d
ata,
s
u
c
h
as
d
ig
itally
s
ig
n
ed
d
o
c
u
m
en
ts
,
is
r
em
o
v
ed
f
r
o
m
th
e
co
llected
d
ataset,
f
o
cu
s
in
g
o
n
im
ag
es a
n
d
f
iles
.
−
Featu
r
e
ex
tr
ac
tio
n
an
d
r
ed
u
ct
io
n
:
E
x
ec
u
tio
n
tr
ac
es
ar
e
lo
g
g
ed
b
y
an
aly
zi
n
g
th
e
m
alwa
r
e
s
am
p
les.
Data
m
in
in
g
tech
n
iq
u
es
ar
e
em
p
lo
y
ed
to
ex
tr
ac
t
m
alwa
r
e
ch
ar
ac
t
er
is
tics
f
r
o
m
th
ese
lo
g
s
.
Data
m
in
in
g
in
v
o
lv
es
th
e
d
is
co
v
er
y
o
f
p
atter
n
s
an
d
p
r
ev
io
u
s
ly
u
n
k
n
o
wn
v
alu
es
in
lar
g
e
d
atab
ases
.
Du
r
in
g
th
e
ex
tr
ac
tio
n
o
f
m
alwa
r
e
f
ea
tu
r
es,
v
ar
io
u
s
ele
m
en
ts
s
u
ch
as
s
tr
in
g
s
,
b
y
te
s
eq
u
en
ce
s
,
o
p
co
d
es,
ass
em
b
ly
in
s
tr
u
ctio
n
s
,
s
y
s
tem
ca
lls
,
API
ca
lls
,
an
d
a
lis
t
o
f
d
y
n
am
ic
lin
k
lib
r
ar
ies
(
DL
L
s
)
ca
n
b
e
u
tili
ze
d
.
T
h
e
f
ea
tu
r
e
ex
tr
ac
tio
n
p
r
o
ce
s
s
em
p
lo
y
s
p
r
in
ci
p
al
co
m
p
o
n
en
t
an
aly
s
is
(
PC
A)
an
d
a
r
an
d
o
m
f
o
r
est
(
R
F)
class
if
ier
i
s
em
p
lo
y
ed
f
o
r
f
ea
tu
r
e
r
ed
u
ctio
n
.
T
h
is
s
tep
id
en
tifie
s
an
d
elim
in
ates ir
r
elev
an
t
f
ea
tu
r
es f
r
o
m
th
e
d
ata.
−
Selectio
n
an
d
class
if
icatio
n
:
T
h
e
p
r
o
p
o
s
ed
en
s
em
b
le
a
p
p
r
o
ac
h
is
u
s
ed
t
o
ex
tr
ac
t
m
alwa
r
e
f
ea
tu
r
es
a
n
d
p
er
f
o
r
m
ac
cu
r
ate
m
alwa
r
e
cla
s
s
if
icatio
n
.
I
t e
lim
in
ates u
n
wa
n
ted
f
ea
tu
r
es a
n
d
en
h
an
ce
s
th
e
ac
cu
r
ac
y
o
f
th
e
class
if
icatio
n
p
r
o
ce
s
s
.
Fig
u
r
e
1
.
Me
th
o
d
o
lo
g
y
d
ep
icti
n
g
f
lo
w
o
f
p
r
o
p
o
s
ed
en
s
em
b
le
ap
p
r
o
ac
h
3.
P
RO
P
O
SE
D
E
N
SE
M
B
L
E
A
P
P
RO
ACH
F
O
R
M
AL
W
A
RE
I
D
E
N
T
I
F
I
CAT
I
O
N
AN
D
CL
AS
SI
F
I
CAT
I
O
N
Dete
ctin
g
an
d
class
if
y
in
g
m
alwa
r
e
p
o
s
es
s
ig
n
if
ican
t
ch
alle
n
g
es
d
u
e
to
th
e
o
b
jectiv
es
o
f
m
alwa
r
e
d
ev
elo
p
er
s
,
wh
ich
in
clu
d
e
in
f
o
r
m
atio
n
th
ef
t,
ex
to
r
tio
n
,
a
n
d
n
etwo
r
k
attac
k
s
.
T
r
ad
itio
n
al
m
eth
o
d
s
h
a
v
e
b
ee
n
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
A
n
o
ve
l e
n
s
emb
le
-
b
a
s
ed
a
p
p
r
o
a
ch
fo
r
Win
d
o
w
s
ma
lw
a
r
e
d
etec
tio
n
(
V
ika
s
V
erma
)
331
ef
f
ec
tiv
e
in
id
en
tify
in
g
k
n
o
w
n
m
alwa
r
e,
b
u
t
th
ey
s
tr
u
g
g
le
with
n
ewly
em
er
g
ed
m
alwa
r
e,
k
n
o
wn
as
ze
r
o
-
d
a
y
m
alwa
r
e.
Ho
wev
er
,
t
h
e
ad
v
a
n
ce
m
en
t
o
f
ML
p
latf
o
r
m
s
h
a
s
g
r
ea
tly
en
h
a
n
ce
d
th
e
ca
p
ab
ilit
ies
o
f
m
alwa
r
e
d
etec
tio
n
m
o
d
els
in
id
en
tify
in
g
th
r
ea
ts
.
ML
tech
n
iq
u
e
s
en
ab
le
m
alwa
r
e
d
etec
tio
n
to
b
e
p
er
f
o
r
m
ed
in
two
cr
u
cial
s
tep
s
:
f
ea
tu
r
e
ex
tr
ac
tio
n
an
d
s
elec
tio
n
,
f
o
llo
wed
b
y
d
ata
cla
s
s
if
icatio
n
o
r
clu
s
ter
in
g
.
T
h
is
p
r
o
p
o
s
ed
ap
p
r
o
ac
h
f
o
cu
s
es
o
n
ML
tech
n
iq
u
es,
wh
ich
ca
n
ef
f
ec
tiv
ely
id
e
n
tify
b
o
t
h
h
ar
m
f
u
l
a
n
d
b
en
ig
n
f
iles
an
d
ac
cu
r
ately
p
r
ed
ict
th
e
n
atu
r
e
o
f
p
r
ev
io
u
s
ly
u
n
s
ee
n
f
iles
.
T
h
e
p
r
o
p
o
s
ed
ap
p
r
o
ac
h
i
n
tr
o
d
u
ce
s
an
en
s
em
b
le
class
if
ier
s
tr
ateg
y
f
o
r
m
alwa
r
e
d
etec
tio
n
an
d
class
if
icatio
n
.
T
h
is
s
tr
ateg
y
in
v
o
lv
es
in
co
r
p
o
r
atin
g
a
b
ase
class
if
ier
in
to
ea
ch
m
o
d
if
ied
tr
ain
in
g
d
ataset,
r
esu
ltin
g
in
a
co
llectio
n
o
f
b
ase
class
if
ier
s
th
at
f
o
r
m
a
n
en
s
e
m
b
le.
T
h
is
en
s
em
b
le
f
o
r
m
atio
n
is
th
e
co
r
e
p
r
in
cip
le
o
f
th
e
ap
p
r
o
ac
h
.
T
o
ac
h
iev
e
t
h
is
,
th
e
tr
ain
in
g
d
atasets
ar
e
r
eo
r
g
an
ized
u
s
in
g
v
ar
io
u
s
r
esa
m
p
lin
g
o
r
weig
h
tin
g
m
eth
o
d
s
,
cr
ea
tin
g
m
u
ltip
le
v
a
r
iatio
n
s
.
3
.
1
.
E
ns
em
ble c
la
s
s
if
ier
des
i
g
n
I
t
co
m
p
r
is
es
s
ev
er
al
s
tep
s
,
in
c
lu
d
in
g
t
h
e
clu
s
ter
in
g
p
r
o
ce
s
s
an
d
th
e
im
p
lem
e
n
tatio
n
o
f
a
n
en
s
em
b
le
-
b
ased
class
if
ier
f
o
r
m
alwa
r
e
id
en
tific
atio
n
an
d
class
if
icatio
n
.
T
h
e
clu
s
ter
in
g
s
tep
is
co
n
d
u
cte
d
p
r
io
r
to
a
p
p
ly
in
g
th
e
en
s
em
b
le
class
if
ier
an
d
u
tili
ze
s
th
e
K
-
m
ea
n
s
clu
s
ter
in
g
ap
p
r
o
ac
h
to
g
r
o
u
p
s
im
ilar
in
f
o
r
m
atio
n
to
g
eth
er
.
T
h
e
clu
s
ter
in
g
is
b
ased
o
n
wo
r
d
f
r
e
q
u
en
cy
,
wh
er
e
wo
r
d
s
with
s
im
ilar
f
r
eq
u
e
n
cy
in
d
ices
ar
e
clu
s
ter
ed
in
to
th
e
s
am
e
g
r
o
u
p
.
T
h
e
n
u
m
b
er
o
f
clu
s
ter
s
r
ep
r
esen
ted
b
y
th
e
ce
n
tr
o
id
s
i
s
d
eter
m
in
ed
b
as
ed
o
n
th
e
d
esire
d
q
u
a
n
tity
.
T
h
e
K
-
m
ea
n
s
alg
o
r
ith
m
b
eg
in
s
b
y
s
elec
tin
g
in
itial
ce
n
te
r
s
f
o
r
th
e
clu
s
ter
s
f
r
o
m
th
e
d
ata
p
atter
n
s
at
k
p
o
in
ts
.
T
h
en
,
th
e
d
is
tan
ce
b
etwe
en
ea
ch
s
am
p
le
an
d
th
e
ce
n
ter
o
f
its
co
r
r
esp
o
n
d
in
g
clu
s
t
er
is
ca
lcu
lated
,
an
d
th
e
s
am
p
le
is
a
s
s
ig
n
ed
to
th
e
clo
s
est
clu
s
t
er
.
T
h
e
av
er
ag
e
v
alu
e
o
f
th
e
d
ata
o
b
jects
with
in
ea
ch
n
ewly
f
o
r
m
ed
clu
s
ter
is
co
m
p
u
ted
to
d
eter
m
i
n
e
th
e
n
ew
ce
n
ter
f
o
r
t
h
at
clu
s
ter
.
T
h
ese
s
tep
s
ar
e
iter
ativ
ely
r
ep
ea
ted
u
n
til
th
e
clu
s
ter
in
g
ce
n
ter
s
o
f
co
n
s
ec
u
ti
v
e
iter
atio
n
s
d
o
n
o
t
s
ig
n
if
ican
t
ly
ch
an
g
e
,
in
d
icatin
g
co
n
v
er
g
e
n
ce
an
d
m
ax
im
u
m
ac
h
iev
em
en
t o
f
th
e
p
r
im
ar
y
clu
s
ter
in
g
f
u
n
ctio
n
.
T
h
e
en
s
em
b
le
lear
n
er
ap
p
r
o
ac
h
co
n
s
is
ts
o
f
th
r
ee
p
h
ases
:
−
Ph
ase
1
:
Pre
p
ar
atio
n
o
f
th
e
e
n
s
em
b
le
in
v
o
l
v
es
s
elec
tin
g
N
b
ase
class
if
ier
s
an
d
ch
o
o
s
in
g
a
m
eta
-
lear
n
in
g
alg
o
r
ith
m
.
−
Ph
ase
2
:
T
r
ain
in
g
o
f
th
e
en
s
em
b
le
o
cc
u
r
s
b
y
tr
ain
in
g
ea
ch
o
f
th
e
M
b
ase
lear
n
er
s
u
s
in
g
th
e
tr
ain
in
g
d
ataset.
K
-
f
o
ld
cr
o
s
s
-
v
alid
atio
n
is
p
er
f
o
r
m
ed
o
n
ea
ch
b
ase
lear
n
er
,
a
n
d
th
e
p
r
ed
ictio
n
s
ar
e
r
ec
o
r
d
e
d
.
−
Ph
ase
3
:
T
esti
n
g
o
f
th
e
e
n
s
em
b
le
is
co
n
d
u
cted
u
s
in
g
n
ew
a
n
d
u
n
k
n
o
wn
d
ata.
T
h
e
d
ec
is
io
n
s
m
ad
e
b
y
th
e
b
ase
lear
n
er
s
ar
e
r
ec
o
r
d
ed
,
a
n
d
th
e
m
eta
-
lea
r
n
er
e
n
s
em
b
le
d
ec
is
io
n
s
ar
e
d
er
iv
e
d
f
r
o
m
t
h
ese
b
ase
-
lev
el
d
ec
is
io
n
s
.
3
.
2
.
Select
io
n o
f
n ba
s
e
cla
s
s
if
iers f
o
r
ens
em
ble
T
h
e
av
ailab
le
liter
atu
r
e
p
r
o
v
id
es
a
wid
e
r
an
g
e
o
f
class
if
ier
s
,
ea
ch
with
its
o
wn
p
r
ed
ictiv
e
c
ap
ab
ilit
ies.
T
o
lev
er
ag
e
th
e
s
tr
en
g
th
s
o
f
th
ese
clas
s
if
ier
s
an
d
cr
ea
te
an
in
n
o
v
ativ
e
en
s
em
b
le
class
if
ier
,
we
ad
o
p
t
th
e
s
tack
ed
en
s
em
b
le
tech
n
iq
u
e.
T
h
is
ap
p
r
o
ac
h
co
m
b
in
es
th
e
p
r
ed
icti
o
n
s
o
f
d
iv
er
s
e
b
ase
m
o
d
els
to
ac
h
iev
e
im
p
r
o
v
ed
class
if
icatio
n
ac
cu
r
ac
y
an
d
r
e
d
u
ce
th
e
r
is
k
o
f
m
is
class
i
f
icatio
n
.
I
n
o
u
r
p
r
o
p
o
s
ed
ap
p
r
o
ac
h
,
we
in
co
r
p
o
r
ate
th
r
ee
s
p
ec
if
ic
b
ase
class
if
ier
s
:
−
SVM:
SVM
s
ar
e
a
d
is
tin
ctiv
e
lear
n
in
g
m
eth
o
d
r
o
o
te
d
in
s
ta
tis
tical
lear
n
in
g
th
eo
r
y
.
T
h
ey
ar
e
co
n
s
tr
u
cted
b
ased
o
n
a
lim
ited
n
u
m
b
er
o
f
s
am
p
les
f
r
o
m
th
e
tr
ain
in
g
d
ata,
aim
in
g
to
ac
h
iev
e
o
p
tim
al
class
if
icatio
n
r
esu
lts
.
I
n
itially
d
esig
n
ed
f
o
r
b
in
ar
y
class
if
icat
io
n
task
s
,
SVMs
h
av
e
b
e
en
e
x
ten
d
e
d
to
h
a
n
d
le
lar
g
e
-
s
ca
le
d
ata
m
an
ag
em
e
n
t
an
d
class
if
icatio
n
in
th
e
co
n
te
x
t
o
f
ad
v
a
n
ce
m
en
ts
in
co
m
p
u
ter
,
n
etwo
r
k
,
an
d
d
atab
ase
tech
n
o
lo
g
ies.
−
Dec
is
io
n
tr
ee
(
DT
)
:
DT
i
s
a
wid
ely
em
p
lo
y
ed
class
if
icatio
n
tech
n
iq
u
e
with
ap
p
licatio
n
s
in
v
ar
io
u
s
r
ea
l
-
wo
r
ld
s
ce
n
ar
io
s
.
T
h
is
s
y
m
b
o
li
c
lear
n
in
g
m
eth
o
d
co
n
s
tr
u
cts
a
h
ier
ar
ch
ical
s
tr
u
ctu
r
e
b
y
an
aly
zin
g
th
e
tr
ain
in
g
d
ataset.
T
h
e
s
tr
u
ctu
r
e
co
n
s
is
ts
o
f
n
o
d
es
an
d
b
r
an
ch
es
r
ep
r
ese
n
tin
g
d
i
f
f
er
en
t
d
ec
is
io
n
s
b
ased
o
n
th
e
attr
ib
u
tes
o
f
th
e
d
ataset.
−
L
o
g
is
tic
r
eg
r
ess
io
n
(
L
R
)
:
L
R
is
a
f
u
n
d
am
en
tal
s
tatis
tical
a
n
d
d
ata
m
in
i
n
g
tech
n
i
q
u
e
wi
d
ely
u
tili
ze
d
b
y
s
tatis
t
ician
s
an
d
r
esear
ch
er
s
f
o
r
an
aly
zi
n
g
an
d
class
if
y
in
g
b
in
ar
y
an
d
p
r
o
p
o
r
tio
n
al
r
esp
o
n
s
e
d
atasets
.
On
e
o
f
its
k
ey
ch
ar
ac
te
r
is
tics
is
th
e
ab
ilit
y
to
g
en
er
ate
p
r
o
b
ab
ilit
i
e
s
au
to
m
atica
lly
,
m
a
k
in
g
it
ap
p
licab
le
to
b
o
th
b
in
ar
y
a
n
d
m
u
lti
-
class
class
if
i
ca
tio
n
p
r
o
b
lem
s
.
Var
io
u
s
en
s
em
b
le
tech
n
iq
u
es,
in
clu
d
in
g
s
tack
in
g
,
b
o
o
s
tin
g
,
b
len
d
in
g
,
an
d
b
ag
g
i
n
g
,
ar
e
a
v
ailab
le
f
o
r
co
n
s
tr
u
ctin
g
en
s
em
b
le
m
o
d
el
s
.
I
n
th
is
s
tu
d
y
,
we
em
p
lo
y
th
e
s
tack
in
g
m
eth
o
d
to
c
r
ea
te
o
u
r
en
s
em
b
le.
At
l
ev
el
0
,
SVM
an
d
DT
m
o
d
els
ar
e
b
u
ilt,
wh
ile
at
l
ev
el
1
,
a
n
L
R
m
o
d
el
is
co
n
s
tr
u
cted
.
T
h
e
o
v
er
all
p
r
o
ce
s
s
is
illu
s
tr
ated
in
Fig
u
r
e
2
.
On
ce
th
e
d
ata
h
as
u
n
d
er
g
o
n
e
p
r
e
-
p
r
o
ce
s
s
in
g
,
we
u
tili
ze
th
e
ter
m
f
r
eq
u
e
n
cy
-
in
v
er
s
e
d
o
cu
m
e
n
t
f
r
eq
u
en
cy
(
T
F
-
I
DF)
tech
n
iq
u
e
to
ca
lcu
late
th
e
f
r
eq
u
en
cy
o
f
a
s
p
ec
if
ic
t
y
p
e
o
f
m
alwa
r
e.
T
h
e
R
F
m
o
d
el
th
en
wo
r
k
s
o
n
th
e
m
al
war
e
f
r
eq
u
e
n
cy
,
tak
in
g
it
i
n
to
ac
co
u
n
t.
T
o
g
e
n
er
ate
u
n
co
r
r
e
lated
v
ar
iab
les,
th
e
d
ata
is
s
u
b
jecte
d
to
PC
A,
wh
ic
h
in
v
o
lv
es
d
i
v
id
in
g
a
s
et
o
f
c
o
r
r
elate
d
v
ar
iab
les
in
to
lin
ea
r
ly
in
d
ep
en
d
en
t
s
u
b
s
ets.
T
h
e
PC
A
alg
o
r
ith
m
p
r
o
ce
s
s
e
s
th
e
m
alwa
r
e
d
ata
with
t
h
e
h
ig
h
est
f
r
eq
u
e
n
cy
as
in
p
u
t
an
d
eli
m
in
ates
th
o
s
e
with
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
9
3
8
I
n
t J Ar
tif
I
n
tell
,
Vo
l.
1
4
,
No
.
1
,
Feb
r
u
ar
y
2
0
2
5
:
327
-
3
3
6
332
th
e
lo
west
f
r
eq
u
en
cy
.
T
h
is
r
ed
u
ce
s
th
e
n
u
m
b
e
r
o
f
ex
t
r
ac
ted
f
ea
tu
r
es
u
s
in
g
t
h
e
PC
A
ap
p
r
o
ac
h
.
B
y
tr
a
n
s
f
o
r
m
in
g
th
e
d
ata
i
n
to
a
lo
wer
-
d
im
en
s
io
n
al
r
ep
r
esen
tatio
n
,
PC
A
ev
alu
ates
th
e
ef
f
ec
tiv
e
lev
el
o
f
v
ar
i
atio
n
p
r
esen
t
in
th
e
d
ata.
T
h
e
PC
A
tech
n
iq
u
e
p
r
im
ar
ily
ai
m
s
to
f
in
d
a
lin
ea
r
tr
an
s
f
o
r
m
atio
n
v
ec
to
r
t
h
at
m
ax
im
iz
es
th
e
d
ata
v
ar
ian
ce
in
th
e
p
r
o
jecte
d
s
p
ac
e,
as r
ep
r
esen
ted
in
(
1
)
.
(
)
=
(
)
(
1
)
W
h
er
e
t is
a
s
eq
u
en
ce
o
r
v
ec
to
r
o
f
v
alu
es,
th
e
s
u
b
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f
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8
9
3
8
A
n
o
ve
l e
n
s
emb
le
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b
a
s
ed
a
p
p
r
o
a
ch
fo
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Win
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o
w
s
ma
lw
a
r
e
d
etec
tio
n
(
V
ika
s
V
erma
)
333
d
is
tan
ce
.
Af
ter
war
d
s
,
th
e
av
er
ag
e
v
alu
e
o
f
ea
ch
n
ewly
f
o
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ed
clu
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ter
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s
d
ata
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b
jects
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s
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ed
to
ca
lcu
late
th
e
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ce
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ter
f
o
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th
at
cl
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ter
.
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iter
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ep
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iter
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t
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ig
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if
ican
tly
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g
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o
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t,
th
e
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p
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h
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d
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ar
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ter
in
g
o
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k
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d
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ch
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ter
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th
e
m
ea
n
o
r
ce
n
tr
o
id
o
f
th
e
ith
g
r
o
u
p
o
r
clu
s
ter
.
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t
in
d
icate
s
th
e
av
er
ag
e
o
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ce
n
tr
al
v
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o
f
th
e
o
b
s
er
v
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s
with
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th
at
p
ar
ticu
lar
g
r
o
u
p
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n
co
r
p
o
r
atin
g
all
th
e
s
tep
s
,
th
e
en
s
em
b
le
ap
p
r
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ac
h
is
d
ev
elo
p
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b
in
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DT
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SV
M,
an
d
L
R
class
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s
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DT
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u
s
ed
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en
s
e
m
b
le
as
it
s
u
p
p
o
r
ts
in
ter
p
r
etab
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y
.
W
h
en
in
ter
p
r
etab
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y
an
d
tr
an
s
p
ar
en
c
y
ar
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cr
u
cial,
DT
s
ar
e
a
co
m
m
o
n
o
p
tio
n
s
in
ce
th
ey
ar
e
ea
s
y
to
co
m
p
r
eh
en
d
an
d
v
is
u
alize
.
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is
d
ep
lo
y
ed
as
it
h
as
ca
p
ab
ilit
y
to
h
an
d
le
h
ig
h
-
d
im
e
n
s
io
n
al
d
ata
ef
f
ec
tiv
ely
.
Mo
r
e
o
v
er
,
SVMs
ar
e
v
er
y
ef
f
ec
tiv
e
f
o
r
is
s
u
es
wh
en
th
e
n
u
m
b
er
s
o
f
f
ea
t
u
r
es
ar
e
lar
g
e
as
co
m
p
ar
ed
to
th
e
n
u
m
b
er
o
f
s
am
p
les.
L
R
g
en
er
ates
p
r
o
b
ab
ilit
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s
co
r
es
b
etwe
en
0
an
d
1
,
wh
ich
r
ep
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t
t
h
e
p
o
s
s
ib
ilit
y
o
f
f
allin
g
in
to
a
s
p
ec
if
ic
class
,
r
ath
er
th
an
b
i
n
ar
y
p
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ed
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n
s
(
0
o
r
1
)
.
Dep
en
d
in
g
o
n
th
e
n
ee
d
s
o
f
t
h
e
ap
p
licatio
n
,
th
is
p
r
o
b
ab
ili
ty
s
co
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e
m
ay
b
e
u
s
ef
u
l
f
o
r
m
ak
in
g
ju
d
g
m
en
ts
,
ev
alu
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f
o
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e
ca
s
ts
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an
d
estab
lis
h
in
g
v
ar
io
u
s
d
ec
is
io
n
th
r
esh
o
ld
s
.
4.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
I
n
th
e
f
ield
o
f
c
y
b
er
s
ec
u
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ity
,
m
alicio
u
s
ac
to
r
s
f
r
eq
u
en
tly
em
p
lo
y
m
alicio
u
s
s
o
f
twar
e
t
o
ca
r
r
y
o
u
t
cy
b
er
-
attac
k
s
o
n
tar
g
ete
d
s
y
s
tem
s
.
Ma
lwar
e,
wh
ich
en
co
m
p
a
s
s
es
v
ar
io
u
s
f
o
r
m
s
s
u
ch
as
v
ir
u
s
es,
wo
r
m
s
,
T
r
o
jan
h
o
r
s
es,
r
o
o
tk
its
,
an
d
r
a
n
s
o
m
war
e,
r
ef
er
s
to
s
o
f
twar
e
d
esig
n
ed
to
in
ten
tio
n
ally
ex
ec
u
te
h
ar
m
f
u
l
ac
tio
n
s
o
n
u
n
s
u
s
p
ec
tin
g
v
ictim
s
'
co
m
p
u
t
er
s
.
E
ac
h
ty
p
e
an
d
f
am
ily
o
f
m
alwa
r
e
h
as
its
o
wn
s
p
ec
if
ic
o
b
jectiv
es,
r
an
g
i
n
g
f
r
o
m
co
m
p
r
o
m
is
in
g
s
y
s
tem
in
teg
r
ity
to
f
ac
ilit
atin
g
th
e
t
h
ef
t
o
f
p
r
iv
ate
i
n
f
o
r
m
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n
a
n
d
en
ab
lin
g
r
em
o
te
co
d
e
ex
ec
u
tio
n
.
T
h
e
s
tu
d
y
in
v
esti
g
ated
t
h
e
ef
f
ec
ts
n
ew
m
alwa
r
e
ty
p
es
o
n
win
d
o
ws
s
y
s
tem
an
d
th
eir
d
etec
t
io
n
wh
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ea
r
lier
s
tu
d
ies
h
av
e
ex
p
lo
r
ed
th
e
im
p
ac
t
o
f
estab
lis
h
ed
te
ch
n
iq
u
es.
I
n
itially
,
m
alwa
r
e
h
ad
s
tr
aig
h
tf
o
r
war
d
o
b
jectiv
es,
m
ak
i
n
g
it
r
elativ
el
y
ea
s
ier
to
d
etec
t.
T
h
is
ca
te
g
o
r
y
,
k
n
o
wn
as
tr
ad
itio
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r
b
asic
m
alwa
r
e,
was
id
en
tifia
b
le
u
s
in
g
estab
lis
h
ed
tech
n
iq
u
es.
Ho
wev
er
,
th
e
th
r
ea
t
lan
d
s
c
ap
e
h
as
ev
o
lv
ed
,
g
i
v
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to
a
n
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g
en
er
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n
o
f
k
e
r
n
el
-
m
o
d
e
m
alwa
r
e.
T
h
ese
a
d
v
an
ce
d
m
alwa
r
e
v
ar
ian
ts
p
o
s
e
s
ig
n
if
ica
n
t
ch
al
len
g
es
in
d
etec
tio
n
co
m
p
ar
ed
to
o
ld
e
r
v
er
s
io
n
s
.
I
n
co
n
tr
ast,
co
n
v
en
tio
n
al
m
alwa
r
e
ty
p
ically
co
n
s
is
ts
o
f
a
s
in
g
le
p
r
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ce
d
u
r
e
an
d
d
o
e
s
n
o
t e
m
p
lo
y
co
m
p
lex
tech
n
iq
u
es to
ev
ad
e
d
etec
tio
n
.
Fu
r
th
er
m
o
r
e
,
m
o
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er
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m
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e
u
tili
ze
s
a
co
m
b
i
n
atio
n
o
f
ac
tiv
e
an
d
d
o
r
m
an
t
p
r
o
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d
u
r
es
s
im
u
ltan
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u
s
ly
,
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p
lo
y
in
g
v
ar
io
u
s
o
b
f
u
s
ca
tio
n
tech
n
iq
u
es
to
co
n
ce
al
its
p
r
esen
ce
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d
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s
is
t
with
in
a
n
etwo
r
k
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o
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r
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o
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m
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s
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ap
p
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av
e
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th
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r
e.
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n
th
is
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ch
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d
y
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a
co
m
p
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r
ativ
e
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a
ly
s
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s
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n
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cted
am
o
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f
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er
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ased
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th
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Mic
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ig
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ataset
[
3
3
]
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h
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ML
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u
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e
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Fig
u
r
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s
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ased
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n
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at
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at
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t
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es
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et
tin
g
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%
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n
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p
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ax
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m
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r
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iev
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d
is
9
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%.
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h
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p
h
asizes
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e
n
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ity
f
o
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r
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en
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h
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ce
th
e
ac
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d
ef
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e
ctiv
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ess
o
f
m
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d
class
if
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n
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u
r
e
3
.
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u
r
ac
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c
o
m
p
ar
is
o
n
o
f
d
if
f
er
e
n
t M
L
ap
p
r
o
ac
h
es f
o
r
m
alwa
r
e
class
i
f
icatio
n
Fig
u
r
e
4
.
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cisi
o
n
c
o
m
p
ar
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o
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o
f
d
if
f
er
e
n
t M
L
ap
p
r
o
ac
h
es f
o
r
m
alwa
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e
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i
f
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n
Fig
u
r
e
5
.
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ec
all
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m
p
a
r
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o
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o
f
d
if
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n
t M
L
ap
p
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ac
h
es f
o
r
m
alwa
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e
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i
f
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n
Fig
u
r
e
6
.
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s
co
r
e
co
m
p
ar
is
o
n
o
f
d
if
f
er
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t M
L
ap
p
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h
es f
o
r
m
alwa
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i
f
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n
5.
CO
NCLU
SI
O
N
I
n
th
is
s
tu
d
y
,
a
co
m
p
r
e
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en
s
iv
e
ex
am
in
atio
n
is
c
o
n
d
u
cted
o
n
v
a
r
io
u
s
ML
tech
n
iq
u
es
av
ai
lab
le
in
t
h
e
ex
is
tin
g
liter
atu
r
e
f
o
r
th
e
id
e
n
tific
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n
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d
class
if
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n
o
f
m
alwa
r
e.
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tr
ics
s
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ch
as
ac
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r
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n
,
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ec
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co
r
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tili
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o
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n
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u
ct
a
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m
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a
r
ativ
e
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aly
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o
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th
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f
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o
f
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s
tech
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i
g
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at
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o
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g
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,
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ex
h
ib
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s
u
p
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r
p
e
r
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ce
.
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wev
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is
s
till
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m
f
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ter
m
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o
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tim
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ased
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eth
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.
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h
e
p
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ed
ap
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in
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r
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m
u
ltip
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ML
tech
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t d
if
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n
t stag
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o
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ass
if
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h
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lts
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at
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SVM
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u
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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335
RE
F
E
R
E
NC
E
S
[
1
]
K
.
K
i
s
h
o
r
e
a
n
d
S
.
S
h
a
r
ma,
“
I
n
f
o
r
ma
t
i
o
n
s
e
c
u
r
i
t
y
&
p
r
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a
c
y
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n
r
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a
l
l
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f
e
-
t
h
r
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a
t
s
&
mi
t
i
g
a
t
i
o
n
s:
a
r
e
v
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e
w
,
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o
u
r
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a
l
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m
p
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o
.
1
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p
.
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4
–
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7
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0
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3
.
[
2
]
S
.
S
h
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mn
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e
s
h
,
M
.
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a
n
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j
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d
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e
sh
a
v
,
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l
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a
p
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v
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,
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s
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[
3
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.
A
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,
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.
[
4
]
H
.
Y
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C
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e
n
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.
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h
a
r
m
a
,
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.
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Evaluation Warning : The document was created with Spire.PDF for Python.