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a
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f
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%
.
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h
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re
su
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th
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g
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m
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tain
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ti
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tec
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iq
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stre
n
g
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tw
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rk
se
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g
a
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st ev
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lv
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th
re
a
ts.
K
ey
w
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d
s
:
Featu
r
e
s
el
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t
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n
Gr
ad
ien
t b
o
o
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tin
g
m
ac
h
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a
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:
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s
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M.
A
b
u
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aj
Dep
ar
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m
en
t o
f
Net
w
o
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k
s
an
d
C
y
b
er
s
ec
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r
it
y
,
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lt
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f
I
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f
o
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m
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T
ec
h
n
o
lo
g
y
Al
-
Ah
li
y
y
a
Am
m
a
n
U
n
iv
er
s
it
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Am
m
an
1
9
1
1
1
,
J
o
r
d
an
E
m
ail:
m
.
ab
u
al
h
aj
@
a
m
m
a
n
u
.
ed
u
.
j
o
1.
I
NT
RO
D
UCT
I
O
N
I
n
r
ec
en
t
y
ea
r
s
,
a
s
c
y
b
er
th
r
e
ats
h
av
e
b
ec
o
m
e
m
o
r
e
s
o
p
h
is
ticated
an
d
f
r
eq
u
e
n
t,
o
r
g
a
n
iza
tio
n
s
h
a
v
e
tu
r
n
ed
to
in
tr
u
s
io
n
d
etec
tio
n
s
y
s
te
m
s
(
I
DS)
as
a
cr
itical
lin
e
o
f
d
ef
en
s
e
i
n
p
r
o
tectin
g
th
e
ir
n
et
w
o
r
k
s
an
d
d
ata.
An
I
DS
f
u
n
ctio
n
s
as
a
s
ec
u
r
it
y
to
o
l
th
at
m
o
n
i
to
r
s
an
d
a
n
al
y
ze
s
n
et
w
o
r
k
tr
a
f
f
ic,
id
en
ti
f
y
in
g
s
u
s
p
icio
u
s
ac
tiv
it
y
an
d
p
o
ten
tial
t
h
r
ea
ts
i
n
r
ea
l
-
ti
m
e
[
1
]
,
[
2
]
.
W
ith
th
e
in
cr
ea
s
in
g
v
o
l
u
m
e
o
f
d
ata
g
en
er
ated
b
y
m
o
d
er
n
n
e
t
w
o
r
k
s
,
tr
ad
itio
n
al
r
u
le
-
b
ased
I
DS
ap
p
r
o
ac
h
es
s
tr
u
g
g
le
to
k
ee
p
p
ac
e,
o
f
ten
lead
i
n
g
to
h
ig
h
er
f
alse
p
o
s
itiv
e
s
an
d
n
eg
at
iv
e
s
[
2
]
,
[
3
]
.
A
s
a
r
esu
l
t,
m
ac
h
i
n
e
lear
n
in
g
(
M
L
)
tech
n
iq
u
e
s
h
a
v
e
e
m
er
g
ed
as
a
p
r
o
m
is
in
g
s
o
l
u
tio
n
f
o
r
en
h
a
n
ci
n
g
I
D
S
p
er
f
o
r
m
a
n
ce
b
y
en
ab
li
n
g
s
y
s
te
m
s
to
lear
n
f
r
o
m
h
i
s
to
r
ical
d
ata
an
d
d
etec
t
p
r
ev
io
u
s
l
y
u
n
k
n
o
w
n
attac
k
p
atter
n
s
[
1
]
,
[
4
]
.
T
h
e
in
teg
r
atio
n
o
f
M
L
clas
s
if
icatio
n
alg
o
r
it
h
m
s
i
n
to
I
DS
s
o
f
f
er
s
a
d
y
n
a
m
ic
ap
p
r
o
ac
h
to
th
r
ea
t
d
etec
tio
n
,
allo
w
i
n
g
th
e
s
y
s
te
m
to
d
if
f
er
e
n
tiate
b
et
w
ee
n
le
g
iti
m
ate
tr
af
f
ic
an
d
p
o
ten
tial
in
tr
u
s
io
n
s
b
ased
o
n
lear
n
ed
p
atter
n
s
[
1
]
,
[
4
]
.
C
lass
i
f
icatio
n
al
g
o
r
it
h
m
s
s
u
ch
a
s
g
r
ad
ien
t
b
o
o
s
tin
g
m
ac
h
i
n
e
(
GB
M)
an
d
lo
g
is
tic
r
eg
r
ess
io
n
(
L
R
)
h
a
v
e
s
h
o
w
n
s
tr
o
n
g
p
o
te
n
tial
i
n
cla
s
s
i
f
y
in
g
n
e
t
w
o
r
k
tr
a
f
f
ic
d
ata
in
to
b
en
ig
n
o
r
m
alicio
u
s
ca
teg
o
r
ies
[
5
]
,
[
6
]
.
B
y
le
v
er
ag
in
g
th
e
ab
ili
t
y
o
f
M
L
m
o
d
els,
I
DSs
ca
n
b
ec
o
m
e
m
o
r
e
r
esil
i
e
n
t
an
d
ac
c
u
r
ate
i
n
d
etec
tin
g
a
w
id
e
ar
r
a
y
o
f
c
y
b
er
th
r
ea
ts
,
in
cl
u
d
i
n
g
b
o
t
h
k
n
o
w
n
a
n
d
u
n
k
n
o
w
n
atta
ck
s
.
Ho
w
e
v
er
,
th
e
ef
f
ec
tiv
e
n
e
s
s
o
f
t
h
ese
m
o
d
els
is
h
i
g
h
l
y
d
ep
en
d
en
t
o
n
t
h
e
q
u
alit
y
o
f
th
e
f
ea
tu
r
es
u
s
ed
f
o
r
tr
ain
in
g
,
w
h
ic
h
m
ak
e
s
f
ea
tu
r
e
s
elec
tio
n
a
cr
iti
ca
l
co
m
p
o
n
e
n
t i
n
b
u
ild
i
n
g
ef
f
i
cien
t I
DS
s
[
7
]
,
[
8
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
R
ec
o
n
f
i
g
u
r
ab
le
&
E
m
b
ed
d
ed
Sy
s
t
I
SS
N:
2089
-
4864
E
n
h
a
n
ci
n
g
in
tr
u
s
io
n
d
etec
tio
n
s
ystems
w
ith
h
yb
r
id
HHO
-
WOA
o
p
timiz
a
tio
n
a
n
d
…
(
Mo
s
l
eh
M.
A
b
u
a
l
h
a
j
)
519
Featu
r
e
s
elec
tio
n
alg
o
r
it
h
m
s
p
lay
a
k
e
y
r
o
le
i
n
e
n
h
an
ci
n
g
th
e
p
er
f
o
r
m
an
ce
o
f
M
L
-
b
ase
d
I
DSs
b
y
id
en
ti
f
y
i
n
g
th
e
m
o
s
t
r
elev
a
n
t
an
d
in
f
o
r
m
ati
v
e
f
ea
t
u
r
es
f
r
o
m
v
ast
n
et
w
o
r
k
tr
af
f
ic
d
atasets
.
R
ed
u
ci
n
g
th
e
d
i
m
en
s
io
n
al
it
y
o
f
t
h
e
d
ata
n
o
t
o
n
l
y
i
m
p
r
o
v
es
m
o
d
el
ac
cu
r
ac
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b
u
t
al
s
o
r
ed
u
ce
s
co
m
p
u
tatio
n
al
co
s
t
s
,
allo
w
in
g
f
o
r
f
aster
an
d
m
o
r
e
e
f
f
icien
t
in
tr
u
s
io
n
d
etec
tio
n
.
Op
ti
m
i
za
tio
n
tech
n
iq
u
e
s
s
u
ch
a
s
w
h
ale
o
p
ti
m
iza
tio
n
alg
o
r
ith
m
(
W
O
A
)
an
d
Har
r
is
Ha
w
k
s
o
p
ti
m
iza
tio
n
(
HHO)
h
av
e
b
ee
n
em
p
lo
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ed
to
f
in
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-
t
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s
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s
s
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es
m
a
x
i
m
ize
d
etec
tio
n
ac
cu
r
ac
y
w
h
ile
m
i
n
i
m
izi
n
g
n
o
is
e
a
n
d
ir
r
elev
an
t
d
ata.
T
h
e
co
m
b
in
a
tio
n
o
f
ef
f
ec
t
iv
e
f
ea
t
u
r
e
s
ele
ctio
n
a
n
d
r
o
b
u
s
t
cla
s
s
i
f
ica
tio
n
al
g
o
r
ith
m
s
ca
n
s
ig
n
i
f
ica
n
tl
y
i
m
p
r
o
v
e
th
e
r
eli
ab
ilit
y
an
d
p
er
f
o
r
m
an
ce
o
f
I
DSs
,
o
f
f
er
i
n
g
a
p
o
w
er
f
u
l
to
o
l
f
o
r
cy
b
er
s
ec
u
r
i
t
y
p
r
o
f
ess
io
n
al
s
to
s
af
e
g
u
ar
d
th
ei
r
n
et
w
o
r
k
s
a
g
ain
s
t
i
n
cr
ea
s
i
n
g
l
y
co
m
p
lex
c
y
b
er
th
r
ea
ts
[
9
]
-
[
1
2
]
.
Nu
m
eo
u
s
r
esear
c
h
w
r
o
k
s
h
a
v
e
p
r
o
p
o
s
ed
t
o
en
h
an
ce
I
DS
s
y
s
te
m
s
p
er
f
o
r
m
an
ce
.
Z
h
ao
an
d
Z
h
ao
[
1
3
]
p
r
o
p
o
s
ed
a
s
o
lu
tio
n
th
at
i
m
p
r
o
v
es
th
e
ac
cu
r
ac
y
o
f
I
DS
s
y
s
te
m
s
b
y
u
s
i
n
g
ML
tec
h
n
iq
u
es.
T
h
e
p
r
o
p
o
s
ed
s
o
lu
tio
n
u
s
es
t
h
e
r
ad
ial
b
asis
f
u
n
c
tio
n
(
R
B
F)
n
e
u
r
al
n
et
wo
r
k
s
to
e
x
tr
ac
t
i
m
p
o
r
tan
t
f
ea
t
u
r
es
f
r
o
m
th
e
d
ata.
T
h
en
,
th
e
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
in
e
(
SVM)
tec
h
n
iq
u
e
i
s
u
s
ed
f
o
r
clas
s
i
f
icatio
n
b
ased
o
n
t
h
e
ex
tr
ac
ted
f
ea
tu
r
e
s
f
r
o
m
th
e
R
B
F
tech
n
iq
u
e.
T
h
e
p
r
o
p
o
s
ed
s
o
lu
tio
n
w
a
s
test
ed
u
s
in
g
th
e
KDD9
9
d
ataset
an
d
im
p
le
m
en
ted
i
n
P
y
t
h
o
n
.
T
h
e
r
esu
lt
s
s
h
o
w
ed
th
at
co
m
b
i
n
i
n
g
R
B
F a
n
d
SVM
t
ec
h
n
iq
u
es a
c
h
ie
v
ed
a
h
ig
h
ac
c
u
r
ac
y
o
f
9
7
%.
R
esear
ch
b
y
Dao
u
d
et
a
l.
[
1
4
]
e
m
p
h
a
s
ize
t
h
e
p
o
ten
tial
o
f
ML
to
e
n
h
an
ce
th
e
ca
p
ab
ilit
i
es
o
f
I
D
S.
T
h
e
au
th
o
r
s
p
r
o
p
o
s
e
im
p
le
m
e
n
ti
n
g
v
ar
io
u
s
M
L
alg
o
r
it
h
m
s
,
s
p
ec
if
icall
y
k
-
n
ea
r
es
t
n
ei
g
h
b
o
r
(
KNN)
,
d
ec
is
io
n
tr
ee
,
an
d
r
an
d
o
m
f
o
r
est
,
w
ith
i
n
th
e
I
DS
f
r
a
m
e
w
o
r
k
.
T
h
e
g
o
al
is
to
m
ea
s
u
r
e
th
eir
ef
f
ec
ti
v
en
es
s
in
i
m
p
r
o
v
i
n
g
d
etec
tio
n
ac
cu
r
ac
y
.
T
h
e
p
ap
er
u
s
es
t
h
e
K
-
Fo
ld
cr
o
s
s
-
v
al
id
atio
n
m
et
h
o
d
to
en
h
a
n
ce
d
etec
tio
n
r
ates.
T
h
e
f
i
n
d
in
g
s
in
d
icate
th
at
th
e
r
an
d
o
m
f
o
r
est
alg
o
r
it
h
m
,
p
ar
ticu
la
r
l
y
w
it
h
1
0
0
tr
ee
s
,
ac
h
iev
ed
t
h
e
h
ig
h
es
t
ac
cu
r
ac
y
o
f
9
2
.
6
5
%,
o
u
tp
er
f
o
r
m
i
n
g
t
h
e
o
th
er
alg
o
r
ith
m
s
test
ed
.
Ak
a
n
d
e
et
a
l.
[
1
5
]
p
r
esen
t
a
n
o
v
el
h
y
b
r
id
alg
o
r
ith
m
th
at
c
o
m
b
i
n
es
co
n
v
o
lu
tio
n
al
n
e
u
r
al
n
et
w
o
r
k
s
(
C
NN)
an
d
d
ee
p
n
e
u
r
al
n
et
wo
r
k
s
.
T
h
is
in
n
o
v
a
tiv
e
ap
p
r
o
ac
h
ai
m
s
to
e
n
h
a
n
ce
t
h
e
ac
c
u
r
ac
y
an
d
e
f
f
ec
ti
v
en
e
s
s
o
f
in
tr
u
s
io
n
d
etec
tio
n
,
ad
d
r
ess
in
g
t
h
e
li
m
itatio
n
s
o
f
tr
ad
iti
o
n
al
m
et
h
o
d
s
i
n
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en
ti
f
y
i
n
g
n
et
w
o
r
k
i
n
tr
u
s
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n
s
.
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h
is
h
y
b
r
id
ap
p
r
o
ac
h
i
s
d
esi
g
n
ed
to
ca
te
g
o
r
ize
n
et
w
o
r
k
p
a
ck
ets
an
d
id
e
n
ti
f
y
i
n
tr
u
s
io
n
s
,
class
i
f
y
in
g
th
e
m
a
s
eith
er
n
o
r
m
a
l
o
r
m
alicio
u
s
.
T
h
e
C
NN
ac
h
ie
v
ed
a
h
i
g
h
ac
cu
r
ac
y
r
ate
o
f
9
9
.
1
8
%,
o
u
tp
er
f
o
r
m
i
n
g
o
th
er
class
i
f
ier
s
.
2.
RE
S
E
ARCH
M
E
T
H
O
D
2
.
1
.
NSL
-
K
DD
da
t
a
s
et
T
h
e
NSL
-
KDD
d
ataset
w
ill
b
e
u
s
ed
in
th
is
w
o
r
k
to
ev
alu
a
te
th
e
p
r
o
p
o
s
ed
ML
m
o
d
el.
T
h
e
NSL
-
K
DD
d
ataset
u
s
ed
co
n
t
ain
s
1
4
8
,
5
1
7
attac
k
s
a
n
d
b
e
n
ig
n
r
ec
o
r
d
s
.
I
n
ad
d
itio
n
,
th
e
NSL
-
K
DD
d
ataset
co
n
s
is
ts
o
f
4
0
f
ea
tu
r
es.
T
h
er
e
ar
e
3
8
d
if
f
er
e
n
t
t
y
p
es
o
f
atta
ck
s
ca
teg
o
r
ized
i
n
to
te
n
t
y
p
e
s
o
f
Do
S
attac
k
,
s
i
x
t
y
p
es
o
f
p
r
o
b
e
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k
,
s
e
v
en
t
y
p
es
o
f
u
s
er
s
to
r
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o
t
(
U2
R
)
attac
k
,
an
d
1
5
t
y
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es
o
f
r
e
m
o
te
to
l
o
ca
l
(
R
2
L
)
attac
k
.
T
ab
le
1
lis
t th
ese
3
8
d
if
f
er
e
n
t t
y
p
e
s
o
f
attac
k
s
[
1
6
]
,
[
1
7
]
.
T
ab
le
1
.
NSL
-
KD
D
d
ataset
att
ac
k
g
r
o
u
p
s
M
a
i
n
a
t
t
a
c
k
S
u
b
t
y
p
e
s
D
o
S
B
a
c
k
,
P
r
o
c
e
sst
a
b
l
e
,
P
o
d
,
L
a
n
d
,
S
mu
r
f
,
N
e
p
t
u
n
e
,
A
p
a
c
h
e
2
,
T
e
a
r
d
r
o
p
,
U
d
p
st
o
r
m,
a
n
d
W
o
r
m
P
r
o
b
e
M
sc
a
n
,
S
a
t
a
n
,
N
m
a
p
,
I
p
sw
e
e
p
,
P
o
r
t
s
w
e
e
p
,
a
n
d
S
a
i
n
t
U
2
R
L
o
a
d
mo
d
u
l
e
,
B
u
f
f
e
r
o
v
e
r
f
l
o
w
,
S
q
l
a
t
t
a
c
k
,
X
t
e
r
m,
R
o
o
t
k
i
t
,
P
e
r
l
,
a
n
d
P
s
R
2
L
G
u
e
ss_
P
a
ssw
o
r
d
,
S
n
m
p
g
u
e
ss,
I
map
,
P
h
f
,
M
u
l
t
i
h
o
p
,
W
a
r
e
z
mast
e
r
,
F
t
p
_
w
r
i
t
e
,
X
sn
o
o
p
,
W
a
r
e
z
S
p
y
,
S
e
n
d
mai
l
,
X
l
o
c
k
,
S
n
m
p
g
e
t
a
t
t
a
c
k
,
c
l
i
e
n
t
,
H
t
t
p
t
u
n
n
e
l
,
a
n
d
N
a
me
d
2
.
2
.
L
o
g
is
t
ic
re
g
re
s
s
io
n
a
nd
g
ra
dient
bo
o
s
t
ing
m
a
chi
ne
cla
s
s
if
ica
t
io
n a
lg
o
rit
h
m
s
2
.
2
.
1
.
L
o
g
is
t
ic
re
g
re
s
s
io
n
cl
a
s
s
if
iers
L
R
i
s
a
b
in
ar
y
clas
s
i
f
icatio
n
a
lg
o
r
ith
m
th
at
m
o
d
el
s
th
e
lik
el
ih
o
o
d
o
f
an
ev
en
t
o
cc
u
r
r
in
g
b
ased
o
n
a
co
llectio
n
o
f
p
r
ed
icto
r
f
ac
to
r
s
u
s
i
n
g
a
lo
g
is
tic
f
u
n
ctio
n
.
T
h
e
lo
g
is
tic
f
u
n
c
tio
n
g
en
er
ate
s
a
v
alu
e
b
et
w
ee
n
0
an
d
1
,
w
h
ic
h
r
ep
r
esen
t
s
th
e
li
k
eli
h
o
o
d
th
at
th
e
ev
e
n
t
w
ill o
cc
u
r
[
6
]
,
[
1
8
]
.
Fig
u
r
e
1
clar
if
ies t
h
e
L
R
tec
h
n
iq
u
e.
2
.
2
.
2
.
G
ra
dient
bo
o
s
t
ing
m
a
c
hin
e
cl
a
s
s
if
iers
GB
M
b
u
ild
s
a
s
eq
u
en
ce
o
f
D
T
s
,
ea
ch
o
f
w
h
ic
h
tr
ies
to
co
r
r
ec
t
th
e
er
r
o
r
s
o
f
th
e
p
r
ev
io
u
s
tr
ee
.
T
h
is
s
eq
u
en
tial
p
r
o
ce
s
s
o
f
b
u
ild
i
n
g
tr
ee
s
,
ad
j
u
s
tin
g
t
h
e
w
ei
g
h
ts
,
an
d
r
ep
ea
tin
g
t
h
e
p
r
o
ce
s
s
is
c
alled
b
o
o
s
tin
g
.
T
h
e
id
ea
b
eh
in
d
GB
M
is
to
co
m
b
i
n
e
m
u
l
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w
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s
i
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A
w
ea
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cla
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f
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m
p
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m
o
d
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t
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m
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cr
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tes a
p
o
w
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f
u
l a
n
d
a
cc
u
r
ate
m
o
d
el
[
5
]
,
[
1
9
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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14
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2
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J
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20
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Fig
u
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1
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L
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H
a
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a
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k
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m
s
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h
m
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h
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e
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e
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f
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e
s
o
f
t
h
e
attac
k
f
r
o
m
th
e
NSL
-
K
DD
d
ataset.
T
h
e
H
HO
an
d
W
O
A
al
g
o
r
ith
m
s
h
a
v
e
b
ee
n
d
er
iv
ed
f
r
o
m
t
h
e
b
e
h
av
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r
Har
r
is
Ha
w
k
s
a
n
d
W
h
ale,
r
esp
ec
tiv
el
y
[
1
1
]
,
[
1
2
]
.
T
h
e
HHO
an
d
W
OA
alg
o
r
ith
m
s
p
er
f
o
r
m
s
e
v
er
al
o
p
er
atio
n
s
to
f
in
d
th
e
b
est
s
o
lu
tio
n
.
T
h
ese
o
p
er
atio
n
s
ar
e
b
ased
o
n
f
u
n
ctio
n
s
,
v
ar
iab
l
es,
an
d
co
n
s
tr
ai
n
t
s
th
at
i
m
p
a
ct
f
i
n
d
in
g
t
h
e
b
est
s
o
lu
tio
n
.
On
e
o
f
th
e
k
e
y
f
u
n
c
tio
n
s
i
n
HHO
a
n
d
W
OA
al
g
o
r
ith
m
s
is
th
e
"
o
b
j
ec
tiv
e
f
u
n
cti
o
n
.
"
T
h
e
o
b
j
ec
tiv
e
f
u
n
ctio
n
f
o
r
b
o
th
HHO
an
d
W
OA
co
n
s
id
er
s
p
r
ed
ictio
n
ac
cu
r
ac
y
f
o
r
th
e
f
ea
tu
r
e
s
u
b
s
et
an
d
th
e
n
u
m
b
er
o
f
s
elec
ted
f
ea
tu
r
es
;
t
h
is
a
i
m
s
to
ch
o
o
s
e
a
f
ea
t
u
r
e
s
u
b
s
et
t
h
at
en
h
a
n
ce
s
th
e
m
o
d
el
'
s
p
r
ed
ictiv
e
ac
c
u
r
ac
y
w
h
il
e
av
o
id
in
g
o
v
er
f
itt
in
g
b
y
m
i
n
i
m
izi
n
g
t
h
e
n
u
m
b
er
o
f
f
ea
t
u
r
e
s
.
T
h
e
o
b
j
ec
tiv
e
f
u
n
ctio
n
o
f
HHO
an
d
W
OA
is
ca
lcu
lated
u
s
i
n
g
(
1
)
[
9
]
,
[
1
0
]
,
[
2
0
]
–
[
2
3
]
.
=
ℎ
∗
+
∗
(
)
(
1
)
A
lp
h
a
an
d
B
eta
ar
e
w
ei
g
h
t
s
t
h
at
d
eter
m
i
n
e
t
h
e
r
elati
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m
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o
r
tan
ce
o
f
th
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er
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r
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ate
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d
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n
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n
t
h
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s
t
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y
,
A
lp
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9
9
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d
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0
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0
1
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icatin
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ig
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e
m
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asi
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n
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m
izi
n
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th
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er
r
o
r
r
ate.
T
h
e
er
r
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r
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lated
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s
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cc
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r
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w
h
er
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r
ac
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i
s
t
h
e
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er
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m
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s
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r
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s
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f
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o
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el
u
s
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n
g
th
e
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ted
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t
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r
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s
u
b
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et.
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h
is
m
ea
n
s
th
at
a
lo
w
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er
r
o
r
r
ate
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alu
e
in
d
icate
d
a
b
etter
f
ea
tu
r
e
s
u
b
s
et
(
b
etter
ac
cu
r
ac
y
)
.
T
h
e
n
u
m
b
er
o
f
s
elec
ted
f
ea
tu
r
es
is
th
e
co
u
n
t
o
f
f
ea
tu
r
e
s
th
e
alg
o
r
ith
m
c
h
o
o
s
es.
T
h
e
m
ax
i
m
u
m
n
u
m
b
er
o
f
f
ea
t
u
r
es
is
th
e
co
u
n
t o
f
al
l a
v
ailab
le
f
ea
tu
r
es i
n
th
e
d
atase
t [
9
]
,
[
1
0
]
,
[
2
0
]
–
[
2
3
]
.
B
esid
es
th
e
o
b
j
ec
tiv
e
f
u
n
ctio
n
,
th
e
d
ec
is
io
n
v
ar
iab
les
f
o
r
m
a
k
e
y
ele
m
en
t
o
f
th
e
HH
O
an
d
W
OA
alg
o
r
ith
m
s
.
I
n
HHO
a
n
d
W
OA
,
t
h
e
d
ec
is
io
n
v
ar
iab
les
ar
e
th
e
p
o
s
itio
n
s
o
f
t
h
e
h
a
w
k
s
/
w
h
ale
s
i
n
th
e
m
u
ltid
i
m
en
s
io
n
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s
ea
r
ch
s
p
ac
e.
E
ac
h
h
a
w
k
's
/
w
h
ale
'
s
p
o
s
iti
o
n
r
ep
r
esen
ts
a
p
o
te
n
tial
s
o
lu
t
io
n
(
f
ea
t
u
r
e
s
u
b
s
et)
,
w
h
er
e
ea
ch
d
i
m
e
n
s
io
n
o
f
t
h
i
s
p
o
s
itio
n
r
ep
r
esen
t
s
a
f
ea
tu
r
e.
T
h
is
in
it
ial
l
y
ca
n
b
e
r
ep
r
esen
ted
w
it
h
a
p
o
s
itio
n
v
ec
to
r
[
9
]
,
[
1
0
]
,
[
2
0
]
–
[
2
3
]
,
as (
2
)
an
d
(
3
)
:
Ve
c
to
r
=[
,
1
,
,
2
,
,
3
…
,
]
(
2
)
,
=
+(
+
)
*
R
a
n
d
(
)
(
3
)
w
h
er
e
,
is
th
e
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o
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itio
n
o
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i
h
a
w
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t
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d
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m
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io
n
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is
th
e
n
u
m
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er
o
f
f
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n
th
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d
ataset,
ar
e
th
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lo
w
er
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d
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p
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o
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o
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n
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ates
a
r
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m
n
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m
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ee
n
0
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d
1
.
T
h
e
v
alu
e
in
d
icate
s
wh
eth
er
a
f
ea
t
u
r
e
is
in
cl
u
d
ed
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th
e
s
u
b
s
et
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ein
g
co
n
s
id
er
ed
.
E
ac
h
p
o
s
itio
n
v
ec
to
r
r
e
p
r
esen
ts
a
f
ea
tu
r
e
s
u
b
s
et
(
s
o
lu
tio
n
)
,
an
d
th
ese
v
ec
to
r
s
ar
e
u
p
d
ated
u
s
in
g
th
e
e
x
p
lo
itatio
n
a
n
d
ex
p
lo
r
atio
n
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h
ase
s
o
f
t
h
e
al
g
o
r
ith
m
.
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n
th
e
HHO
e
x
p
lo
r
atio
n
p
h
ase,
t
h
e
d
ec
i
s
io
n
v
ar
iab
les ar
e
th
e
p
o
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itio
n
s
o
f
t
h
e
h
a
w
k
s
,
w
h
ic
h
ar
e
u
p
d
ated
u
s
i
n
g
(
4
)
[9
]
,
[
10
]
,
[
20
]
–
[
2
3
]
.
,
(
)
=
,
(
)
+
∗
(
,
+
,
(
)
)
(
4
)
W
h
er
e
,
(
)
is
th
e
u
p
d
ated
p
o
s
itio
n
o
f
th
e
i
h
a
w
k
i
n
th
e
d
d
i
m
e
n
s
io
n
,
,
(
)
is
th
e
cu
r
r
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t
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o
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itio
n
o
f
th
e
h
a
w
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r
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d
o
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n
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m
b
er
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0
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d
1
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,
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th
e
p
o
s
itio
n
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f
a
r
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d
o
m
l
y
s
elec
t
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h
a
w
k
f
r
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m
t
h
e
p
o
p
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latio
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A
s
f
o
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th
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ex
p
lo
itatio
n
p
h
ase,
t
h
e
p
o
s
itio
n
s
o
f
th
e
h
a
w
k
s
ar
e
u
p
d
ated
b
ased
o
n
th
e
lo
ca
tio
n
o
f
th
e
p
r
e
y
(
t
h
e
b
est s
o
lu
tio
n
f
o
u
n
d
s
o
f
ar
)
u
s
in
g
(
5
)
[9
]
,
[
10
]
,
[
20
]
–
[
2
3
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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t J
R
ec
o
n
f
i
g
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ab
le
&
E
m
b
ed
d
ed
Sy
s
t
I
SS
N:
2089
-
4864
E
n
h
a
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g
in
tr
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s
io
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d
etec
tio
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s
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w
ith
h
yb
r
id
HHO
-
WOA
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p
timiz
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d
…
(
Mo
s
l
eh
M.
A
b
u
a
l
h
a
j
)
521
,
(
)
=
,
+
∗
|
,
+
,
(
)
|
(
5
)
W
h
er
e
,
+
is
th
e
c
u
r
r
en
t
b
est
s
o
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tio
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f
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d
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y
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a
w
k
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n
t
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.
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n
t
h
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A
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w
h
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e
u
p
d
ated
u
s
i
n
g
(
6
)
[9
]
,
[
10
]
,
[
20
]
–
[
2
3
]
.
,
(
)
=
,
(
)
−
∗
|
∗
,
+
,
(
)
|
(
6
)
W
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,
(
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t
h
e
u
p
d
ated
p
o
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n
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w
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n
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d
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r
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o
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th
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o
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A
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es t
w
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eh
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io
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s
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n
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(
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an
d
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-
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m
et
h
o
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(
8
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9
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[
10
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,
[
20
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–
[
2
3
]
.
,
(
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=
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(
7
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(
2
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(
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W
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an
d
l
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tan
ts
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n
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H
HO
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o
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ain
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.
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ain
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o
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d
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t
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o
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n
(
f
ea
t
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e)
,
as sh
o
w
n
in
(
9
)
[
9
]
,
[
10
]
,
[
20
]
–
[
2
3
]
.
,
=
{
,
,
>
,
,
>
,
,
ℎ
(
9
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An
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ai
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in
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ar
e
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r
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o
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o
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e
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ased
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n
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t
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o
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5
.
W
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en
,
>
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h
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esh
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,
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h
e
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t
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r
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s
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o
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ld
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e
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e
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's
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ted
to
1
.
Oth
er
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s
e,
t
h
e
f
ea
t
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r
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t
h
e
s
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o
it
'
s
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n
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te
d
to
0
.
T
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e
f
ea
t
u
r
es
ar
e
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ted
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a
b
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3.
RE
SU
L
T
S AN
D
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SCU
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N
T
h
e
co
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f
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(
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allo
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ac
cu
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s
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m
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t
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ac
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th
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b
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f
attac
k
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t
h
at
w
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f
o
r
ec
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.
In
(
1
0
)
c
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ld
b
e
em
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ca
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f
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(
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m
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m
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n
.
RE
F
E
R
E
NC
E
S
[
1
]
B
.
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o
,
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2
]
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[
5
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D
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.
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ma
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[
6
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.
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,
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