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4
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9
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[
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1
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d
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n
[
1
9
]
.
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n
[
2
0
]
,
w
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w
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N
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KD
D
.
I
n
[
2
0
]
,
o
n
l
y
D
D
OS
at
t
a
c
k
i
n
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w
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t
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h
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k
s
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I
n
[
2
1
]
,
e
f
f
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c
i
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D
S
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h
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v
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s
e
v
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a
l
r
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s
e
a
r
c
h
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s
w
o
r
k
[
2
2
-
2
4
]
h
a
v
e
b
e
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n
c
o
n
d
u
c
t
e
d
i
n
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w
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o
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k
l
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s
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a
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e
t
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i
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n
.
2.
RE
S
E
ARCH
M
E
T
H
O
D
I
n
th
e
cu
r
r
en
t
wo
r
k
,
th
e
r
esu
lt
s
f
r
o
m
s
ev
er
al
class
if
ier
s
wer
e
co
m
b
in
ed
u
s
in
g
m
ajo
r
ity
v
o
tin
g
-
b
ased
en
s
em
b
le
m
eth
o
d
to
im
p
r
o
v
e
th
e
d
etec
tio
n
r
ate.
Dec
is
io
n
tr
ee
was
u
s
e
d
to
id
en
tify
s
ig
n
if
ican
t
attr
ib
u
tes
s
o
th
at
th
e
tr
ain
in
g
tim
e
an
d
th
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co
m
p
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tatio
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co
m
p
lex
ity
o
f
th
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d
ata
co
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ld
b
e
m
in
im
ize
d
.
T
h
e
d
ata
was
th
en
class
if
ied
as
n
o
r
m
al
o
r
i
n
tr
u
s
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u
s
in
g
o
n
e
is
to
m
a
n
y
a
p
p
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ac
h
es,
an
d
PS
O
was
u
s
ed
f
o
r
o
p
tim
izatio
n
.
NSL
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KDD
d
ataset
was
em
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lo
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to
ass
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m
o
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p
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ce
.
A
g
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r
am
ewo
r
k
f
o
r
en
s
em
b
le
-
b
ased
I
DS h
as b
ee
n
p
r
esen
ted
in
Fig
u
r
e1
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
1
6
9
3
-
6
9
3
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T
E
L
KOM
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KA
T
elec
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m
m
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n
C
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m
p
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t E
l Co
n
tr
o
l
,
Vo
l.
19
,
No
.
2
,
Ap
r
il 2
0
2
1
:
6
6
4
-
671
666
Fig
u
r
e
1
.
Sy
s
tem
f
r
a
m
ewo
r
k
2
.
1
.
Da
t
a
s
et
a
nd
pre
-
pro
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s
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ing
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n
th
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p
r
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p
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o
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el,
NSL
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KDD
h
as
b
ee
n
em
p
lo
y
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d
,
co
n
s
is
ts
o
f
1
4
8
5
1
7
r
ec
o
r
d
s
,
wh
ich
is
p
ar
titi
o
n
ed
as
tr
ain
in
g
(
1
2
5
9
7
3
r
ec
o
r
d
s
)
a
n
d
test
d
ataset
(
2
2
5
4
4
r
ec
o
r
d
s
)
[
2
5
]
.
I
n
th
e
cu
r
r
e
n
t
wo
r
k
,
th
e
d
ataset
was
p
ar
titi
o
n
ed
in
to
f
iv
e
g
r
o
u
p
s
,
an
d
ea
c
h
o
n
e
o
f
th
em
was
tr
ain
ed
/tes
ted
u
s
in
g
d
if
f
er
e
n
t
class
if
ier
s
.
Fin
al
r
esu
lts
w
er
e
o
b
tain
ed
u
s
in
g
m
ajo
r
ity
v
o
tin
g
.
Pre
-
p
r
o
ce
s
s
in
g
is
a
cr
u
cial
s
tep
a
s
it
h
elp
s
in
th
e
r
em
o
v
al
o
f
s
tatis
t
ical
ir
r
eg
u
lar
ities
an
d
th
e
s
elec
tio
n
o
f
im
p
o
r
tan
t
f
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atu
r
es
f
r
o
m
th
e
d
ata.
T
h
o
s
e
f
ea
tu
r
es
th
at
ar
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in
cr
ed
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ly
co
r
r
elate
d
to
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is
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tio
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s
id
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to
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e
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t,
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d
in
th
e
cu
r
r
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n
t
wo
r
k
,
d
ec
is
io
n
tr
ee
h
as
b
ee
n
u
s
ed
f
o
r
f
ea
tu
r
e
s
elec
tio
n
.
Prio
r
s
tu
d
ies
in
th
is
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ield
in
d
icate
th
at
a
p
r
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p
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s
et
o
f
attr
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u
tes
ca
n
en
h
an
ce
s
y
s
te
m
p
er
f
o
r
m
an
ce
b
y
r
e
d
u
cin
g
th
e
tr
ain
in
g
tim
e
[
2
6
]
.
T
h
e
d
atas
et
was
in
itially
p
ar
titi
o
n
ed
in
to
s
u
b
s
ets,
an
d
th
e
ir
r
elev
an
t f
ea
tu
r
es p
r
esen
t in
NSL
-
KDD
wer
e
r
em
o
v
ed
.
A
m
o
n
g
4
1
attr
ib
u
tes,
o
n
ly
ten
m
o
s
t
s
ig
n
if
ican
t
f
ea
t
u
r
es
wer
e
tak
en
in
t
o
ac
co
u
n
t
d
e
p
en
d
in
g
o
n
wh
ich
th
e
m
o
d
el
was
f
u
r
th
er
tr
ain
e
d
/
test
ed
.
T
h
e
d
ec
is
io
n
t
r
ee
s
elec
ts
th
e
b
est
attr
ib
u
tes
f
r
o
m
a
g
i
v
en
s
et,
ac
co
r
d
i
n
g
to
wh
ic
h
th
e
d
ata
is
p
ar
titi
o
n
ed
in
to
class
es.
Mo
r
eo
v
er
,
g
en
e
r
aliza
tio
n
ac
cu
r
ac
y
an
d
ex
ce
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n
t
r
ea
l
-
tim
e
p
e
r
f
o
r
m
an
ce
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f
d
ec
is
io
n
tr
ee
s
ca
n
b
e
u
s
ed
to
d
etec
t n
ew
attac
k
s
[
2
7
]
.
2
.
2
.
Cla
s
s
if
iers
In
d
iv
id
u
al
class
if
ier
s
th
at
ar
e
u
s
ed
to
ass
ess
th
e
m
o
d
el
p
e
r
f
o
r
m
a
n
ce
h
a
v
e
th
ei
r
f
laws
t
h
at
lead
t
o
co
m
p
r
o
m
is
e
th
e
p
er
f
o
r
m
a
n
ce
o
f
th
e
s
y
s
tem
.
T
h
ese
is
s
u
es
n
ee
d
to
b
e
ad
d
r
ess
ed
s
o
th
at
th
e
s
ec
u
r
ity
o
f
th
e
s
y
s
tem
is
n
o
t
co
m
p
r
o
m
is
ed
,
an
d
m
o
r
e
r
eliab
le
s
er
v
ices
a
r
e
p
r
o
v
id
ed
t
o
th
e
u
s
er
s
.
Fo
r
th
is
p
u
r
p
o
s
e,
th
e
en
s
em
b
le
-
b
ased
a
p
p
r
o
ac
h
ca
n
b
e
em
p
lo
y
e
d
,
wh
ic
h
is
a
c
o
m
b
in
atio
n
o
f
in
d
iv
id
u
al
class
if
ier
s
an
d
is
a
m
o
r
e
f
lex
ib
le
an
d
p
o
wer
f
u
l
to
o
l
as
f
ar
as
d
etec
tio
n
o
f
n
etwo
r
k
in
tr
u
s
io
n
is
co
n
ce
r
n
ed
.
An
ex
ce
ll
en
t
en
s
em
b
le
is
th
e
o
n
e
th
at
m
a
x
im
izes
th
e
d
if
f
e
r
en
ce
b
etwe
en
t
h
e
b
ase
class
if
ier
,
an
d
t
h
is
ca
n
b
e
ac
h
iev
ed
b
y
d
iv
id
in
g
th
e
d
ataset
in
to
s
u
b
s
ets [
2
8
]
.
T
h
e
p
r
o
ce
s
s
u
s
ed
in
th
e
m
o
d
el
ca
n
b
e
o
u
tlin
ed
in
th
e
s
u
b
s
eq
u
en
t
s
tep
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KOM
NI
KA
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l Co
n
tr
o
l
A
n
en
s
emb
le
b
a
s
ed
a
p
p
r
o
a
ch
f
o
r
effec
tive
in
tr
u
s
io
n
d
etec
tio
n
u
s
in
g
ma
jo
r
ity
vo
tin
g
(
A
lw
i M
.
B
a
mh
d
i
)
667
−
Div
id
e
th
e
d
ataset
in
to
s
ev
er
al
d
atasets
s
o
th
a
t
th
e
d
if
f
er
en
ce
b
etwe
en
th
e
v
a
r
io
u
s
b
a
s
e
class
if
ier
s
is
m
ax
im
ized
,
an
d
th
u
s
,
a
b
etter
en
s
em
b
le
ca
n
b
e
f
o
r
m
e
d
.
−
C
o
m
b
in
e
th
e
r
esu
lts
o
f
b
ase
class
if
ier
s
af
ter
co
m
p
letio
n
o
f
th
e
tr
ain
in
g
p
h
ase
s
o
th
at
th
e
f
in
al
class
if
ier
i
s
o
b
tain
ed
.
−
T
h
e
o
u
tp
u
t
o
b
tain
ed
in
t
h
e
tr
ain
in
g
p
h
ase
is
th
en
u
s
ed
to
test
th
e
d
ata,
an
d
ac
co
r
d
in
g
l
y
,
th
e
d
ata
ca
n
b
e
class
if
ied
as n
o
r
m
al
o
r
in
tr
u
s
iv
e.
2
.
2
.
1
.
S
up
po
rt
v
ec
t
o
r
m
a
chi
ne
(
SVM
)
SVM
was
p
r
o
p
o
s
ed
b
y
Vap
n
i
k
(
1
9
9
5
)
[
2
9
]
.
I
t
is
wid
ely
u
s
ed
to
s
o
lv
e
r
eg
r
ess
io
n
an
d
cla
s
s
if
icatio
n
p
r
o
b
lem
s
b
y
cr
ea
tin
g
a
n
u
m
b
e
r
o
f
h
y
p
e
r
-
p
lan
es,
an
d
am
o
n
g
th
em
,
th
e
o
p
tim
al
h
y
p
er
-
p
la
n
e
is
ch
o
s
en
b
ased
o
n
th
e
m
ax
im
u
m
s
ep
ar
atio
n
b
etwe
en
th
e
class
es.
SVM
ca
n
ef
f
ec
tiv
ely
b
e
u
s
ed
f
o
r
th
e
cl
ass
if
icatio
n
o
f
b
o
th
lin
ea
r
an
d
n
o
n
-
lin
ea
r
d
ata
[
3
0
]
.
I
n
c
u
r
r
en
t
wo
r
k
,
r
a
d
ial
lin
ea
r
b
asis
k
er
n
el
(
R
B
F)
f
u
n
c
tio
n
was
u
s
ed
as
i
t
wo
r
k
s
well
f
o
r
th
e
n
o
n
-
lin
ea
r
d
ata.
R
B
F
u
s
e
s
g
am
m
a
p
ar
am
eter
to
class
if
y
th
e
d
ata
wh
ich
in
th
e
cu
r
r
en
t
wo
r
k
is
ca
lcu
lated
as th
e
r
ec
ip
r
o
ca
l
o
f
th
e
f
ea
t
u
r
es.
2
.
2
.
2
.
R
a
nd
o
m
decisi
o
n f
o
re
s
t
s
(
RF
)
R
F
i
s
a
co
m
b
in
atio
n
o
f
d
ec
is
io
n
tr
ee
s
th
at
is
u
s
ed
to
b
o
o
s
t
th
e
ef
f
ec
tiv
en
ess
o
f
th
e
m
o
d
el.
I
n
R
F,
b
ag
g
in
g
is
em
p
lo
y
e
d
to
s
p
lit
t
h
e
d
ata
in
to
v
ar
io
u
s
s
u
b
s
ets,
an
d
th
en
d
ec
is
io
n
tr
ee
s
ar
e
co
n
s
tr
u
cted
u
s
in
g
th
ese
s
u
b
s
ets
[
3
1
]
.
R
F
d
o
es
n
o
t
s
u
f
f
er
f
r
o
m
o
v
er
-
f
itti
n
g
p
r
o
b
lem
an
d
h
as
lo
w
class
if
icatio
n
er
r
o
r
s
[
3
2
]
.
B
o
o
ts
tr
ap
s
am
p
les
f
r
o
m
th
e
d
ataset
wer
e
u
s
ed
to
co
n
s
tr
u
ct
in
d
iv
id
u
al
tr
ee
s
in
th
e
f
o
r
est.
Gin
i
im
p
u
r
ity
m
ea
s
u
r
e
was
ap
p
lied
to
c
h
o
o
s
e
th
e
b
est
n
o
d
e
f
o
r
th
e
s
p
lit
an
d
m
ax
im
u
m
n
u
m
b
er
o
f
tr
ee
s
wer
e
s
et
to
2
5
b
ased
o
n
wh
ic
h
th
e
p
er
f
o
r
m
an
ce
was e
v
alu
ated
.
2
.
2
.
3
.
K
-
nea
re
s
t
neig
hb
o
r
(
K
NN)
KNN
is
a
s
u
p
er
v
is
ed
class
if
ie
r
.
I
n
itially
,
th
e
v
alu
e
o
f
K
is
s
elec
ted
,
an
d
th
e
n
E
u
clid
ea
n
d
is
tan
ce
is
co
m
p
u
ted
am
o
n
g
th
e
v
ar
i
o
u
s
d
ata
p
o
in
ts
b
ased
o
n
wh
ich
th
e
d
ata
is
d
iv
id
ed
i
n
to
K
-
n
u
m
b
er
o
f
clu
s
ter
s
.
T
h
e
d
ata
p
o
in
ts
th
at
h
av
e
a
m
in
i
m
u
m
d
is
tan
ce
b
etwe
en
th
em
ar
e
p
lace
d
in
o
n
e
clu
s
ter
as
th
ey
h
av
e
s
im
ilar
ch
ar
ac
ter
is
tics
[
3
3
]
.
KNN
is
e
asy
to
im
p
lem
en
t
a
n
d
wo
r
k
s
well
f
o
r
lar
g
e
d
atasets
[
3
4
]
.
I
n
th
e
cu
r
r
en
t
wo
r
k
,
th
e
m
o
d
el
p
er
f
o
r
m
a
n
ce
was
ev
alu
ated
o
n
K=
5
a
n
d
th
e
E
u
clid
ea
n
d
is
tan
ce
m
ea
s
u
r
e
w
as
u
s
ed
to
f
in
d
th
e
n
ea
r
est n
eig
h
b
o
r
s
.
Prio
r
to
th
is
,
th
e
d
ataset
was n
o
r
m
alize
d
t
o
r
ed
u
ce
v
ar
iatio
n
s
am
o
n
g
v
ar
io
u
s
attr
ib
u
tes.
2
.
2
.
4
.
P
a
rt
icle
s
wa
r
m
o
ptim
i
za
t
io
n
(
P
SO
)
PS
O
was
p
r
esen
ted
b
y
Ken
n
e
d
y
an
d
E
b
er
h
a
r
t
[
3
5
]
.
I
t
m
im
i
cs
th
e
b
eh
a
v
io
r
o
f
a
f
lo
c
k
o
f
b
ir
d
s
an
d
u
s
es
th
e
s
am
e
p
r
in
cip
le
to
d
i
r
ec
t
th
e
p
ar
ticles
to
e
x
p
lo
r
e
th
e
o
p
tim
al
g
l
o
b
al
s
o
lu
tio
n
.
PS
O
ar
e
co
m
p
ar
ativ
ely
ea
s
ier
to
im
p
lem
en
t a
s
co
m
p
ar
ed
to
g
en
etic
alg
o
r
ith
m
s
as th
ey
d
o
n
o
t h
av
e
ev
o
lu
tio
n
ar
y
o
p
er
ato
r
s
[
1
1
,
3
6
]
.
I
n
th
e
cu
r
r
en
t
wo
r
k
,
o
n
e
o
f
tr
ai
n
in
g
d
ata
s
u
b
s
et
co
m
p
r
is
in
g
o
f
4
4
5
5
in
s
tan
ce
s
was
u
s
ed
as
a
n
in
p
u
t
to
th
e
PS
O
an
d
th
e
weig
h
ts
in
f
o
r
m
o
f
a
n
ar
r
ay
wer
e
o
b
tain
ed
a
n
d
ac
c
o
r
d
in
g
ly
th
e
d
ata
was c
lass
if
ied
.
2
.
2
.
5
.
E
x
t
ra
-
t
re
e
cla
s
s
if
ier
(
E
T
C)
E
T
C
is
an
e
n
s
em
b
le
-
b
ased
a
p
p
r
o
ac
h
wh
ic
h
is
p
r
im
ar
ily
b
ased
o
n
d
ec
is
io
n
tr
ee
s
an
d
a
r
e
u
s
ed
to
in
d
u
ce
v
ar
iatio
n
s
b
y
c
h
an
g
i
n
g
th
e
a
p
p
r
o
ac
h
in
wh
ich
tr
ee
s
a
r
e
b
u
ilt.
Owin
g
to
its
r
an
d
o
m
i
ze
d
ch
ar
ac
te
r
is
tics
,
th
ey
ar
e
c
o
m
p
u
tatio
n
all
y
less
ex
p
en
s
iv
e
co
m
p
ar
e
d
to
t
h
at
o
f
r
an
d
o
m
f
o
r
est
an
d
,
th
u
s
,
ca
n
b
e
ef
f
icien
tly
u
s
ed
f
o
r
i
n
tr
u
s
io
n
d
etec
tio
n
.
I
n
E
T
C
,
s
q
u
ar
e
r
o
o
t
o
f
th
e
to
tal
n
u
m
b
er
o
f
f
ea
tu
r
es
was
tak
en
in
to
co
n
s
id
er
ati
o
n
t
o
ch
o
o
s
e
th
e
b
est n
o
d
e
f
o
r
s
p
lit
wh
er
ea
s
Gin
i im
p
u
r
ity
m
ea
s
u
r
e
was u
s
ed
to
d
eter
m
in
e
t
h
e
q
u
ality
o
f
th
e
s
am
e.
2
.
2
.
6
.
M
ultila
y
er
perc
ept
ro
n (
M
L
P
)
I
t
is
a
n
eu
r
al
n
etwo
r
k
co
m
p
r
is
in
g
o
f
in
te
r
co
n
n
ec
ted
n
o
d
es
o
r
n
eu
r
o
n
s
th
at
ar
e
u
s
ed
to
m
ap
th
e
in
p
u
ts
with
th
e
co
r
r
esp
o
n
d
in
g
o
u
tp
u
t
v
ec
to
r
s
.
T
h
e
n
o
d
es
h
a
v
e
weig
h
ts
ass
ig
n
ed
with
th
e
m
,
an
d
th
e
o
u
tp
u
t
is
co
m
p
u
ted
as
th
e
f
u
n
ctio
n
o
f
t
h
e
s
u
m
m
atio
n
o
f
in
p
u
ts
to
a
n
o
d
e,
s
u
b
jecte
d
t
o
a
n
o
n
-
lin
e
ar
f
u
n
ctio
n
[
3
7
]
.
As
n
eu
r
al
n
etwo
r
k
s
h
a
v
e
f
lex
i
b
le
tim
e
-
d
elay
in
g
ab
ilit
ies,
th
e
y
ca
n
b
e
ef
f
icien
tly
u
s
ed
f
o
r
n
etwo
r
k
in
tr
u
s
io
n
d
etec
tio
n
as
th
ey
d
ec
r
ea
s
e
f
al
s
e
alar
m
r
ate.
T
o
ac
h
iev
e
th
e
d
esire
d
r
esu
lts
,
a
r
ec
tifie
d
li
n
ea
r
u
n
it
ac
tiv
atio
n
f
u
n
ctio
n
was
u
s
ed
in
th
e
h
i
d
d
en
lay
er
.
Mo
r
e
o
v
er
,
a
r
eg
u
lar
i
za
tio
n
v
alu
e
o
f
0
.
0
0
0
1
was
ad
d
ed
to
lo
s
s
f
u
n
ctio
n
to
p
r
ev
e
n
t m
o
d
el
o
v
er
f
itti
n
g
.
2
.
2
.
7
.
M
a
j
o
rit
y
v
o
t
ing
I
n
m
ajo
r
ity
v
o
ti
n
g
,
th
e
d
atase
t
is
p
ar
titi
o
n
in
to
s
ev
er
al
s
u
b
-
s
ets
an
d
v
ar
io
u
s
class
if
ier
s
ar
e
u
s
ed
to
tr
ain
th
e
s
am
e,
th
er
eb
y
ac
co
m
p
lis
h
in
g
th
e
lear
n
in
g
p
h
ase.
T
h
e
f
in
al
r
esu
lts
o
f
allo
ca
tin
g
a
lab
el
to
th
e
s
am
p
le
d
ep
en
d
s
o
n
th
e
m
ax
im
u
m
n
u
m
b
er
o
f
v
o
tes
r
ec
eiv
e
d
i
n
f
a
v
o
r
o
f
a
p
a
r
ticu
lar
class
o
b
tain
ed
f
r
o
m
d
if
f
e
r
en
t
class
if
ier
s
.
I
n
th
e
cu
r
r
en
t
wo
r
k
,
weig
h
ted
m
ajo
r
ity
v
o
tin
g
was
u
s
ed
to
cla
s
s
if
y
th
e
d
at
a
wh
er
e
PS
O
wa
s
em
p
lo
y
ed
f
o
r
ass
ig
n
in
g
weig
h
ts
to
v
ar
io
u
s
class
if
ier
s
.
Mo
r
e
o
v
er
,
p
r
io
r
s
tu
d
ies
o
n
I
D
S
[
3
8
,
3
9
]
h
av
e
r
ev
ea
le
d
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
1
6
9
3
-
6
9
3
0
T
E
L
KOM
NI
KA
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l Co
n
tr
o
l
,
Vo
l.
19
,
No
.
2
,
Ap
r
il 2
0
2
1
:
6
6
4
-
671
668
th
at
v
o
tin
g
ca
n
b
e
u
s
ed
to
i
m
p
r
o
v
e
th
e
p
er
f
o
r
m
an
ce
o
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An
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
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:
1
6
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2
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Ap
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0
2
1
:
6
6
4
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671
670
RE
F
E
R
E
NC
E
S
[
1
]
Y.
Zh
o
u
,
G
.
Ch
e
n
g
,
S
.
Jia
n
g
a
n
d
M
.
Da
i,
“
B
u
i
ld
i
n
g
a
n
e
fficie
n
t
i
n
tru
si
o
n
d
e
tec
ti
o
n
sy
ste
m
b
a
se
d
o
n
fe
a
tu
re
s
se
lec
ti
o
n
a
n
d
e
n
se
m
b
le cla
ss
ifi
e
r,
”
Co
mp
u
ter
n
e
two
rk
s
,
v
o
l.
1
7
4
,
p
p
.
1
-
1
7
,
2
0
2
0
.
[2
]
L.
L
v
,
W.
Wan
g
,
Z.
Z
h
a
n
g
a
n
d
X.
Li
u
,
“
A n
o
v
e
l
i
n
tru
si
o
n
d
e
tec
ti
o
n
s
y
ste
m
b
a
se
d
o
n
o
p
t
ima
l
h
y
b
ri
d
k
e
rn
e
l
e
x
trem
e
lea
rn
in
g
m
a
c
h
in
e
,
”
Kn
o
wled
g
e
-
b
a
se
d
sy
ste
ms
,
v
o
l
.
1
9
5
,
p
p
.
1
-
1
7
,
2
0
2
0
.
[3
]
H.
Ala
z
z
a
m
,
A.
S
h
a
rieh
a
n
d
K.E
.
S
a
b
ri,
“
A
fe
a
tu
re
se
lec
ti
o
n
a
l
g
o
rit
h
m
fo
r
i
n
tru
si
o
n
d
e
tec
ti
o
n
s
y
ste
m
b
a
se
d
o
n
p
ig
e
o
n
i
n
sp
ired
o
p
ti
m
ize
r,
”
Exp
e
rt sy
ste
ms
wit
h
a
p
p
li
c
a
ti
o
n
s
,
v
o
l
.
1
4
8
,
p
p
.
1
-
1
4
,
2
0
2
0
.
[4
]
H.
Ra
jad
u
ra
i
a
n
d
U.D.
G
a
n
d
h
i,
“
A
sta
c
k
e
d
e
n
se
m
b
le
lea
rn
in
g
m
o
d
e
l
fo
r
i
n
tru
si
o
n
d
e
tec
ti
o
n
in
wir
e
les
s
n
e
two
rk
,
”
Ne
u
ra
l
c
o
mp
u
ti
n
g
a
n
d
a
p
p
li
c
a
t
io
n
s
,
p
p
.
1
-
9
,
2
0
2
0
.
[5
]
D.
G
.
Ka
li
ta,
V.
P
.
S
in
g
h
a
n
d
V.
Ku
m
a
r,
“
S
VM
Hy
p
e
r
-
p
a
ra
m
e
ters
o
p
ti
m
iza
ti
o
n
u
sin
g
m
u
lt
i
-
P
S
O
fo
r
in
tr
u
sio
n
d
e
tec
ti
o
n
,
”
S
o
c
ia
l
n
e
tw
o
rk
in
g
a
n
d
c
o
m
p
u
t
a
ti
o
n
a
l
in
telli
g
e
n
c
e
.
L
e
c
tu
re
n
o
tes
in
n
e
two
rk
s
a
n
d
sy
ste
ms
,
v
o
l
.
1
0
0
,
p
p
.
2
2
7
-
2
4
2
,
2
0
2
0
.
[6
]
F
.
S
a
l
o
,
A.
B
.
Na
ss
if
a
n
d
A.
Esse
x
,
“
Dim
e
n
sio
n
a
li
ty
re
d
u
c
ti
o
n
wi
th
IG
-
P
CA
a
n
d
e
n
se
m
b
le
c
las
sifier
fo
r
n
e
two
r
k
in
tru
si
o
n
d
e
tec
ti
o
n
,
”
Co
m
p
u
ter
N
e
two
rk
s
,
v
o
l
.
1
4
8
,
p
p
.
1
6
4
-
1
7
5
,
2
0
1
9
.
[7
]
M
.
S
a
fa
ld
i
n
,
M
.
Ota
ir
a
n
d
L
.
Ab
u
a
li
g
a
h
,
“
Im
p
ro
v
e
d
b
in
a
ry
g
ra
y
wo
lf
o
p
ti
m
ize
r
a
n
d
S
VM
f
o
r
i
n
tr
u
sio
n
d
e
tec
ti
o
n
sy
ste
m
in
wire
les
s se
n
so
r
n
e
tw
o
r
k
s,”
J
o
u
r
n
a
l
o
f
a
mb
ie
n
t
i
n
telli
g
e
n
c
e
a
n
d
h
u
m
a
n
ize
d
c
o
mp
u
ti
n
g
,
p
p
.
1
-
18
,
2
0
2
0
.
[8
]
M
.
A.
F
e
rra
g
,
L.
M
a
g
lara
s,
A.
A
h
m
im
,
M
.
De
rd
o
u
r
a
n
d
H.
Ja
n
ick
e
,
“
RDTIDS
:
R
u
les
a
n
d
d
e
c
isi
o
n
tree
-
b
a
se
d
in
tru
si
o
n
d
e
tec
ti
o
n
sy
ste
m
fo
r
in
t
e
rn
e
t
o
f
t
h
in
g
s n
e
tw
o
rk
s,”
F
u
t
u
re
in
ter
n
e
t
,
p
p
.
1
-
14
,
2
0
2
0
.
[9
]
I.
H.
S
a
rk
e
r,
Y.B.
Ab
u
sh
a
rk
,
F
.
Also
h
a
m
i
a
n
d
A.I
.
Kh
a
n
,
“
In
tru
DTre
e
:
A
m
a
c
h
in
e
lea
rn
i
n
g
b
a
se
d
c
y
b
e
r
-
se
c
u
rit
y
in
tru
si
o
n
d
e
tec
ti
o
n
m
o
d
e
l,
”
S
y
mm
e
try
,
p
p
.
1
-
15
,
2
0
2
0
.
[1
0
]
K.
Ch
ish
o
lm,
C.
Ya
k
o
p
c
ic,
M
.
S
.
Ala
m
a
n
d
T
.
M
.
Tah
a
,
“
M
u
lt
i
lay
e
r
p
e
rc
e
p
tro
n
a
l
g
o
rit
h
m
s
fo
r
n
e
t
wo
rk
i
n
tru
si
o
n
d
e
tec
ti
o
n
o
n
p
o
rtab
le
l
o
w
p
o
we
r
h
a
rd
wa
re
,
”
in
c
o
m
p
u
t
in
g
a
n
d
c
o
mm
u
n
ic
a
ti
o
n
w
o
rk
sh
o
p
a
n
d
c
o
n
fer
e
n
c
e
,
2
0
2
0
.
CCW
C
2
0
2
0
.
T
e
n
th
An
n
u
a
l
IEE
E
,
Las
Ve
g
a
s,
USA,
2
0
2
0
,
p
p
.
9
0
1
-
906
.
[1
1
]
T.
B.
P
ra
sa
d
,
P
.
S
.
P
ra
sa
d
a
n
d
K.
P
.
Ku
m
a
r,
“
An
i
n
tru
si
o
n
d
e
tec
ti
o
n
sy
ste
m
so
ftwa
re
p
r
o
g
ra
m
u
si
n
g
KN
N
n
e
a
re
s
t
n
e
ig
h
b
o
u
rs ap
p
ro
a
c
h
,
”
I
n
ter
n
a
ti
o
n
a
l
jo
u
r
n
a
l
o
f
sc
ien
c
e
re
se
a
rc
h
a
n
d
i
n
n
o
v
a
ti
o
n
e
n
g
in
e
e
rin
g
,
p
p
.
1
-
6
,
2
0
2
0
.
[1
2
]
S
.
Ra
jag
o
p
a
l
,
P
.
P
.
Ku
n
d
a
p
u
r
a
n
d
K.S
.
Ha
re
e
sh
a
,
“A
sta
c
k
in
g
e
n
se
m
b
le
fo
r
n
e
two
rk
i
n
tru
si
o
n
d
e
tec
ti
o
n
u
si
n
g
h
e
tero
g
e
n
e
o
u
s
d
a
tas
e
ts,
”
S
e
c
u
re
Co
mm
u
n
ica
ti
o
n
Ne
two
rk
s
,
p
p
.
1
-
9
,
2
0
2
0
.
[1
3
]
W.
Wa
n
g
,
Y.
Li
,
X.
Wan
g
,
J.
Li
u
a
n
d
X
.
Z
h
a
n
g
,
“
De
tec
ti
n
g
a
n
d
r
o
id
m
a
li
c
io
u
s
a
p
p
s
a
n
d
c
a
teg
o
rizi
n
g
b
e
n
i
g
n
a
p
p
s
with
e
n
se
m
b
le o
f
c
las
sifiers
,
”
Fu
t
u
re
Ge
n
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p
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0
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0
.
[1
5
]
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M
a
,
F
.
Wan
g
,
J.
Ch
e
n
g
,
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Yu
a
n
d
X.
Ch
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n
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h
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.
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Li
,
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n
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8
]
A.
J.
M
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l
.
,
“
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p
.
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6
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-
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6
8
.
[1
9
]
N.F
.
Ha
q
,
A.R.
On
i
k
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n
d
F
.
M
.
S
h
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h
,
“
An
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ly
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,
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AI
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telli
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n
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.
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0
]
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l.
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“
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ti
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ra
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1
]
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.
M
.
H
.
Ba
m
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k
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l.
,
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9
9
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p
.
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6
.
[2
2
]
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.
Laq
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,
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E
.
Ya
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.
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AN
ET
,
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ter
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ti
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l
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o
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rn
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f
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lec
trica
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n
d
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m
p
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ter
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g
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g
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l.
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0
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p
.
2
7
0
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-
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0
.
[2
3
]
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F
a
rn
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h
a
,
M
.
Ra
h
m
a
n
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d
M
.
T.
Ah
m
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d
,
“
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,
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ter
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ti
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rn
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c
trica
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mp
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ter
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g
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),
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l.
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0
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n
o
.
5
,
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p
.
5
5
1
4
-
5
5
2
5
,
2
0
2
0
.
[2
4
]
I.
Ab
ra
r
,
e
t
a
l.
,
“A
m
a
c
h
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lea
rn
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s
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m
o
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NSL
-
KD
D
d
a
t
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se
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in
sm
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rt
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tro
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ics
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o
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,
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0
.
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0
.
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d
ia,
2
0
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0
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p
.
9
1
9
-
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2
4
.
[2
5
]
H.
Ch
a
e
,
B.
Jo
,
S
.
Ch
o
i
a
n
d
T
.
P
a
rk
,
“
F
e
a
tu
re
se
lec
ti
o
n
fo
r
in
tr
u
si
o
n
d
e
tec
ti
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si
n
g
NSL
-
KDD,
”
Rec
e
n
t
Ad
v
a
n
c
e
s
in
Co
m
p
u
t
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ien
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e
,
p
p
.
1
8
4
-
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7
,
2
0
1
3
.
[2
6
]
N.K.
Ka
n
a
k
a
ra
jan
a
n
d
K.
M
u
n
i
a
sa
m
y
,
“
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p
ro
v
in
g
th
e
a
c
c
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ra
c
y
o
f
in
tr
u
sio
n
d
e
tec
ti
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n
u
sin
g
G
AR
-
fo
re
st
with
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a
tu
re
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lec
ti
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n
,
”
i
n
Pr
o
c
e
e
d
in
g
s
o
f
4
th
In
ter
n
a
ti
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n
a
l
C
o
n
fer
e
n
c
e
o
n
Fro
n
ti
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rs
in
In
telli
g
e
n
t
Co
mp
u
ti
n
g
:
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h
e
o
ry
Ap
p
li
c
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ti
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n
s.
Ad
v
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n
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s in
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n
telli
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n
d
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ti
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0
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6
,
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p
.
5
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4
7
.
[2
7
]
S
.
P
e
d
d
a
b
a
c
h
ig
a
ri,
A.
Ab
ra
h
a
m
,
C.
G
ro
sa
n
a
n
d
J.
Th
o
m
a
s
,
“
M
o
d
e
li
n
g
in
tru
si
o
n
d
e
tec
ti
o
n
sy
ste
m
u
sin
g
h
y
b
ri
d
in
telli
g
e
n
t
sy
ste
m
s,”
J
o
u
r
n
a
l
o
f
N
e
two
rk
a
n
d
Co
m
p
u
ter
A
p
p
l
ica
ti
o
n
s
,
v
o
l
.
3
0
,
p
p
.
1
1
4
-
1
3
2
,
2
0
0
7
.
[2
8
]
J.
G
u
,
L.
Wan
g
,
Y.
Ch
u
n
g
a
a
n
d
S
.
Wan
g
,
“
A
n
o
v
e
l
a
p
p
ro
a
c
h
t
o
in
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si
o
n
d
e
tec
ti
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n
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si
n
g
S
VM
e
n
se
m
b
le
with
fe
a
tu
re
a
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m
e
n
tatio
n
,
”
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o
mp
u
ter
a
n
d
se
c
u
rity
,
v
o
l.
8
6
,
p
p
.
5
3
-
6
2
,
2
0
1
9
.
[2
9
]
F
.
Ku
a
n
g
,
W.
X
u
a
n
d
S
.
Zh
a
n
g
,
“
A
n
o
v
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l
h
y
b
rid
KPCA
a
n
d
S
VM
with
G
A
m
o
d
e
l
fo
r
in
tr
u
sio
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d
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te
c
ti
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n
,
”
A
p
p
li
e
d
S
o
ft
C
o
mp
u
ti
n
g
,
v
o
l.
1
8
,
p
p
.
1
7
8
-
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8
4
,
2
0
1
4
.
Evaluation Warning : The document was created with Spire.PDF for Python.
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I.
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.
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sh
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ri,
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.
J.
Iq
b
a
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A.
Ra
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im
,
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P
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rfo
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[3
1
]
Q.
Zh
o
u
a
,
H.
Zh
o
u
a
a
n
d
T.
Li
b
,
“
Co
st
-
se
n
siti
v
e
fe
a
tu
re
se
lec
ti
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n
u
sin
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ra
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d
o
m
fo
re
s
t:
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in
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rm
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tu
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s,”
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n
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a
se
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sy
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l.
9
5
,
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p
.
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.
[3
2
]
N.
F
a
rn
a
a
z
a
n
d
M
.
A.
Ja
b
b
a
r,
“
Ra
n
d
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F
o
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M
o
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tru
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2742
.
B
I
O
G
RAP
H
I
E
S O
F
AUTH
O
RS
Dr
.
Alwi
M
.
Ba
m
h
d
i
,
is
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n
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ss
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tan
t
P
ro
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ra
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in
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rio
t
-
Watt
Un
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rsity
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UK
.
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re
se
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o
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Ad
Ho
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fo
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Abra
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rre
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.
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c
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c
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,
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f
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sh
m
ir.
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s
h
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s
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tec
h
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o
T
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.
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h
e
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S
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a
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g
a
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p
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e
,
Un
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o
f
Ka
sh
m
ir.
Earlier,
h
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d
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ll
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Re
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ICT
-
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o
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o
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m
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slim
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r
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a
n
d
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p
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Un
iv
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rsity
o
f
Ka
sh
m
ir.
His
b
a
sic
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se
a
r
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h
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Cry
p
to
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ra
p
h
y
&
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two
r
k
S
e
c
u
rit
y
;
a
n
d
In
ter
n
e
t
o
f
th
i
n
g
s (I
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