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v
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v
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s
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d
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s
tr
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ch
allen
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[
1
]
.
Ou
tco
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ass
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th
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,
co
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s
ec
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r
i
ty
p
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ac
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s
a
s
well
[
2
]
.
Sev
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[
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.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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Dete
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ased
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8
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So
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ase
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[
9
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.
O
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ased
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[
1
0
]
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T
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r
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−
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k
,
to
a
s
s
es
s
it
s
ef
f
ec
tiv
en
ess
in
ac
cu
r
ately
id
en
tify
in
g
v
ar
io
u
s
ty
p
es o
f
n
etwo
r
k
attac
k
s
.
−
I
d
en
tify
a
p
p
r
o
p
r
iate
ML
m
et
h
o
d
s
to
ef
f
ec
tiv
ely
d
etec
t n
etwo
r
k
s
ec
u
r
ity
in
cid
e
n
ts
in
W
SNs
.
T
h
is
r
esear
ch
in
tr
o
d
u
ce
s
an
i
n
n
o
v
ativ
e
ML
-
b
ased
f
r
am
ew
o
r
k
s
p
ec
ially
d
esig
n
e
d
to
d
et
ec
t
n
etwo
r
k
s
ec
u
r
ity
in
cid
en
ts
in
W
SN
s
.
Dis
tin
ct
ea
r
lies
t
ap
p
r
o
ac
h
es
th
at
f
o
cu
s
ed
o
n
c
r
y
p
to
g
r
ap
h
ic
s
o
lu
tio
n
s
o
r
r
u
le
-
b
ased
an
o
m
aly
d
etec
tio
n
m
eth
o
d
s
,
o
u
r
f
r
am
ewo
r
k
in
te
g
r
ates
th
o
r
o
u
g
h
f
ea
t
u
r
e
e
n
g
in
ee
r
in
g
,
in
clu
d
i
n
g
co
m
m
u
n
icatio
n
m
etr
ics,
e
n
v
ir
o
n
m
en
tal
d
ata,
an
d
en
e
r
g
y
co
n
s
u
m
p
tio
n
,
to
a
d
v
an
ce
th
e
d
etec
tio
n
ca
p
ab
ilit
ies.
T
h
e
r
esu
lts
o
f
th
is
s
tu
d
y
h
an
d
r
o
b
u
s
t
ev
id
en
ce
s
u
p
p
o
r
tin
g
th
e
ap
p
licatio
n
o
f
ML
tech
n
iq
u
e
s
f
o
r
ad
v
an
cin
g
th
e
s
ec
u
r
ity
o
f
W
SNs
.
T
h
i
s
s
o
lu
ti
o
n
co
u
ld
b
e
u
s
ed
b
y
n
etwo
r
k
ad
m
in
is
tr
ato
r
s
an
d
s
ec
u
r
ity
an
aly
s
ts
to
ac
cu
r
ately
d
etec
t v
ar
ied
n
etwo
r
k
s
ec
u
r
ity
in
cid
en
ts
in
W
SNs
.
T
h
e
f
o
llo
win
g
s
ec
tio
n
s
will illu
s
tr
ate
th
e
r
elev
an
ce
an
d
m
ea
n
in
g
o
f
o
u
r
w
o
r
k
.
T
h
e
p
a
p
er
is
s
tr
u
ctu
r
ed
as
f
o
llo
ws:
s
ec
tio
n
2
p
r
esen
t
s
m
ater
ials
an
d
m
eth
o
d
s
u
s
e
d
d
u
r
in
g
o
u
r
ex
p
er
im
en
tatio
n
to
g
ai
n
th
e
r
esu
lts
,
in
clu
d
in
g
th
e
d
ata
p
r
ep
r
o
ce
s
s
in
g
s
tep
s
,
f
ea
tu
r
e
en
g
in
ee
r
in
g
tech
n
iq
u
es,
an
d
th
e
ML
m
o
d
els
im
p
lem
en
ted
.
Sectio
n
3
d
is
cu
s
s
es
th
e
ex
p
er
im
en
tatio
n
an
d
r
esu
lts
,
ex
h
ib
itin
g
th
e
p
er
f
o
r
m
a
n
ce
m
etr
ics
o
f
th
e
d
ec
is
io
n
tr
e
e
(
DT
)
an
d
r
an
d
o
m
f
o
r
est
(
R
F)
m
o
d
els.
T
h
is
s
ec
tio
n
will
also
in
clu
d
e
a
co
m
p
ar
ativ
e
a
n
aly
s
is
to
em
p
h
asize
th
e
ac
cu
r
ac
y
an
d
r
o
b
u
s
tn
ess
o
f
o
u
r
p
r
o
p
o
s
ed
f
r
a
m
ewo
r
k
.
I
n
th
e
en
d
,
s
ec
tio
n
4
,
will
d
is
cu
s
s
th
e
in
f
er
e
n
ce
s
o
f
o
u
r
f
in
d
in
g
s
,
r
esu
lts
,
an
d
f
u
tu
r
e
r
esear
ch
d
ir
ec
t
io
n
s
,
h
ig
h
lig
h
tin
g
th
e
p
r
ac
tical
ap
p
licatio
n
s
an
d
p
o
te
n
tial
en
h
an
ce
m
e
n
ts
in
th
e
f
ield
o
f
W
SN secu
r
ity
.
2.
M
E
T
H
O
D
2
.
1
.
Study
a
re
a
T
h
e
r
esear
ch
aim
s
to
en
h
a
n
ce
cy
b
er
s
ec
u
r
ity
with
in
W
SNs
.
W
SN
s
ar
e
ess
en
tial
in
m
an
y
a
p
p
licatio
n
s
,
f
r
o
m
c
o
n
s
u
m
er
ap
p
lian
ce
s
to
in
d
u
s
tr
ial
s
y
s
tem
s
.
B
y
th
eir
cr
itical
p
u
r
p
o
s
e,
ass
u
r
in
g
r
o
b
u
s
t
s
ec
u
r
ity
m
ea
s
u
r
es
ag
ain
s
t
an
o
m
alies
an
d
cy
b
er
th
r
ea
ts
is
f
o
r
em
o
s
t.
T
h
is
s
tu
d
y
ad
v
an
ta
g
es
a
th
o
r
o
u
g
h
d
a
taset
d
er
iv
ed
f
r
o
m
s
im
u
lated
W
SN
en
v
ir
o
n
m
en
ts
[
1
1
]
,
[
1
2
]
t
o
m
ir
r
o
r
r
ea
l
n
etwo
r
k
b
e
h
av
io
r
s
an
d
p
o
s
s
ib
le
s
ec
u
r
ity
v
u
ln
er
ab
ilit
ies.
2
.
2
.
M
et
ho
d
T
h
is
p
ar
t
d
etails
th
e
m
eth
o
d
u
tili
ze
d
to
d
is
co
v
er
an
d
u
n
d
er
s
tan
d
tr
en
d
s
,
an
d
d
ata
b
ased
o
n
d
ataset.
An
ex
p
er
im
en
tal
m
eth
o
d
o
l
o
g
y
was
u
s
ed
wh
ich
in
v
o
lv
ed
th
r
ee
p
h
ases
:
ex
p
lo
r
ato
r
y
d
at
a
an
a
ly
s
i
s
(
E
DA)
,
Mo
d
elin
g
a
n
d
d
esig
n
,
an
d
e
x
p
er
im
en
ts
.
T
h
e
m
eth
o
d
o
lo
g
y
em
b
r
ac
es
an
E
DA
to
r
ev
ea
l
im
p
licit
p
atter
n
s
an
d
an
o
m
alies
with
in
th
e
n
etwo
r
k
d
ata,
d
ata
co
llectio
n
,
p
r
ep
r
o
c
ess
in
g
,
f
ea
tu
r
e
en
g
in
ee
r
in
g
,
m
o
d
el
tr
ain
in
g
,
a
n
d
ev
alu
atio
n
.
T
h
e
f
ir
s
t
p
a
r
t
f
o
c
u
s
es
o
n
u
n
d
er
s
tan
d
in
g
tr
en
d
s
,
an
d
d
ata
th
r
o
u
g
h
E
DA,
in
c
lu
d
in
g
tim
e
s
er
ies,
co
r
r
elatio
n
,
d
is
tr
ib
u
tio
n
,
attac
k
ty
p
e
an
aly
s
is
,
an
d
a
n
o
m
aly
d
etec
tio
n
.
T
h
e
s
ec
o
n
d
p
ar
t,
b
a
s
ed
o
n
th
e
in
s
ig
h
ts
f
r
o
m
E
DA,
in
v
o
l
v
es
th
e
d
esig
n
an
d
m
o
d
elin
g
o
f
th
e
ML
an
d
AI
m
o
d
els
f
o
r
W
SN.
T
h
is
u
n
d
er
s
tan
d
in
g
s
er
v
es
th
e
f
ea
tu
r
e
e
n
g
in
ee
r
in
g
p
h
ase,
wh
er
e
r
aw
d
ata
attr
ib
u
tes
ar
e
tr
an
s
f
o
r
m
e
d
in
to
f
ea
tu
r
es
b
e
n
ef
icial
to
m
ac
h
in
e
lear
n
in
g
al
g
o
r
ith
m
s
.
E
x
p
er
i
m
en
ts
wer
e
r
u
n
u
s
in
g
Go
o
g
le
C
o
lab
an
d
J
u
p
y
ter
No
te
b
o
o
k
en
v
ir
o
n
m
e
n
ts
,
b
en
ef
itin
g
th
eir
ca
p
ab
ilit
ies
f
o
r
d
ata
an
aly
s
is
,
v
is
u
aliza
tio
n
,
an
d
ML
m
o
d
el
d
e
v
elo
p
m
e
n
t.
A
th
o
r
o
u
g
h
d
ataset
o
b
tain
ed
f
r
o
m
s
im
u
lated
W
SN
en
v
ir
o
n
m
en
ts
was u
s
ed
,
in
cl
u
d
in
g
s
ev
e
r
al
f
ea
tu
r
es
(
tim
e,
en
er
g
y
c
o
n
s
u
m
p
tio
n
,
co
m
m
u
n
icatio
n
m
etr
ics,
an
d
e
n
v
ir
o
n
m
en
tal
d
ata)
.
2
.
3
.
Da
t
a
prepa
ra
t
i
o
n a
nd
f
ea
t
ure
eng
ineering
Data
p
r
ep
ar
atio
n
a
n
d
f
ea
tu
r
e
en
g
in
ee
r
in
g
ar
e
im
p
o
r
tan
t
p
h
ases
th
at
g
r
o
u
n
d
th
e
r
aw
d
ata
with
ad
v
an
ce
d
m
ac
h
in
e
lear
n
in
g
al
g
o
r
ith
m
s
.
T
h
e
p
r
o
ce
s
s
s
tar
ted
with
th
o
r
o
u
g
h
d
ata
clea
n
i
n
g
to
r
em
ed
y
d
is
p
ar
ities
an
d
tr
ea
t
ir
r
elev
an
t
en
tr
ies.
C
o
n
s
ec
u
tiv
e
clea
n
in
g
an
d
n
o
r
m
aliza
tio
n
tech
n
iq
u
es
wer
e
u
tili
ze
d
to
s
ca
le
n
u
m
er
ical
d
ata,
ass
u
r
in
g
n
o
attr
ib
u
te
u
n
e
q
u
al
in
f
lu
en
ce
d
th
e
m
o
d
el.
Miss
in
g
v
alu
es
wer
e
s
y
s
tem
atica
lly
ad
d
r
ess
ed
b
y
im
p
u
tatio
n
,
en
s
u
r
in
g
t
h
e
d
ataset'
s
in
teg
r
ity
.
C
ateg
o
r
ical
v
ar
ia
b
les
wer
e
en
c
o
d
in
g
,
tr
an
s
f
o
r
m
in
g
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
7
,
No
.
3
,
Ma
r
ch
20
2
5
:
1
650
-
1
6
6
0
1652
tex
tu
al
d
ata
in
to
a
n
u
m
er
ical
f
o
r
m
at
co
m
p
lian
t
with
alg
o
r
it
h
m
ic
p
r
o
ce
s
s
in
g
.
T
h
is
in
clu
d
e
d
tr
an
s
f
o
r
m
i
n
g
th
e
'
At
tack
ty
p
e'
v
ar
iab
le
in
to
an
e
n
co
d
ed
f
o
r
m
at,
cr
u
cial
f
o
r
clas
s
if
icatio
n
task
s
with
in
cy
b
er
s
ec
u
r
ity
d
o
m
ain
s
.
T
em
p
o
r
al
f
ea
tu
r
e
e
x
tr
ac
tio
n
c
o
n
s
titu
ted
an
o
u
ts
tan
d
in
g
p
o
r
t
io
n
o
f
th
e
f
ea
tu
r
e
en
g
in
ee
r
in
g
p
r
o
ce
s
s
.
T
im
estam
p
d
ata
wer
e
d
ec
o
m
p
o
s
ed
in
to
d
is
cr
ete
co
m
p
o
n
e
n
ts
s
u
ch
as
h
o
u
r
s
,
d
ay
s
,
an
d
m
o
n
th
s
,
p
r
o
v
i
d
in
g
g
r
an
u
lar
in
s
ig
h
ts
in
to
tem
p
o
r
a
l
p
atter
n
s
an
d
an
o
m
alies.
T
h
is
tem
p
o
r
al
d
is
s
ec
tio
n
allo
wed
th
e
m
o
d
els
to
n
o
tice
p
atter
n
s
r
elate
d
to
s
p
ec
if
ic
tim
es,
en
h
an
cin
g
t
h
eir
p
r
ed
icti
v
e
ac
cu
r
ac
y
f
o
r
tim
e
-
s
en
s
itiv
e
an
o
m
alies.
T
h
e
d
ataset
was
s
p
lit
in
to
tr
ain
in
g
an
d
test
in
g
d
atasets
.
T
h
e
tr
ain
in
g
d
ataset
co
n
tain
s
8
0
%
(
2
9
9
,
7
2
8
r
o
ws)
an
d
th
e
test
in
g
d
ataset
co
n
tain
s
2
0
%
(
7
4
,
9
3
3
r
o
ws)
o
f
th
e
to
tal
d
ata.
Data
v
alu
es
wer
e
s
to
r
ed
in
th
e
d
atab
ase
an
d
th
en
ex
p
o
r
ted
in
C
SV f
il
es to
ap
p
ly
th
e
ML
m
o
d
els to
th
e
d
ata.
Fu
r
th
er
m
o
r
e
,
ad
v
an
ce
d
f
ea
tu
r
e
en
g
in
ee
r
in
g
tech
n
iq
u
es
wer
e
en
g
ag
e
d
to
d
is
till
in
f
o
r
m
ativ
e
attr
ib
u
tes
f
r
o
m
r
aw
n
etwo
r
k
m
etr
ics.
C
o
m
m
u
n
icatio
n
r
atio
s
,
en
er
g
y
ef
f
icien
cy
i
n
d
icato
r
s
,
an
d
n
et
wo
r
k
lo
a
d
m
etr
ics
wer
e
ca
lcu
lat
ed
,
tr
an
s
f
o
r
m
in
g
r
aw
s
en
s
o
r
d
ata
an
d
n
etwo
r
k
m
etr
ics
in
to
f
ea
tu
r
es
with
elev
ated
p
r
ed
ictiv
e
ca
p
ab
ilit
ies.
Fo
r
th
is
s
tu
d
y
,
k
e
y
f
ea
tu
r
es in
teg
r
ated
in
to
m
o
d
el
alg
o
r
ith
m
s
in
clu
d
e
d
:
−
T
em
p
o
r
al
f
ea
tu
r
es
:
'
Ho
u
r
'
an
d
'
Day
Of
W
ee
k
'
ex
tr
ac
ted
f
r
o
m
th
e
'
T
im
e'
d
ata,
o
f
f
e
r
in
g
in
s
ig
h
ts
in
to
p
atter
n
s
th
at
m
ay
co
r
r
elate
with
n
etwo
r
k
s
ec
u
r
ity
in
cid
e
n
ts
at
s
p
ec
if
ic
tim
es;
−
C
o
m
m
u
n
icatio
n
r
atio
s
:
'
ADV
_
R
atio
'
an
d
'JOIN
_
R
atio
'
,
d
er
iv
ed
f
r
o
m
th
e
ad
v
an
ce
d
-
to
-
r
e
ce
iv
ed
an
d
jo
in
-
r
eq
u
est
-
to
-
r
ec
eiv
e
d
m
ess
ag
e
r
atio
s
,
r
esp
ec
tiv
ely
,
h
ig
h
lig
h
t
in
g
co
m
m
u
n
icatio
n
b
eh
av
io
r
an
o
m
alies;
−
Dis
tan
ce
m
etr
ics
:
'
T
o
tal_
Di
s
t_
T
o
_
B
S'
,
co
m
b
in
in
g
'
Dis
t
_
T
o
_
C
H'
(
d
is
tan
ce
to
clu
s
ter
h
ea
d
)
a
n
d
'
d
is
t_
C
H_
T
o
_
B
S
'
(
d
is
tan
ce
f
r
o
m
clu
s
ter
h
ea
d
to
b
ase
s
tatio
n
)
,
to
ass
ess
th
e
im
p
ac
t
o
f
n
o
d
e
p
o
s
itio
n
in
g
o
n
n
etwo
r
k
v
u
ln
er
a
b
ilit
y;
−
E
n
er
g
y
ef
f
icien
cy
in
d
icato
r
s
:
'
E
n
er
g
y
_
p
er
_
Pack
et'
an
d
'
E
n
er
g
y
_
p
er
_
Dis
t'
,
f
o
cu
s
in
g
o
n
th
e
en
er
g
y
co
n
s
u
m
ed
p
er
d
ata
p
ac
k
et
s
en
t
an
d
p
e
r
d
is
tan
ce
u
n
it,
wh
ich
c
an
s
ig
n
al
in
ef
f
icien
t
o
r
co
m
p
r
o
m
is
ed
n
o
d
es;
−
E
n
v
ir
o
n
m
en
tal
ch
an
g
es
:
'
T
em
p
_
C
h
an
g
e'
an
d
'
Hu
m
id
ity
_
C
h
an
g
e'
,
tr
ac
k
in
g
ab
r
u
p
t
v
ar
iatio
n
s
in
tem
p
er
atu
r
e
a
n
d
h
u
m
id
ity
th
at
co
u
ld
af
f
ec
t
s
en
s
o
r
p
er
f
o
r
m
an
ce
an
d
p
o
ten
tially
in
d
icate
tam
p
er
in
g
o
r
an
o
m
alies;
−
Netwo
r
k
lo
ad
:
ca
lcu
lated
as
'
Netwo
r
k
_
Activ
ity
'
an
d
r
e
p
r
esen
ted
b
y
'
Netwo
r
k
_
L
o
ad
'
,
p
r
o
v
id
in
g
a
m
ea
s
u
r
e
o
f
th
e
n
etwo
r
k
'
s
o
p
er
atio
n
al
b
u
r
d
en
a
n
d
id
e
n
tify
in
g
p
o
te
n
tial stre
s
s
o
r
attac
k
v
ec
to
r
s
.
T
h
ese
en
g
in
ee
r
e
d
f
ea
tu
r
es
o
u
tlin
e
th
e
co
m
p
lex
d
y
n
a
m
ics
o
f
W
SNs
,
o
f
f
er
in
g
a
n
u
an
ce
d
u
n
d
er
s
tan
d
i
n
g
o
f
n
etwo
r
k
b
eh
av
io
r
t
h
at
s
u
p
p
o
r
ts
ef
f
ec
ti
v
e
an
o
m
aly
d
etec
tio
n
.
T
h
is
ap
p
r
o
ac
h
n
o
t
o
n
ly
s
u
p
p
o
r
ts
th
e
in
s
tan
t
d
etec
tio
n
o
f
p
o
ten
tial
s
ec
u
r
ity
b
r
ea
ch
es
b
u
t
also
eq
u
i
p
s
n
etwo
r
k
a
d
m
in
is
tr
ato
r
s
with
ac
tio
n
ab
le
in
s
ig
h
ts
,
th
er
eb
y
ai
d
in
g
p
r
o
ac
tiv
e
m
ea
s
u
r
es to
r
ei
n
f
o
r
ce
n
etwo
r
k
d
ef
en
s
es.
2
.
4
.
M
a
chine le
a
rning
m
o
de
ls
I
n
th
is
wo
r
k
,
two
m
ac
h
in
e
lea
r
n
in
g
alg
o
r
ith
m
s
wer
e
u
s
ed
f
o
r
th
eir
ad
ap
tab
ilit
y
an
d
ef
f
ec
ti
v
en
ess
in
tack
lin
g
class
if
icatio
n
ch
allen
g
es
with
in
th
e
c
y
b
er
s
ec
u
r
ity
f
r
am
ewo
r
k
o
f
W
SNs
.
DT
,
an
d
R
F
wer
e
s
elec
ted
to
ev
alu
ate
th
e
b
est
p
r
ed
ictio
n
m
o
d
el
f
o
r
W
SN.
T
h
ese
s
u
p
er
v
i
s
ed
lear
n
i
n
g
ML
alg
o
r
ith
m
s
a
r
e
esp
ec
ially
s
u
ited
d
u
e
to
th
eir
ca
p
ab
ilit
y
to
h
a
n
d
le
th
e
d
ataset'
s
co
m
p
lex
ity
,
wh
ich
in
clu
d
es
d
is
tin
ct
f
ea
tu
r
es
lik
e
tem
p
o
r
al
in
f
o
r
m
atio
n
,
co
m
m
u
n
icatio
n
m
etr
ics,
an
d
en
v
i
r
o
n
m
e
n
tal
f
ac
to
r
s
—
all
cr
u
cial
f
o
r
d
etec
ti
n
g
an
o
m
alies.
DT
p
r
o
v
id
es
a
m
o
d
el
th
at
r
ef
in
es
th
e
u
n
d
er
s
tan
d
in
g
o
f
h
o
w
v
ar
io
u
s
f
ea
tu
r
es
im
p
ac
t
th
e
p
r
ed
ictio
n
o
f
s
ec
u
r
ity
th
r
ea
ts
.
R
F
is
a
p
r
ed
ictio
n
m
eth
o
d
th
at
ex
ten
d
s
th
is
b
y
a
g
g
r
eg
atin
g
m
u
ltip
le
tr
ee
s
to
en
h
an
ce
p
r
ed
ictio
n
ac
cu
r
ac
y
an
d
r
o
b
u
s
tn
ess
ag
ain
s
t o
v
er
f
itti
n
g
,
en
s
u
r
in
g
th
e
m
o
d
el'
s
r
eliab
ilit
y
ac
r
o
s
s
d
if
f
er
en
t WS
N
s
ce
n
ar
io
s
.
2
.
4
.
1
.
Alg
o
rit
hm
1
:
DT
At
th
e
ce
n
ter
o
f
o
u
r
m
o
d
e
l
s
elec
tio
n
i
s
th
e
DT
a
lg
o
r
ith
m
,
p
r
ef
er
r
e
d
f
o
r
its
s
im
p
licity
an
d
in
ter
p
r
etab
ilit
y
.
T
h
is
m
o
d
el
co
n
s
tr
u
cts
a
tr
ee
-
lik
e
s
tr
u
ctu
r
e
o
f
d
ec
is
io
n
s
,
wh
er
e
ea
ch
n
o
d
e
r
ep
r
esen
ts
a
f
ea
tu
r
e
in
th
e
d
ataset,
an
d
ea
ch
b
r
a
n
ch
s
y
m
b
o
lizes
a
d
ec
is
io
n
r
u
le
lead
in
g
to
d
if
f
er
e
n
t
o
u
tco
m
e
s
.
I
n
t
h
e
co
n
tex
t
o
f
W
SN
s
,
th
e
DT
p
r
o
v
id
es
an
in
h
er
en
t
m
ec
h
an
is
m
to
d
is
s
ec
t
an
d
u
n
d
er
s
tan
d
th
e
u
n
d
er
l
y
in
g
f
ac
to
r
s
co
n
tr
i
b
u
tin
g
to
n
etwo
r
k
a
n
o
m
alies.
B
y
th
o
r
o
u
g
h
ly
s
eg
m
en
tin
g
th
e
d
ataset
b
ased
o
n
f
ea
tu
r
e
v
alu
es,
it g
iv
es in
s
ig
h
ts
in
to
th
e
s
ig
n
if
ican
ce
o
f
s
p
ec
if
ic
f
ea
t
u
r
es
in
p
r
e
d
ictin
g
s
ec
u
r
ity
b
r
ea
ch
es.
T
h
is
ap
p
r
o
ac
h
n
o
t
o
n
l
y
s
u
p
p
o
r
ts
an
o
m
aly
d
etec
tio
n
b
u
t a
ls
o
f
ac
ilit
ates th
e
id
en
tific
atio
n
o
f
p
o
te
n
tial a
r
ea
s
f
o
r
n
etwo
r
k
s
ec
u
r
ity
im
p
r
o
v
em
en
t.
2
.
4
.
2
.
Alg
o
rit
hm
2
:
RF
B
u
ild
in
g
u
p
o
n
th
e
DT
f
o
u
n
d
a
tio
n
,
th
e
R
F
alg
o
r
ith
m
in
co
r
p
o
r
ates
jo
in
t
DT
to
f
o
r
m
a
p
ac
k
ed
m
o
d
el,
s
ig
n
if
ican
tly
en
h
an
cin
g
th
e
p
r
ed
ictiv
e
p
er
f
o
r
m
an
ce
an
d
s
tab
ilit
y
.
B
y
g
en
er
atin
g
a
m
u
ltit
u
d
e
o
f
tr
ee
s
an
d
ag
g
r
eg
atin
g
th
eir
p
r
e
d
ictio
n
s
,
R
F
m
itig
ates
th
e
o
v
e
r
f
itti
n
g
is
s
u
es
co
m
m
o
n
ly
ass
o
ciate
d
with
s
in
g
le
DT
.
I
t
g
ain
s
d
o
m
in
a
n
t
ac
cu
r
ac
y
th
r
o
u
g
h
m
aj
o
r
ity
v
o
tin
g
o
r
a
v
er
ag
in
g
,
m
a
k
in
g
it
p
ar
ticu
lar
ly
s
k
illed
at
h
an
d
lin
g
th
e
co
m
p
lex
a
n
d
d
y
n
am
ic
n
atu
r
e
o
f
c
y
b
er
s
ec
u
r
ity
th
r
ea
ts
with
in
W
SNs
.
T
h
is
alg
o
r
ith
m
'
s
ca
p
ac
ity
to
a
n
aly
ze
ex
ten
s
iv
e
d
atasets
an
d
n
o
tice
co
m
p
lex
p
atter
n
s
m
ak
es
it
a
n
in
v
alu
a
b
le
to
o
l
f
o
r
p
r
ev
en
ti
v
e
id
en
tify
i
n
g
an
d
m
itig
atin
g
p
o
ten
tial secu
r
ity
i
n
cid
en
ts
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
Dete
ctin
g
n
etw
o
r
k
s
ec
u
r
ity
in
c
id
en
ts
in
w
ir
eles
s
s
en
s
o
r
…
(
T
a
ma
r
a
Zh
u
ka
b
a
ye
va
)
1653
2
.
4
.
3
.
F
ea
t
ure
i
m
po
rt
a
nce
a
n
d m
o
del dem
o
ns
t
ra
t
io
n
An
ess
en
tial
co
m
p
o
n
en
t
o
f
u
ti
lizin
g
th
ese
alg
o
r
ith
m
s
is
th
e
ev
alu
atio
n
o
f
f
ea
tu
r
e
im
p
o
r
ta
n
ce
,
wh
ich
u
n
co
v
e
r
s
th
e
r
elativ
e
s
ig
n
i
f
ican
ce
o
f
ea
c
h
f
ea
t
u
r
e
in
th
e
p
r
e
d
ictiv
e
m
o
d
els.
T
h
is
a
n
aly
s
is
n
o
t
o
n
ly
im
p
r
o
v
es
th
e
u
n
d
er
s
tan
d
in
g
o
f
th
e
m
o
d
els'
d
ec
is
io
n
-
m
ak
in
g
p
r
o
ce
s
s
es
b
u
t
also
tailo
r
s
f
u
tu
r
e
d
ata
co
llectio
n
an
d
p
r
ep
r
o
ce
s
s
in
g
ef
f
o
r
ts
.
B
y
id
en
tify
in
g
wh
ich
f
ea
t
u
r
es
m
o
s
t
f
o
r
cib
ly
in
f
lu
e
n
ce
th
e
m
o
d
el'
s
p
r
ed
ictio
n
s
,
r
esear
ch
er
s
ca
n
f
o
c
u
s
th
eir
e
f
f
o
r
ts
o
n
th
e
m
o
s
t
a
p
p
licab
le
d
ata,
o
p
tim
izin
g
th
e
e
f
f
icien
cy
an
d
ef
f
icac
y
o
f
cy
b
er
s
ec
u
r
ity
m
ea
s
u
r
es
in
W
SNs
.
Mo
r
eo
v
er
,
th
e
u
p
co
m
in
g
p
h
ase
was
to
tr
ain
an
ML
m
o
d
el
to
d
y
n
am
ically
class
if
y
an
d
p
r
ed
ict
v
ar
io
u
s
ty
p
es o
f
an
o
m
alies a
n
d
s
ec
u
r
ity
th
r
ea
ts
with
in
W
SNs
.
2
.
5
.
M
o
dels
v
a
lid
a
t
io
n
Mo
d
el
v
alid
atio
n
was
co
n
d
u
cted
th
r
o
u
g
h
a
s
er
ies
o
f
p
er
f
o
r
m
an
ce
e
v
alu
atio
n
s
u
s
in
g
t
h
e
s
tan
d
ar
d
m
o
d
el
p
er
f
o
r
m
a
n
ce
m
etr
ics
s
u
ch
as
ac
cu
r
ac
y
,
p
r
ec
is
io
n
,
r
ec
all
,
an
d
F1
s
co
r
e.
T
h
e
b
eh
a
v
i
o
r
an
d
p
er
f
o
r
m
an
ce
o
f
th
e
m
o
d
el
wer
e
ass
ess
ed
b
y
u
s
in
g
th
ese
m
etr
ics
an
d
th
eir
r
elev
an
ce
t
o
DT
an
d
R
F.
Acc
u
r
ac
y
m
ea
s
u
r
es
th
e
p
r
o
p
o
r
tio
n
o
f
co
r
r
ec
tly
class
if
ied
in
s
tan
ce
s
o
u
t
o
f
th
e
to
tal
i
n
s
tan
ce
s
ev
alu
ated
[
1
3
]
,
[
1
4
]
.
I
n
th
e
ca
s
e
o
f
DT
an
d
R
F
m
o
d
els,
ac
cu
r
ac
y
r
ef
l
ec
ts
th
e
o
v
er
all
co
r
r
ec
tn
ess
o
f
th
e
m
o
d
el'
s
p
r
ed
ictio
n
s
,
in
d
icatin
g
h
o
w
well
th
ey
class
if
y
b
o
th
n
o
r
m
al
an
d
a
n
o
m
alo
u
s
n
etwo
r
k
ac
tiv
ities
.
Pre
cisi
o
n
q
u
an
tifie
s
th
e
ac
cu
r
ac
y
o
f
p
o
s
itiv
e
p
r
ed
ictio
n
s
m
ad
e
b
y
th
e
m
o
d
el
[
1
5
]
.
I
n
o
u
r
ca
s
e,
p
r
ec
is
io
n
ev
alu
ates
th
e
DT
an
d
R
F
m
o
d
els'
ca
p
ac
ity
to
co
r
r
ec
tly
id
e
n
tify
ac
tu
al
n
et
wo
r
k
th
r
ea
ts
with
o
u
t
f
alsely
lab
elin
g
n
o
r
m
al
ac
tiv
ities
a
s
an
o
m
alies.
R
ec
all
m
ea
s
u
r
es
th
e
m
o
d
el'
s
ab
ilit
y
to
ca
p
tu
r
e
all
p
o
s
itiv
e
in
s
tan
ce
s
in
th
e
d
ataset
[
1
6
]
.
I
n
th
e
c
o
n
tex
t
o
f
cy
b
er
s
ec
u
r
ity
,
r
ec
all
ass
ess
e
s
h
o
w
f
i
n
e
th
e
DT
an
d
R
F
m
o
d
els
d
etec
t
r
ea
l
n
etwo
r
k
th
r
e
ats,
en
s
u
r
in
g
m
in
im
al
f
alse
n
eg
ativ
es.
T
h
e
F1
s
co
r
e
is
th
e
m
ea
n
o
f
p
r
ec
is
io
n
a
n
d
r
ec
all,
p
r
o
v
id
in
g
a
b
alan
c
ed
ass
ess
m
en
t
o
f
a
m
o
d
el'
s
p
er
f
o
r
m
an
ce
.
I
t
c
o
n
s
id
er
s
b
o
th
f
alse
p
o
s
itiv
es
an
d
f
alse
n
eg
ativ
es,
m
ak
in
g
it
an
a
p
p
licab
le
m
etr
ic
f
o
r
ass
es
s
in
g
m
o
d
el
ef
f
ec
tiv
en
ess
in
im
b
alan
ce
s
ce
n
ar
io
s
[
1
7
]
.
T
h
e
F1
s
co
r
e
p
r
o
v
id
es
a
th
o
r
o
u
g
h
ev
al
u
atio
n
o
f
DT
an
d
R
F
m
o
d
els,
in
d
icatin
g
r
o
b
u
s
tn
ess
in
d
ete
ctin
g
n
etwo
r
k
an
o
m
alies
wh
ile
m
in
im
izin
g
m
is
class
if
icatio
n
s
.
T
h
ese
m
etr
ics
p
r
o
v
id
e
i
n
s
ig
h
ts
in
to
th
e
DT
an
d
R
F
m
o
d
el’
s
p
e
r
f
o
r
m
an
ce
,
em
p
h
asizin
g
th
eir
ef
f
ec
tiv
en
ess
in
d
etec
tin
g
an
o
m
alies with
in
W
SN.
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
e
an
aly
s
is
o
f
th
e
r
esu
lts
h
i
g
h
lig
h
ts
th
e
p
iv
o
tal
r
o
le
o
f
f
ea
tu
r
e
en
g
in
ee
r
in
g
in
o
p
tim
iz
in
g
m
o
d
el
p
er
f
o
r
m
an
ce
.
Af
ter
c
o
llectin
g
th
e
d
ata,
Go
o
g
le
C
o
lab
an
d
J
u
p
y
ter
No
teb
o
o
k
wer
e
u
tili
ze
d
f
o
r
d
ata
an
aly
s
is
an
d
v
is
u
aliza
tio
n
o
f
d
ata.
I
n
th
is
s
ec
tio
n
,
we
in
tr
o
d
u
ce
t
h
e
f
in
d
in
g
s
f
r
o
m
o
u
r
ex
p
er
i
m
en
ts
an
d
o
u
tlin
e
co
n
clu
s
iv
e
r
em
ar
k
s
b
ased
o
n
t
h
e
co
n
d
u
cted
a
n
aly
s
es.
3
.
1
.
E
x
plo
ra
t
o
ry
da
t
a
a
na
l
y
s
is
(
E
D
A)
3
.
1
.
1
.
O
v
er
v
i
ew
o
f
da
t
a
s
et
cha
ra
ct
er
is
t
ics
Ou
r
an
aly
s
is
b
eg
in
s
with
an
E
DA,
ex
p
lain
in
g
k
ey
d
ataset
ch
ar
ac
ter
is
tics
,
an
o
m
alies,
an
d
p
atter
n
s
.
T
h
is
in
s
p
ec
tio
n
s
h
o
ws
v
alu
ab
le
in
s
ig
h
ts
in
to
th
e
d
is
tr
ib
u
tio
n
,
v
ar
iab
ilit
y
,
an
d
r
elatio
n
s
a
m
o
n
g
v
ar
iab
les.
T
h
e
d
ataset
u
tili
ze
d
i
n
th
is
s
tu
d
y
d
em
o
n
s
tr
ates
a
wid
e
v
ar
iety
o
f
f
ea
tu
r
es
th
at
ca
p
tu
r
e
th
e
in
tr
icate
d
y
n
am
ics
o
f
wir
eless
s
en
s
o
r
n
etwo
r
k
s
(
W
SN
s
)
.
B
y
lev
er
ag
i
n
g
t
h
e
s
e
d
iv
er
s
e
f
ea
tu
r
es,
th
e
a
n
aly
s
is
en
s
u
r
es
a
co
m
p
r
eh
e
n
s
iv
e
u
n
d
er
s
tan
d
in
g
o
f
th
e
d
ataset,
en
a
b
lin
g
th
e
d
esig
n
o
f
ef
f
ec
tiv
e
m
ac
h
i
n
e
l
ea
r
n
in
g
m
o
d
els
f
o
r
an
o
m
aly
d
etec
tio
n
.
3
.
1
.
2
.
T
im
e
s
er
ies a
na
ly
s
is
Ou
r
p
r
im
a
r
y
ai
m
o
f
th
is
p
h
ase
was
to
r
e
v
ea
l
tem
p
o
r
al
p
att
er
n
s
an
d
p
o
te
n
tial
an
o
m
alies
em
b
ed
d
e
d
with
in
cr
itical
v
ar
iab
les
(
tim
e,
ex
p
an
d
ed
en
er
g
y
,
tem
p
er
atu
r
e,
an
d
h
u
m
id
ity
)
.
W
e
in
itiated
th
e
E
DA
with
d
at
a
clea
n
in
g
an
d
p
r
ep
ar
atio
n
s
tep
s
,
h
an
d
lin
g
o
f
m
is
s
in
g
v
alu
es,
an
d
s
tan
d
a
r
d
izatio
n
o
f
c
o
lu
m
n
n
am
es,
to
en
s
u
r
e
d
ataset
in
teg
r
ity
.
Fo
llo
win
g
th
is
,
we
en
g
ag
ed
s
tatis
tical
s
u
m
m
ar
ies
to
h
ig
h
lig
h
t
k
ey
s
tatis
t
ical
attr
ib
u
tes
s
u
ch
as
ce
n
tr
al
ten
d
e
n
cies,
d
is
p
er
s
i
o
n
s
,
an
d
r
a
n
g
es.
T
h
e
ex
a
m
in
a
tio
n
r
ev
ea
le
d
n
o
m
is
s
in
g
v
alu
es
in
cr
itical
f
ield
s
,
th
er
eb
y
e
n
s
u
r
in
g
t
h
e
co
m
p
le
ten
ess
o
f
o
u
r
d
ataset.
T
h
e
s
tatis
tical
s
u
m
m
ar
ies
b
r
in
g
s
ig
n
if
ican
t
v
ar
iab
ilit
y
with
in
ex
p
an
d
e
d
en
e
r
g
y
(
m
e
an
o
f
~
0
.
3
0
6
u
n
its
,
s
tan
d
ar
d
d
ev
iatio
n
o
f
0
.
6
6
9
u
n
its
)
,
te
m
p
er
atu
r
e
(
m
ea
n
o
f
~1
5
°C
,
s
tan
d
ar
d
d
ev
iatio
n
o
f
1
4
.
4
4
4
°C
)
,
an
d
h
u
m
id
ity
(
m
ea
n
o
f
~5
0
%,
s
tan
d
ar
d
d
e
v
i
atio
n
o
f
2
8
.
8
5
2
%).
Fig
u
r
e
1
s
h
o
ws
th
e
tim
e
s
er
ies
an
aly
s
is
d
ata,
v
is
u
alizin
g
ex
p
an
d
ed
e
n
er
g
y
,
tem
p
er
at
u
r
e,
an
d
h
u
m
id
i
ty
o
v
e
r
tim
e.
T
h
e
p
r
esen
ce
o
f
s
ig
n
if
ican
t
v
ar
iab
ilit
y
an
d
n
o
m
is
s
in
g
v
al
u
es
in
k
ey
f
ield
s
in
d
i
ca
tes
a
s
o
lid
a
n
d
th
o
r
o
u
g
h
d
ataset,
v
ital f
o
r
ac
c
u
r
ate
an
o
m
al
y
d
etec
tio
n
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
7
,
No
.
3
,
Ma
r
ch
20
2
5
:
1
650
-
1
6
6
0
1654
Fig
u
r
e
1
.
C
u
r
r
e
n
t tim
es ser
ies d
ata
3
.
1
.
3
.
Co
rr
ela
t
io
n
a
na
ly
s
is
T
h
is
p
h
ase
p
u
r
s
u
ed
to
ex
p
lai
n
th
e
co
m
p
lex
r
elatio
n
s
h
ip
s
b
etwe
en
v
ar
io
u
s
f
ea
tu
r
es,
cr
u
ci
al
f
o
r
o
u
r
s
u
b
s
eq
u
en
t
f
ea
tu
r
e
s
elec
tio
n
.
A
co
r
r
elatio
n
m
atr
ix
v
is
u
ally
r
ep
r
esen
ts
th
e
p
ea
r
s
o
n
co
r
r
e
latio
n
co
ef
f
icien
ts
,
wh
ich
m
ea
s
u
r
e
th
e
lin
ea
r
r
elat
io
n
s
h
ip
b
etwe
en
th
e
d
ataset'
s
v
ar
iab
les.
T
em
p
er
atu
r
e
an
d
h
u
m
id
ity
em
er
g
ed
as
in
s
ig
n
if
ican
t
co
r
r
elatio
n
s
with
o
th
er
v
ar
iab
les,
in
d
icatin
g
th
eir
p
o
ten
tial
in
d
ep
en
d
e
n
ce
o
r
lack
o
f
in
f
lu
en
ce
with
in
th
e
n
etwo
r
k
b
eh
av
i
o
r
c
ap
tu
r
ed
b
y
o
u
r
d
ataset.
Ho
we
v
er
,
ex
p
a
n
d
ed
en
e
r
g
y
s
h
o
wed
a
m
o
d
er
ate
p
o
s
itiv
e
co
r
r
elatio
n
with
d
is
t_
C
H_
T
o
_
B
S
(
0
.
3
8
)
,
im
p
ly
i
n
g
escalate
d
en
er
g
y
c
o
n
s
u
m
p
tio
n
with
g
r
e
ater
d
is
tan
ce
s
f
r
o
m
th
e
clu
s
ter
h
ea
d
to
th
e
b
ase
s
tatio
n
.
Ad
d
itio
n
ally
,
th
e
co
r
r
elatio
n
c
o
ef
f
icien
ts
b
etwe
e
n
Dis
t_
T
o
_
C
H
an
d
co
m
m
u
n
icatio
n
m
etr
ics
s
u
ch
as
J
OI
N_
S
(
0
.
5
5
)
an
d
J
OI
N_
R
(
-
0
.
1
6
)
s
u
g
g
est
m
o
d
er
a
te
co
r
r
elatio
n
s
.
T
h
is
im
p
lies
th
at
n
o
d
es
p
o
s
itio
n
e
d
f
ar
th
e
r
f
r
o
m
t
h
e
clu
s
ter
h
e
ad
ex
h
i
b
it
v
ar
y
in
g
co
m
m
u
n
icatio
n
b
e
h
av
io
r
s
,
p
o
ten
tially
in
f
lu
en
ce
d
b
y
s
ig
n
al
r
an
g
e
lim
itatio
n
s
o
r
n
etwo
r
k
co
n
g
esti
o
n
.
C
o
n
v
er
s
ely
,
d
is
t_
C
H_
T
o
_
B
S
h
as
a
s
tr
o
n
g
n
eg
ativ
e
c
o
r
r
elatio
n
with
SC
H_
R
(
-
0
.
6
8
)
,
p
o
ten
tially
in
d
icatin
g
s
ch
e
d
u
lin
g
co
m
m
u
n
icatio
n
is
s
u
es
wit
h
in
cr
ea
s
ed
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ce
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r
o
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ase
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tatio
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[
1
8
]
.
Fig
u
r
e
2
s
h
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r
r
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o
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ich
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r
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cc
ess
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m
aly
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etec
tio
n
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
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-
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7
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Dete
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1655
Fig
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.
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ize
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Fig
u
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3
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Plo
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es
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ay
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B
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[
1
9
]
.
T
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t c
r
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tan
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etwo
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k
attac
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s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
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esian
J
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C
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3
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3
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u
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.
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DT
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el
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ir
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etec
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u
r
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6
p
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ity
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2
0
]
.
Fig
u
r
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5
.
DT
d
ec
is
io
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u
r
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ac
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t o
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attac
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g
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r
ay
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le
2
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al
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,
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d
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DM
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4
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
Dete
ctin
g
n
etw
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r
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s
ec
u
r
ity
in
c
id
en
ts
in
w
ir
eles
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s
en
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…
(
T
a
ma
r
a
Zh
u
ka
b
a
ye
va
)
1657
Fig
u
r
e
6
.
Featu
r
e
im
p
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DT
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o
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el
3
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2
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2
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atin
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ir
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atter
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en
t
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[
2
1
]
.
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m
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el,
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r
e
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f
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8
.
2
3
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T
h
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v
alu
es
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h
asize
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e
m
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el'
s
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ce
d
ab
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r
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ely
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in
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s
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f
u
r
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ic
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m
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n
.
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le
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5
1
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n
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V_
S
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4
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8
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ce
o
n
th
e
m
o
d
el'
s
p
r
ed
ictio
n
s
[
2
2
]
.
T
ab
le
1
.
Featu
r
e
im
p
o
r
tan
ce
ta
b
le
-
RF
m
o
d
el
F
e
a
t
u
r
e
I
D
F
e
a
t
u
r
e
V
a
l
u
e
0
Ex
p
a
n
d
e
d
e
n
e
r
g
y
0
.
5
1
2
0
9
1
A
D
V
_
S
0
.
4
8
7
9
Fig
u
r
e
7
s
h
o
ws
th
e
m
o
s
t
im
p
o
r
tan
t
f
ea
tu
r
es
f
o
r
class
if
y
in
g
n
etwo
r
k
s
ec
u
r
ity
in
cid
e
n
ts
in
th
e
R
F
m
o
d
el,
with
ex
p
a
n
d
ed
e
n
er
g
y
(
1
1
.
7
2
%),
ADV_
S
(
1
1
.
0
5
%),
an
d
T
o
tal_
Dis
t_
T
o
_
B
S
(
8
.
1
8
%)
co
n
tr
ib
u
tin
g
s
ig
n
if
ican
tly
to
th
e
m
o
d
el'
s
d
ec
is
io
n
-
m
ak
in
g
p
r
o
ce
s
s
.
Me
tr
ics,
(
Data
_
Sen
t_
T
o
_
B
S,
an
d
SC
H_
S),
p
lay
v
ital
r
o
les in
an
aly
zin
g
n
etwo
r
k
tr
af
f
ic,
b
eh
av
i
o
r
,
a
n
d
ef
f
icien
cy
.
Fig
u
r
e
7
.
Featu
r
e
im
p
o
r
ta
n
ce
-
R
F m
o
d
el
3
.
3
.
Co
m
pa
ra
t
iv
e
a
na
ly
s
is
3
.
3
.
1
.
P
er
f
o
r
m
a
nce
co
m
pa
riso
n bet
wee
n dec
is
io
n t
re
e
a
nd
RF
T
o
co
n
f
ir
m
th
e
p
r
ed
ictio
n
ab
i
lity
o
f
th
e
p
r
o
p
o
s
ed
m
o
d
els,
t
h
e
ac
cu
r
a
cy
o
f
t
h
e
DT
an
d
R
F
m
o
d
els
was
ev
alu
ated
b
ased
o
n
th
eir
p
er
f
o
r
m
a
n
ce
(
ac
cu
r
ac
y
s
co
r
es).
T
h
e
in
p
u
ts
to
b
o
th
m
o
d
el
s
in
clu
d
ed
n
etwo
r
k
s
ec
u
r
ity
in
cid
en
ts
,
n
etwo
r
k
c
o
m
m
u
n
icatio
n
m
etr
ics,
e
n
v
ir
o
n
m
en
tal
v
ar
iab
les,
a
n
d
o
th
e
r
u
s
ef
u
l
p
ar
am
eter
s
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
7
,
No
.
3
,
Ma
r
ch
20
2
5
:
1
650
-
1
6
6
0
1658
ex
tr
ac
ted
f
r
o
m
s
en
s
o
r
r
ea
d
in
g
s
o
r
n
etwo
r
k
lo
g
s
[
2
3
]
.
Fig
u
r
e
8
s
h
o
ws
th
e
p
r
ed
ictio
n
r
esu
lts
f
r
o
m
co
m
p
ar
in
g
th
e
DT
an
d
R
F
m
o
d
els.
DT
m
o
d
el
r
ea
lized
an
ac
cu
r
ac
y
o
f
9
9
.
4
7
%,
wh
ile
th
e
R
F
m
o
d
el
o
u
tp
er
f
o
r
m
ed
it
with
a
n
ac
cu
r
ac
y
o
f
9
9
.
7
1
%.
T
h
is
m
ea
n
s
th
at
b
o
th
m
o
d
els
p
er
f
o
r
m
ed
ex
ce
p
tio
n
ally
well
in
class
if
y
in
g
n
etwo
r
k
s
ec
u
r
ity
in
cid
en
ts
in
o
u
r
d
atas
et.
Sti
ll,
th
e
R
F
m
o
d
el
d
em
o
n
s
tr
ated
a
s
lig
h
tly
h
ig
h
er
ac
c
u
r
ac
y
,
in
d
icatin
g
t
h
at
its
en
s
em
b
le
lear
n
in
g
ap
p
r
o
ac
h
,
wh
ich
c
o
m
b
in
es
m
u
lt
ip
le
DT
,
m
ay
h
av
e
c
o
n
tr
ib
u
ted
to
im
p
r
o
v
e
d
class
if
icatio
n
ac
cu
r
ac
y
co
m
p
a
r
ed
to
th
e
s
in
g
le
DT
ap
p
r
o
ac
h
o
f
th
e
DT
m
o
d
el
[
2
4
]
.
Fig
u
r
e
8
.
C
o
m
p
a
r
is
o
n
o
f
th
e
p
er
f
o
r
m
a
n
ce
m
etr
ics
T
h
e
r
esu
lts
o
f
th
is
s
tu
d
y
r
ein
f
o
r
ce
th
e
ef
f
ec
tiv
en
ess
o
f
u
s
in
g
ML
tec
h
n
iq
u
es,
DT
,
an
d
RF
m
o
d
els,
in
d
etec
tin
g
n
etwo
r
k
s
ec
u
r
ity
i
n
cid
en
ts
in
W
SNs
.
T
h
ese
f
in
d
in
g
s
in
d
icate
t
h
at
n
etwo
r
k
ad
m
in
is
tr
ato
r
s
ca
n
im
p
lem
en
t
o
u
r
f
r
am
ewo
r
k
to
id
en
tify
an
d
m
itig
ate
s
ec
u
r
it
y
th
r
ea
ts
in
r
ea
l
-
tim
e,
as
a
r
e
s
u
lt
en
h
an
cin
g
th
e
s
ec
u
r
ity
p
o
s
tu
r
e
o
f
W
SN
in
f
r
astru
ctu
r
es.
W
h
ile
th
e
R
F
m
o
d
el
h
as
p
r
o
v
en
ju
s
t
h
ig
h
er
ac
cu
r
ac
y
th
an
th
e
DT
m
o
d
el,
it
was
s
tu
n
n
in
g
to
o
b
s
er
v
e
th
at
s
o
m
e
f
ea
tu
r
es
(
e
n
er
g
y
co
n
s
u
m
p
tio
n
a
n
d
n
etwo
r
k
l
o
ad
)
,
p
lay
ed
a
m
o
r
e
k
ey
r
o
le
in
th
e
class
if
icatio
n
p
r
o
ce
s
s
th
an
o
r
ig
in
ally
ex
p
ec
ted
,
in
d
icatin
g
th
at
f
u
tu
r
e
an
o
m
aly
d
etec
tio
n
s
y
s
tem
s
s
h
o
u
ld
co
n
s
id
er
th
ese
f
ac
to
r
s
t
o
a
d
v
an
ce
d
etec
tio
n
r
ates.
T
h
is
s
tu
d
y
h
as
lim
itatio
n
s
th
e
ac
tu
al
m
o
d
els
d
o
n
o
t
co
n
s
id
er
th
e
p
o
s
s
ib
le
im
p
ac
t
o
f
ad
v
an
cin
g
cy
b
er
th
r
ea
ts
an
d
f
lex
ib
le
attac
k
s
tr
ateg
ies,
r
eq
u
ir
i
n
g
co
n
tin
u
in
g
u
p
d
ates
an
d
r
e
-
tr
ai
n
in
g
to
p
r
eser
v
e
ef
f
ec
tiv
e
n
ess
.
Fu
tu
r
e
r
esear
ch
s
h
o
u
ld
ce
n
ter
o
n
th
e
d
esig
n
a
n
d
im
p
lem
en
tatio
n
o
f
a
s
am
p
le
W
SN
s
ec
u
r
e
co
m
m
u
n
icatio
n
in
f
r
astru
ctu
r
e
,
in
teg
r
atin
g
b
o
th
h
a
r
d
war
e
a
n
d
s
o
f
twar
e,
to
r
u
n
ex
p
e
r
im
en
tal
s
tu
d
ies o
f
th
e
p
r
o
p
o
s
ed
s
o
lu
tio
n
s
in
p
r
ac
tical
im
p
lem
en
tatio
n
ar
ea
s
[
2
5
]
.
4.
CO
NCLU
SI
O
N
Ou
r
s
tu
d
y
u
n
v
eils
th
e
ef
f
ec
ti
v
en
ess
o
f
m
ac
h
in
e
lear
n
in
g
a
lg
o
r
ith
m
s
,
p
ar
ticu
lar
l
y
R
F,
in
d
etec
tin
g
n
etwo
r
k
s
ec
u
r
ity
in
ci
d
en
ts
in
W
SN
s
.
B
y
u
s
in
g
en
g
in
ee
r
e
d
f
ea
tu
r
es
an
d
v
is
u
aliza
tio
n
,
we
s
ee
im
p
o
r
tan
t
in
s
ig
h
ts
in
to
th
e
b
asic
p
atter
n
s
an
d
d
ec
is
io
n
lo
g
ic
o
f
t
h
e
m
o
d
els.
T
h
e
p
r
o
p
o
s
ed
m
o
d
el
will
ass
is
t
n
etwo
r
k
ad
m
in
is
tr
ato
r
s
an
d
s
ec
u
r
ity
an
aly
s
ts
with
an
ef
f
icien
t
m
ec
h
an
is
m
f
o
r
d
etec
tin
g
a
n
d
m
itig
atin
g
n
etwo
r
k
s
ec
u
r
ity
in
cid
en
ts
in
W
SN
s
,
e
n
h
an
cin
g
th
e
s
ec
u
r
ity
p
o
s
tu
r
e
o
f
I
o
T
d
e
p
lo
y
m
e
n
ts
.
T
h
is
s
t
u
d
y
co
n
f
ir
m
ed
th
a
t
u
s
in
g
m
ac
h
in
e
lear
n
in
g
tech
n
i
q
u
es,
s
u
ch
as
DT
an
d
R
F,
ca
n
ad
v
an
ce
an
o
m
aly
d
etec
tio
n
a
cc
u
r
ac
y
in
W
SNs
,
r
eg
ar
d
in
g
tim
ely
th
r
ea
t
id
en
tific
atio
n
an
d
r
esp
o
n
s
e.
T
h
e
f
u
tu
r
e
w
o
r
k
o
f
W
SN
s
ec
u
r
ity
in
ter
ests
th
e
im
p
lem
en
tatio
n
o
f
a
s
am
p
le
W
SN
s
ec
u
r
e
co
m
m
u
n
icatio
n
in
f
r
astru
ctu
r
e,
lettin
g
ex
p
er
im
en
tal
s
tu
d
ies
ass
es
s
th
e
ef
f
ec
tiv
en
ess
o
f
p
r
o
p
o
s
e
d
s
ec
u
r
ity
m
o
d
els.
B
y
f
o
cu
s
in
g
o
n
p
r
ac
tical
im
p
lem
e
n
tatio
n
,
ex
p
er
im
en
tal
ev
alu
atio
n
,
an
d
i
n
d
u
s
tr
y
c
o
o
p
er
atio
n
,
we
ca
n
en
h
an
ce
th
e
e
v
o
lu
tio
n
o
f
well
a
n
d
r
esil
ien
t
s
ec
u
r
ity
m
o
d
els
f
o
r
W
SN
s
,
co
n
tr
ib
u
tin
g
t
o
s
tr
en
g
t
h
en
in
g
t
h
e
cy
b
e
r
s
ec
u
r
ity
o
f
I
o
T
d
ep
lo
y
m
en
ts
.
ACK
NO
WL
E
DG
E
M
E
NT
S
T
h
is
r
esear
ch
h
as
b
ee
n
f
u
n
d
e
d
b
y
th
e
Scien
ce
C
o
m
m
ittee
o
f
th
e
Min
is
tr
y
o
f
E
d
u
ca
tio
n
an
d
Scien
ce
o
f
th
e
R
ep
u
b
lic
o
f
Kaz
a
k
h
s
tan
(
Gr
an
t
No
.
AP1
9
6
8
0
3
4
5
)
.
W
e
th
an
k
th
e
i
n
s
titu
tio
n
s
f
o
r
th
e
s
u
p
p
o
r
t
o
f
f
u
n
d
in
g
.
RE
F
E
R
E
NC
E
S
[
1
]
S
.
D
a
ma
d
a
m,
M
.
Z
o
u
r
b
a
k
h
sh
,
R
.
J
a
v
i
d
a
n
,
a
n
d
A
.
F
a
r
o
u
g
h
i
,
“
A
n
i
n
t
e
l
l
i
g
e
n
t
i
o
t
b
a
se
d
t
r
a
f
f
i
c
l
i
g
h
t
m
a
n
a
g
e
me
n
t
sy
s
t
e
m
:
d
e
e
p
r
e
i
n
f
o
r
c
e
me
n
t
l
e
a
r
n
i
n
g
,
”
S
m
a
r
t
C
i
t
i
e
s
,
v
o
l
.
5
,
n
o
.
4
,
p
p
.
1
2
9
3
–
1
3
1
1
,
S
e
p
.
2
0
2
2
,
d
o
i
:
1
0
.
3
3
9
0
/
s
mart
c
i
t
i
e
s5
0
4
0
0
6
6
.
[
2
]
H
.
B
e
n
a
d
d
i
,
K
.
I
b
r
a
h
i
mi
,
A
.
B
e
n
s
l
i
m
a
n
e
,
a
n
d
J
.
Q
a
d
i
r
,
“
A
d
e
e
p
r
e
i
n
f
o
r
c
e
men
t
l
e
a
r
n
i
n
g
b
a
s
e
d
i
n
t
r
u
si
o
n
d
e
t
e
c
t
i
o
n
sy
s
t
e
m
(
d
r
l
-
i
d
s)
f
o
r
sec
u
r
i
n
g
w
i
r
e
l
e
ss
se
n
s
o
r
n
e
t
w
o
r
k
s
a
n
d
i
n
t
e
r
n
e
t
o
f
t
h
i
n
g
s,
”
i
n
L
e
c
t
u
r
e
N
o
t
e
s
o
f
t
h
e
I
n
s
t
i
t
u
t
e
f
o
r
C
o
m
p
u
t
e
r
S
c
i
e
n
c
e
s
,
S
o
c
i
a
l
-
I
n
f
o
r
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