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3
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[
3
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with
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[
3
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[
5
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.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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I
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J
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Sci
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Vo
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43
,
No
.
1
,
J
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20
26
:
345
-
35
4
346
T
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co
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ies
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tatic
f
u
s
io
n
s
tr
ateg
ies
an
d
f
o
cu
s
p
r
im
a
r
ily
o
n
o
v
er
all
ac
c
u
r
ac
y
,
wh
ile
th
e
r
ed
u
ctio
n
o
f
f
alse n
eg
ativ
es r
em
ain
s
r
elativ
ely
u
n
d
e
r
ex
p
lo
r
ed
in
h
ig
h
l
y
i
m
b
alan
ce
d
I
I
o
T
e
n
v
ir
o
n
m
en
ts
[
8
]
-
[
1
0
]
.
R
ec
en
t
ad
v
an
ce
s
in
co
n
v
o
lu
t
io
n
al
n
eu
r
al
n
etwo
r
k
(
C
NN)
-
L
STM
ar
ch
itectu
r
es,
tr
an
s
f
o
r
m
er
-
b
ase
d
m
o
d
els,
an
d
en
s
em
b
le
lear
n
in
g
tech
n
iq
u
es
h
a
v
e
f
u
r
th
er
im
p
r
o
v
e
d
an
o
m
al
y
d
etec
tio
n
ca
p
ab
ilit
y
in
co
m
p
le
x
I
I
o
T
s
ce
n
ar
io
s
[
9
]
-
[
1
2
]
.
I
n
ad
d
it
io
n
,
b
e
n
ch
m
ar
k
d
atasets
s
u
ch
as
b
o
tn
et
o
f
th
in
g
s
(
B
o
T
-
I
o
T
)
an
d
I
o
T
-
2
3
h
a
v
e
en
ab
led
m
o
r
e
r
ea
lis
tic
ev
alu
at
io
n
o
f
I
DS.
Ho
wev
er
,
ac
h
iev
i
n
g
r
eliab
le
d
etec
tio
n
with
m
in
im
al
m
is
s
ed
attac
k
s
r
em
ain
s
a
s
ig
n
if
ican
t
ch
allen
g
e
[
1
3
]
,
[
1
4
]
.
Fu
r
th
er
m
o
r
e,
r
ec
en
t
s
tu
d
ies
h
av
e
also
in
v
esti
g
ated
o
p
tim
ized
is
o
latio
n
f
o
r
est
m
o
d
els to
im
p
r
o
v
e
in
tr
u
s
io
n
d
etec
tio
n
p
er
f
o
r
m
an
ce
in
h
eter
o
g
en
e
o
u
s
I
I
o
T
en
v
ir
o
n
m
en
ts
[
1
5
]
.
T
o
ad
d
r
ess
th
is
is
s
u
e,
th
is
s
t
u
d
y
p
r
o
p
o
s
es
a
h
y
b
r
i
d
an
o
m
aly
d
etec
tio
n
f
r
am
ewo
r
k
th
at
in
teg
r
ates
au
to
en
co
d
er
,
is
o
lati
o
n
f
o
r
est,
an
d
L
STM
m
o
d
els
th
r
o
u
g
h
a
weig
h
ted
d
ec
is
io
n
f
u
s
io
n
s
tr
at
eg
y
[
6
]
–
[
8
]
.
R
ath
er
th
an
f
o
cu
s
in
g
s
o
lely
o
n
m
ax
i
m
izin
g
m
etr
ics
s
u
ch
as
ar
ea
u
n
d
er
th
e
c
u
r
v
e
(
AUC),
th
e
p
r
o
p
o
s
ed
ap
p
r
o
ac
h
p
r
io
r
itizes
d
etec
tio
n
r
eliab
ilit
y
b
y
r
ed
u
cin
g
f
alse
n
eg
ativ
e
s
wh
ile
m
ain
tain
in
g
b
alan
ce
d
p
er
f
o
r
m
an
ce
.
T
h
e
f
r
am
ewo
r
k
also
in
co
r
p
o
r
ates
f
ea
tu
r
e
n
o
r
m
aliza
tio
n
,
p
r
e
p
r
o
ce
s
s
in
g
,
an
d
class
im
b
alan
ce
h
an
d
lin
g
to
s
u
p
p
o
r
t
s
tab
le
m
o
d
el
p
er
f
o
r
m
a
n
ce
.
E
x
p
er
im
en
tal
e
v
alu
atio
n
o
n
th
e
B
o
T
-
I
o
T
d
ataset,
with
ad
d
itio
n
al
v
alid
atio
n
u
s
in
g
I
o
T
-
2
3
[
1
3
]
,
[
1
4
]
d
em
o
n
s
tr
ates
th
at
th
e
p
r
o
p
o
s
ed
f
r
am
ewo
r
k
ac
h
iev
es
a
f
av
o
r
ab
le
b
alan
ce
b
etwe
en
d
ete
ctio
n
ac
cu
r
a
cy
a
n
d
r
eliab
ilit
y
.
B
y
co
m
b
in
in
g
co
m
p
lem
en
tar
y
lea
r
n
in
g
p
ar
a
d
ig
m
s
,
th
e
f
r
am
ewo
r
k
p
r
o
v
id
es
i
m
p
r
o
v
e
d
r
o
b
u
s
tn
ess
f
o
r
an
o
m
aly
d
etec
tio
n
in
c
h
allen
g
in
g
I
I
o
T
e
n
v
ir
o
n
m
en
ts
.
T
h
e
m
ain
co
n
tr
ib
u
tio
n
s
o
f
t
h
is
s
tu
d
y
a
r
e
as
f
o
llo
ws:
i)
a
h
y
b
r
id
in
tr
u
s
io
n
d
etec
tio
n
f
r
am
e
wo
r
k
in
teg
r
atin
g
au
t
o
en
co
d
er
,
is
o
l
atio
n
f
o
r
est,
an
d
L
STM
th
r
o
u
g
h
a
weig
h
te
d
f
u
s
io
n
s
tr
ate
g
y
;
ii)
a
d
etec
tio
n
ap
p
r
o
ac
h
th
at
ex
p
licitly
p
r
i
o
r
itizes
f
alse
-
n
eg
ativ
e
r
ed
u
ct
io
n
in
h
ig
h
l
y
im
b
alan
ce
d
I
I
o
T
en
v
ir
o
n
m
e
n
ts
;
iii)
a
co
m
p
r
e
h
en
s
iv
e
ev
alu
ati
o
n
u
s
in
g
b
en
ch
m
ar
k
d
atasets
,
d
em
o
n
s
tr
atin
g
im
p
r
o
v
ed
d
ete
ctio
n
r
eliab
ilit
y
an
d
r
o
b
u
s
tn
ess
co
m
p
ar
e
d
with
s
tan
d
alo
n
e
m
o
d
els.
T
h
e
r
em
ain
d
er
o
f
th
is
p
a
p
er
is
o
r
g
an
ized
a
s
f
o
llo
ws.
Sectio
n
2
r
ev
iews
r
elate
d
wo
r
k
,
s
ec
tio
n
3
p
r
esen
ts
th
e
p
r
o
p
o
s
ed
m
et
h
o
d
o
lo
g
y
,
s
ec
tio
n
4
d
is
cu
s
s
es
th
e
ex
p
er
im
en
tal
r
esu
lts
,
an
d
s
ec
tio
n
5
c
o
n
clu
d
e
s
th
e
p
ap
er
a
n
d
o
u
tlin
es f
u
tu
r
e
r
esear
ch
d
ir
ec
tio
n
s
.
2.
RE
L
AT
E
D
WO
RK
T
h
e
i
n
c
r
e
a
s
i
n
g
a
d
o
p
t
i
o
n
o
f
I
I
o
T
t
e
c
h
n
o
l
o
g
i
e
s
h
a
s
d
r
i
v
e
n
s
ig
n
i
f
i
c
a
n
t
r
e
s
e
a
r
c
h
o
n
I
D
S
f
o
r
p
r
o
t
e
c
t
i
n
g
i
n
d
u
s
t
r
i
a
l
n
e
tw
o
r
k
s
a
g
a
i
n
s
t
c
y
b
e
r
t
h
r
e
a
t
s
.
T
r
a
d
i
ti
o
n
a
l
s
i
g
n
a
t
u
r
e
-
b
a
s
e
d
a
p
p
r
o
a
c
h
e
s
o
f
t
e
n
s
t
r
u
g
g
l
e
t
o
c
o
p
e
w
i
t
h
t
h
e
d
y
n
a
m
i
c
,
h
e
t
e
r
o
g
e
n
e
o
u
s
,
a
n
d
e
v
o
l
v
i
n
g
n
a
t
u
r
e
o
f
I
I
o
T
t
r
a
f
f
i
c.
A
s
a
r
es
u
l
t
,
m
a
c
h
i
n
e
l
e
a
r
n
i
n
g
a
n
d
d
e
e
p
l
e
a
r
n
i
n
g
t
e
c
h
n
i
q
u
e
s
h
a
v
e
b
e
c
o
m
e
w
i
d
e
ly
a
d
o
p
t
e
d
f
o
r
a
n
o
m
a
l
y
d
e
t
e
c
t
i
o
n
[
2
]
,
[
3
]
.
E
a
r
l
y
s
t
u
d
i
e
s
p
r
i
m
a
r
i
l
y
e
m
p
l
o
y
e
d
c
l
a
s
s
i
c
a
l
m
a
c
h
i
n
e
l
e
a
r
n
i
n
g
a
l
g
o
r
i
t
h
m
s
s
u
c
h
a
s
S
V
M
,
RF
,
a
n
d
K
N
N
[
3
]
,
[
5
]
.
W
h
i
l
e
t
h
e
s
e
a
p
p
r
o
a
c
h
e
s
a
r
e
c
o
m
p
u
t
a
t
i
o
n
a
l
l
y
e
f
f
i
c
i
e
n
t
a
n
d
r
e
l
a
t
i
v
e
l
y
i
n
t
e
r
p
r
e
t
a
b
l
e
,
t
h
e
i
r
p
e
r
f
o
r
m
a
n
c
e
o
f
t
e
n
d
e
p
e
n
d
s
o
n
e
x
t
e
n
s
i
v
e
f
e
a
t
u
r
e
e
n
g
i
n
e
e
r
i
n
g
a
n
d
m
a
y
b
e
l
i
m
i
t
e
d
w
h
e
n
m
o
d
e
l
i
n
g
c
o
m
p
l
e
x
n
o
n
l
i
n
e
a
r
r
e
l
a
t
i
o
n
s
h
i
p
s
a
n
d
t
e
m
p
o
r
a
l
d
e
p
e
n
d
e
n
c
i
e
s
i
n
i
n
d
u
s
t
r
i
a
l
t
r
a
f
f
i
c
[
4
]
,
[
5
]
.
T
o
ad
d
r
ess
th
ese
lim
itatio
n
s
,
d
ee
p
lear
n
in
g
ap
p
r
o
ac
h
es
h
av
e
b
ee
n
in
cr
ea
s
in
g
l
y
ex
p
l
o
r
ed
[
4
]
.
Au
to
en
c
o
d
er
-
b
ased
m
eth
o
d
s
lea
r
n
co
m
p
ac
t
r
ep
r
esen
tatio
n
s
o
f
n
o
r
m
al
tr
af
f
ic
an
d
d
etec
t
an
o
m
alies
th
r
o
u
g
h
r
ec
o
n
s
tr
u
ctio
n
er
r
o
r
s
,
wh
er
e
as
L
STM
n
etwo
r
k
s
ef
f
ec
tiv
el
y
ca
p
tu
r
e
tem
p
o
r
al
d
ep
en
d
e
n
cies
an
d
s
eq
u
e
n
tial
attac
k
b
eh
av
io
r
s
[
6
]
,
[
7
]
.
I
s
o
l
atio
n
f
o
r
est
h
as
also
g
ain
ed
atten
tio
n
d
u
e
to
its
ab
ilit
y
to
d
etec
t
an
o
m
alies
wi
th
o
u
t
r
eq
u
ir
i
n
g
lab
eled
attac
k
d
ata
[
8
]
.
Ho
wev
er
,
th
ese
in
d
iv
id
u
al
ap
p
r
o
ac
h
es
o
f
ten
e
x
h
ib
it
lim
itatio
n
s
r
elate
d
to
co
m
p
u
tatio
n
al
c
o
m
p
lex
ity
,
p
ar
am
ete
r
s
en
s
itiv
ity
,
o
r
r
estricte
d
d
etec
tio
n
ca
p
ab
ilit
y
u
n
d
er
h
ig
h
ly
im
b
alan
ce
d
tr
af
f
ic
c
o
n
d
itio
n
s
[
1
5
]
.
T
ab
le
1
s
u
m
m
ar
izes
r
ep
r
esen
tativ
e
in
tr
u
s
io
n
d
etec
tio
n
ap
p
r
o
ac
h
es
p
r
o
p
o
s
ed
f
o
r
I
I
o
T
e
n
v
ir
o
n
m
en
ts
.
T
h
e
s
elec
ted
s
tu
d
ies
co
v
er
m
ac
h
in
e
lear
n
in
g
,
d
ee
p
lear
n
in
g
,
an
d
h
y
b
r
id
f
r
am
ewo
r
k
s
,
h
ig
h
lig
h
tin
g
t
h
eir
s
tr
en
g
th
s
an
d
lim
itatio
n
s
[
9
]
,
[
1
0
]
.
T
h
is
co
m
p
ar
is
o
n
p
r
o
v
i
d
es
a
b
asis
f
o
r
id
en
tify
in
g
cu
r
r
en
t
r
esear
ch
g
ap
s
an
d
p
o
s
itio
n
in
g
th
e
p
r
o
p
o
s
ed
f
r
a
m
ewo
r
k
with
in
th
e
ex
is
tin
g
liter
atu
r
e
[
1
6
]
,
[
1
7
]
.
As
s
h
o
wn
in
T
ab
le
1
,
r
ec
en
t
s
tu
d
ies
in
cr
ea
s
in
g
ly
em
p
lo
y
h
y
b
r
id
i
n
tr
u
s
io
n
d
etec
tio
n
f
r
am
ewo
r
k
s
to
im
p
r
o
v
e
d
etec
t
io
n
r
o
b
u
s
tn
ess
b
y
co
m
b
in
in
g
m
u
ltip
le
lear
n
in
g
p
ar
ad
ig
m
s
.
Alth
o
u
g
h
th
ese
ap
p
r
o
ac
h
es
g
en
er
ally
o
u
tp
e
r
f
o
r
m
s
in
g
le
-
m
o
d
el
s
o
lu
tio
n
s
,
m
an
y
s
till
r
ely
o
n
s
tatic
f
u
s
io
n
m
ec
h
a
n
is
m
s
s
u
ch
as
m
ajo
r
ity
v
o
ti
n
g
o
r
s
im
p
l
e
av
er
ag
i
n
g
,
w
h
ich
im
p
licitly
ass
u
m
e
eq
u
a
l
r
eliab
ilit
y
am
o
n
g
co
m
p
o
n
en
t
m
o
d
el
s
.
T
h
is
as
s
u
m
p
tio
n
m
ay
b
e
u
n
s
u
itab
le
f
o
r
h
ig
h
ly
im
b
alan
ce
d
I
I
o
T
tr
af
f
ic,
wh
e
r
e
d
if
f
er
en
t
m
o
d
els
co
n
tr
ib
u
t
e
d
if
f
er
en
tly
to
a
n
o
m
aly
d
ete
ctio
n
p
er
f
o
r
m
an
ce
.
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:
2502
-
4
7
5
2
Hyb
r
id
ma
ch
in
e
lea
r
n
in
g
fr
a
mewo
r
k
fo
r
a
n
o
ma
ly
d
etec
tio
n
i
n
in
d
u
s
tr
ia
l I
o
T
… (
I
Dewa
Ma
d
e
Wid
ia
)
347
Fu
r
th
er
m
o
r
e
,
m
o
s
t
ex
is
tin
g
s
tu
d
ies
f
o
cu
s
p
r
im
a
r
ily
o
n
o
v
e
r
all
ac
cu
r
a
cy
o
r
AUC,
wh
ile
ex
p
licit
f
alse
-
n
eg
ativ
e
r
ed
u
ctio
n
r
ec
eiv
es
c
o
m
p
a
r
ativ
ely
less
atten
tio
n
.
I
n
in
d
u
s
tr
ial
en
v
ir
o
n
m
en
ts
,
m
is
s
ed
attac
k
s
ca
n
r
esu
lt
i
n
o
p
er
atio
n
al
d
is
r
u
p
tio
n
,
s
af
et
y
r
is
k
s
,
an
d
f
in
an
cial
lo
s
s
es
,
m
ak
in
g
d
etec
tio
n
r
eliab
ilit
y
a
cr
itical
d
esig
n
ob
jectiv
e.
T
o
ad
d
r
ess
th
ese
lim
itatio
n
s
,
th
is
s
tu
d
y
p
r
o
p
o
s
es
a
h
y
b
r
id
in
tr
u
s
io
n
d
etec
tio
n
f
r
am
e
wo
r
k
th
at
in
teg
r
ates
au
to
en
co
d
e
r
,
is
o
latio
n
f
o
r
est,
an
d
L
STM
m
o
d
els
th
r
o
u
g
h
a
weig
h
ted
d
ec
is
io
n
f
u
s
io
n
m
ec
h
an
is
m
[
1
8
]
–
[
2
0
]
.
U
n
lik
e
co
n
v
en
tio
n
al
en
s
em
b
le
a
p
p
r
o
ac
h
es
,
th
e
p
r
o
p
o
s
ed
f
r
am
ewo
r
k
ass
ig
n
s
d
if
f
er
en
t
weig
h
ts
ac
co
r
d
in
g
t
o
th
e
an
o
m
al
y
d
et
ec
tio
n
ca
p
ab
ilit
y
o
f
ea
ch
m
o
d
el,
th
er
eb
y
im
p
r
o
v
in
g
d
etec
ti
o
n
r
eliab
ilit
y
u
n
d
e
r
h
ig
h
ly
im
b
alan
ce
d
I
I
o
T
c
o
n
d
i
tio
n
s
.
L
ig
h
t
g
r
a
d
ien
t
b
o
o
s
tin
g
m
ac
h
in
e
(
L
i
g
h
tGB
M)
is
em
p
lo
y
ed
as
a
b
as
elin
e
m
o
d
el
f
o
r
c
o
m
p
ar
ati
v
e
ev
alu
a
tio
n
.
B
y
em
p
h
asizin
g
f
alse
-
n
e
g
ativ
e
r
ed
u
ctio
n
an
d
lev
er
ag
i
n
g
co
m
p
lem
en
tar
y
d
etec
tio
n
p
ar
ad
i
g
m
s
,
th
e
p
r
o
p
o
s
ed
f
r
am
ewo
r
k
aim
s
to
b
r
id
g
e
th
e
g
a
p
b
etwe
en
d
etec
tio
n
ac
cu
r
ac
y
a
n
d
o
p
er
atio
n
al
r
eliab
ilit
y
in
r
ea
l
-
wo
r
ld
I
I
o
T
en
v
i
r
o
n
m
e
n
ts
.
T
ab
le
1
.
Su
m
m
a
r
y
o
f
r
elate
d
wo
r
k
o
n
I
I
o
T
I
DS
Ref
e
r
e
n
c
e
s
M
e
t
h
o
d
S
t
r
e
n
g
t
h
Li
mi
t
a
t
i
o
n
[
6
]
A
u
t
o
e
n
c
o
d
e
r
D
e
t
e
c
t
s
u
n
se
e
n
a
n
o
m
a
l
i
e
s
W
e
a
k
t
e
m
p
o
r
a
l
m
o
d
e
l
i
n
g
[
7
]
LSTM
C
a
p
t
u
r
e
s
t
e
m
p
o
r
a
l
p
a
t
t
e
r
n
s
H
i
g
h
c
o
m
p
u
t
a
t
i
o
n
a
l
c
o
s
t
[
6
]
,
[
8
]
A
u
t
o
e
n
c
o
d
e
r
+
i
s
o
l
a
t
i
o
n
f
o
r
e
s
t
S
t
r
u
c
t
u
r
a
l
+
o
u
t
l
i
e
r
d
e
t
e
c
t
i
o
n
S
t
a
t
i
c
f
u
si
o
n
[
5
]
,
[
1
5
]
En
se
mb
l
e
mac
h
i
n
e
l
e
a
r
n
i
n
g
I
mp
r
o
v
e
d
r
o
b
u
st
n
e
ss
Eq
u
a
l
-
w
e
i
g
h
t
a
ssu
m
p
t
i
o
n
[
4
]
,
[
5
]
H
y
b
r
i
d
mac
h
i
n
e
l
e
a
r
n
i
n
g
I
D
S
H
i
g
h
e
r
d
e
t
e
c
t
i
o
n
a
c
c
u
r
a
c
y
Li
mi
t
e
d
f
a
l
se
-
n
e
g
a
t
i
v
e
f
o
c
u
s
[
9
]
C
N
N
–
LST
M
S
p
a
t
i
a
l
-
t
e
mp
o
r
a
l
l
e
a
r
n
i
n
g
C
o
m
p
u
t
a
t
i
o
n
a
l
l
y
i
n
t
e
n
si
v
e
[
1
1
]
Tr
a
n
sf
o
r
mer I
D
S
Lo
n
g
-
r
a
n
g
e
d
e
p
e
n
d
e
n
c
y
l
e
a
r
n
i
n
g
La
r
g
e
t
r
a
i
n
i
n
g
r
e
q
u
i
r
e
m
e
n
t
s
Th
i
s
w
o
r
k
A
u
t
o
e
n
c
o
d
e
r
+
i
s
o
l
a
t
i
o
n
f
o
r
e
s
t
+
LST
M
(
w
e
i
g
h
t
e
d
f
u
si
o
n
)
F
a
l
se
-
n
e
g
a
t
i
v
e
r
e
d
u
c
t
i
o
n
a
n
d
d
e
t
e
c
t
i
o
n
r
e
l
i
a
b
i
l
i
t
y
S
l
i
g
h
t
l
y
l
o
w
e
r
A
U
C
t
h
a
n
L
i
g
h
t
G
B
M
3.
M
E
T
H
O
D
T
h
is
s
ec
tio
n
p
r
esen
ts
th
e
a
r
ch
i
tectu
r
e
an
d
o
p
e
r
atio
n
al
wo
r
k
f
l
o
w
o
f
th
e
p
r
o
p
o
s
ed
h
y
b
r
id
I
D
S
f
o
r
I
I
o
T
en
v
ir
o
n
m
en
ts
.
T
h
e
f
r
am
ewo
r
k
in
teg
r
ates
m
u
ltip
le
lear
n
in
g
p
ar
a
d
ig
m
s
to
ad
d
r
ess
th
e
li
m
itatio
n
s
o
f
s
in
g
le
-
m
o
d
el
in
t
r
u
s
io
n
d
etec
tio
n
ap
p
r
o
ac
h
es
a
n
d
im
p
r
o
v
e
d
etec
tio
n
r
elia
b
ilit
y
u
n
d
er
h
ig
h
l
y
i
m
b
alan
ce
d
n
etwo
r
k
tr
af
f
ic
co
n
d
itio
n
s
.
T
h
e
o
v
er
all
d
etec
tio
n
p
r
o
ce
s
s
co
n
s
is
ts
o
f
f
iv
e
m
ain
s
tag
es:
d
ata
ac
q
u
is
itio
n
;
d
ata
p
r
ep
r
o
ce
s
s
in
g
an
d
f
ea
t
u
r
e
t
r
an
s
f
o
r
m
atio
n
;
m
o
d
el
-
s
p
ec
if
ic
an
o
m
aly
d
etec
tio
n
;
weig
h
te
d
d
ec
is
io
n
f
u
s
io
n
;
p
er
f
o
r
m
an
ce
ev
al
u
atio
n
[
1
6
]
,
[
2
1
]
.
Fig
u
r
e
1
illu
s
tr
ates
th
e
o
v
e
r
all
ar
ch
itectu
r
e
o
f
th
e
p
r
o
p
o
s
ed
f
r
am
ewo
r
k
.
Netwo
r
k
tr
af
f
ic
d
a
ta
ar
e
f
ir
s
t
p
r
ep
r
o
ce
s
s
ed
th
r
o
u
g
h
f
ea
tu
r
e
s
elec
tio
n
,
ca
teg
o
r
ical
en
co
d
in
g
,
an
d
f
ea
tu
r
e
n
o
r
m
aliza
tio
n
.
T
h
e
p
r
o
ce
s
s
ed
d
ata
ar
e
s
u
b
s
eq
u
en
tly
an
aly
ze
d
b
y
m
u
ltip
le
an
o
m
aly
d
etec
tio
n
m
o
d
els
o
p
er
atin
g
in
p
a
r
allel.
Fin
ally
,
th
e
o
u
tp
u
ts
g
en
er
ated
b
y
th
e
in
d
iv
id
u
al
m
o
d
els
ar
e
ag
g
r
eg
ated
th
r
o
u
g
h
a
weig
h
ted
d
ec
is
io
n
f
u
s
io
n
m
e
ch
an
is
m
to
p
r
o
d
u
ce
th
e
f
in
al
class
if
icatio
n
r
esu
lt.
B
y
co
m
b
in
in
g
r
ec
o
n
s
tr
u
ctio
n
-
b
ased
lear
n
in
g
,
s
tatis
tical
an
o
m
aly
d
etec
tio
n
,
an
d
tem
p
o
r
al
s
eq
u
en
ce
m
o
d
elin
g
,
th
e
f
r
am
ewo
r
k
aim
s
to
im
p
r
o
v
e
r
o
b
u
s
tn
ess
an
d
d
ete
ctio
n
r
eliab
ilit
y
in
h
eter
o
g
en
e
o
u
s
I
I
o
T
en
v
ir
o
n
m
en
ts
[
1
1
]
,
[
1
5
]
,
[
2
1
]
.
Fig
u
r
e
1
.
Hy
b
r
id
I
DS a
r
ch
itectu
r
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
5
2
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
43
,
No
.
1
,
J
u
ly
20
26
:
345
-
35
4
348
3
.
1
.
Da
t
a
s
et
des
cr
iptio
n
T
h
e
p
r
o
p
o
s
ed
f
r
am
ewo
r
k
is
e
v
alu
ated
u
s
in
g
two
p
u
b
licly
a
v
ailab
le
I
I
o
T
d
atasets
:
B
o
T
-
I
o
T
an
d
I
o
T
-
23
.
B
o
T
-
I
o
T
c
o
n
tain
s
a
wid
e
r
an
g
e
o
f
attac
k
s
ce
n
ar
io
s
,
in
clu
d
in
g
d
en
ial
-
of
-
s
er
v
ice,
p
r
o
b
in
g
,
an
d
in
f
o
r
m
atio
n
th
ef
t
ac
tiv
ities
,
m
ak
in
g
it
s
u
itab
le
f
o
r
lar
g
e
-
s
ca
le
in
tr
u
s
io
n
d
etec
tio
n
r
esear
ch
[
1
3
]
,
[
1
4
]
.
T
o
e
v
alu
ate
th
e
g
en
er
aliza
tio
n
ca
p
ab
ilit
y
o
f
t
h
e
p
r
o
p
o
s
ed
f
r
am
ewo
r
k
,
ad
d
i
tio
n
al
v
alid
atio
n
is
p
er
f
o
r
m
e
d
u
s
in
g
th
e
I
o
T
-
23
d
ataset,
wh
ich
in
clu
d
es
r
ea
l
-
w
o
r
ld
I
o
T
m
alwa
r
e
tr
af
f
ic
a
n
d
b
en
ig
n
n
etwo
r
k
b
e
h
av
io
r
.
Du
e
to
th
e
lar
g
e
s
ize
o
f
th
e
B
o
T
-
I
o
T
d
ataset,
a
r
ep
r
es
en
tativ
e
s
am
p
le
co
n
tain
in
g
a
p
p
r
o
x
im
ately
2
.
2
m
illi
o
n
r
ec
o
r
d
s
was
g
en
er
ated
u
s
in
g
p
r
o
b
ab
ilis
tic
s
am
p
lin
g
with
a
3
%
in
clu
s
io
n
r
ate.
B
o
th
d
atasets
ex
h
ib
it
s
ig
n
if
ica
n
t
class
im
b
alan
ce
,
r
ef
lectin
g
r
ea
lis
tic
in
d
u
s
tr
ial
n
etwo
r
k
co
n
d
itio
n
s
.
Fig
u
r
e
2
p
r
esen
ts
t
h
e
class
d
is
tr
ib
u
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led
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ial
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al
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elate
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les.
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g
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ith
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ic
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lar
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er
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o
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ig
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ateg
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u
r
e
2
.
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lass
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is
tr
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tio
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teg
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(
lo
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3
.
2
.
Da
t
a
s
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pro
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ing
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o
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ar
e
t
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ata
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el
tr
ain
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a
s
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u
ctu
r
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d
p
r
ep
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ce
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ip
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lied
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u
s
tr
ated
in
Fig
u
r
e
3
.
T
h
e
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r
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r
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teg
o
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ical
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n
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m
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lin
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Hig
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ca
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d
in
ality
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r
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ch
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I
P
ad
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MA
C
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o
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m
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e
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e
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u
r
e
3
.
Data
p
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ip
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k
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af
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ic
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:
2502
-
4
7
5
2
Hyb
r
id
ma
ch
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s
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… (
I
Dewa
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e
Wid
ia
)
349
3.
3
.
M
o
del a
rc
hite
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T
h
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p
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ataset
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iv
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ets
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s
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ig
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tati
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ase
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o
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tab
u
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ata
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e
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o
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r
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e
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r
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ter
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n
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.
T
h
e
m
o
d
el
is
tr
ain
ed
o
n
th
e
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ll m
u
lti
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class
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ataset
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p
r
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v
i
d
e
a
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m
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r
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en
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iv
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o
m
p
ar
is
o
n
with
th
e
p
r
o
p
o
s
ed
h
y
b
r
id
f
r
am
ewo
r
k
[
2
2
]
.
−
I
s
o
latio
n
f
o
r
est
(
s
tatis
tical
an
o
m
aly
d
etec
tio
n
)
is
an
en
s
e
m
b
le
-
b
ased
a
n
o
m
aly
d
etec
tio
n
alg
o
r
it
h
m
th
at
is
o
lates
an
o
m
alies
b
y
r
ec
u
r
s
iv
ely
p
ar
titi
o
n
in
g
th
e
f
ea
tu
r
e
s
p
ac
e.
B
ec
au
s
e
an
o
m
alies
ar
e
ty
p
ically
r
ar
e
an
d
d
if
f
er
en
t
f
r
o
m
n
o
r
m
al
o
b
s
er
v
atio
n
s
,
th
ey
ca
n
b
e
is
o
lated
with
f
ewe
r
p
ar
titi
o
n
s
,
m
ak
in
g
is
o
lati
o
n
f
o
r
est
ef
f
icien
t f
o
r
lar
g
e
-
s
ca
le
d
atase
ts
[
8
]
.
−
Au
to
en
co
d
e
r
(
r
ec
o
n
s
tr
u
ctio
n
-
b
ased
d
etec
tio
n
)
f
o
c
u
s
es
o
n
lear
n
in
g
r
ep
r
esen
tatio
n
s
o
f
n
o
r
m
al
tr
af
f
ic
b
eh
av
io
r
.
T
h
e
m
o
d
el
co
n
s
is
ts
o
f
an
en
c
o
d
er
t
h
at
co
m
p
r
ess
es
in
p
u
t
f
ea
tu
r
es
in
to
a
laten
t
r
e
p
r
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tatio
n
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d
a
d
ec
o
d
er
th
at
r
ec
o
n
s
tr
u
cts
th
e
o
r
i
g
in
al
in
p
u
t.
A
n
o
m
alies
ar
e
d
etec
ted
wh
e
n
th
e
r
ec
o
n
s
tr
u
ctio
n
er
r
o
r
ex
ce
ed
s
a
p
r
e
d
ef
in
ed
t
h
r
esh
o
l
d
,
in
d
icatin
g
d
ev
iatio
n
f
r
o
m
le
ar
n
ed
n
o
r
m
al
p
atter
n
s
[
6
]
.
−
L
STM
(
tem
p
o
r
al
an
o
m
aly
d
etec
tio
n
)
n
etwo
r
k
s
ar
e
d
esig
n
ed
to
ca
p
t
u
r
e
l
ong
-
ter
m
d
ep
en
d
en
cies
i
n
s
eq
u
en
tial
d
ata
an
d
ar
e
th
er
ef
o
r
e
well
-
s
u
ited
f
o
r
an
aly
zi
n
g
n
etwo
r
k
tr
a
f
f
ic
f
lo
ws
[
1
3
]
.
B
y
lear
n
in
g
tem
p
o
r
al
p
atter
n
s
o
f
n
o
r
m
al
tr
af
f
ic
b
e
h
av
io
r
,
L
STM
m
o
d
els
ca
n
d
etec
t
ab
n
o
r
m
al
s
eq
u
en
ce
p
atter
n
s
ass
o
ciate
d
with
cy
b
er
-
attac
k
s
[
7
]
.
Fig
u
r
e
4
illu
s
tr
ates
th
e
au
to
e
n
co
d
er
ar
c
h
itectu
r
e
u
s
ed
in
th
e
p
r
o
p
o
s
ed
f
r
am
ewo
r
k
.
T
h
e
en
co
d
er
co
m
p
r
ess
es
h
ig
h
-
d
im
en
s
io
n
al
tr
af
f
ic
f
ea
tu
r
es
in
to
a
laten
t
r
ep
r
esen
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n
,
wh
ile
th
e
d
ec
o
d
er
r
ec
o
n
s
tr
u
cts
th
e
o
r
ig
in
al
in
p
u
t.
T
h
e
m
o
d
el
i
s
tr
ain
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e
x
clu
s
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ely
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n
o
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m
al
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af
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s
am
p
les,
en
a
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it
t
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ar
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ter
is
tics
o
f
leg
itima
te
n
e
two
r
k
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eh
a
v
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r
.
Fig
u
r
e
4
.
Au
t
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en
co
d
er
ar
c
h
itectu
r
e
f
o
r
an
o
m
al
y
d
etec
tio
n
T
h
e
d
ec
o
d
e
r
th
en
attem
p
ts
to
r
ec
o
n
s
tr
u
ct
th
e
o
r
ig
in
al
in
p
u
t
d
ata
f
r
o
m
th
e
laten
t
r
ep
r
esen
tatio
n
.
Du
r
in
g
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e
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ai
n
in
g
p
h
ase,
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e
au
to
en
co
d
er
is
tr
ain
ed
u
s
in
g
o
n
ly
n
o
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m
al
tr
af
f
ic
s
am
p
les,
all
o
win
g
th
e
m
o
d
el
to
lear
n
th
e
ty
p
ical
s
tr
u
ctu
r
e
o
f
l
eg
itima
te
n
etwo
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k
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eh
av
io
r
.
As
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r
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e
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o
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el
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o
m
es
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ig
h
ly
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s
wh
en
p
r
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s
s
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o
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An
an
o
m
a
ly
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e
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ased
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th
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to
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o
n
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tr
u
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u
tp
u
t
̂
wh
ich
ca
n
b
e
e
x
p
r
ess
ed
[
6
]
:
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
5
2
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
43
,
No
.
1
,
J
u
ly
20
26
:
345
-
35
4
350
=
‖
−
̂
‖
2
wh
er
e
E
r
ep
r
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ts
th
e
r
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s
tr
u
ctio
n
er
r
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r
.
I
f
th
e
r
ec
o
n
s
tr
u
ctio
n
er
r
o
r
ex
ce
ed
s
a
p
r
ed
e
f
in
ed
th
r
esh
o
ld
,
th
e
co
r
r
esp
o
n
d
in
g
tr
af
f
ic
in
s
tan
ce
is
class
if
ied
as
an
o
m
alo
u
s
.
T
h
is
m
ec
h
an
is
m
allo
ws
t
h
e
a
u
to
en
co
d
e
r
to
d
etec
t
d
ev
iatio
n
s
f
r
o
m
lear
n
e
d
n
o
r
m
al
tr
af
f
ic
p
atter
n
s
,
m
ak
in
g
it
p
ar
ticu
lar
ly
ef
f
ec
tiv
e
f
o
r
id
e
n
tify
in
g
u
n
k
n
o
wn
o
r
p
r
ev
io
u
s
ly
u
n
s
ee
n
attac
k
s
.
3.
4
.
Dec
is
io
n f
us
io
n m
ec
ha
nis
m
T
h
e
f
in
al
s
tag
e
o
f
th
e
p
r
o
p
o
s
ed
f
r
am
ew
o
r
k
em
p
lo
y
s
a
d
ec
is
io
n
f
u
s
io
n
m
ec
h
an
is
m
t
o
co
m
b
in
e
th
e
p
r
ed
ictio
n
s
o
f
all
b
ase
lear
n
e
r
s
in
to
a
s
in
g
le
o
u
tco
m
e.
E
ac
h
in
d
iv
id
u
al
m
o
d
el
p
r
o
d
u
ce
s
a
b
in
ar
y
d
ec
is
io
n
,
wh
er
e
0
r
ep
r
esen
ts
n
o
r
m
al
tr
a
f
f
ic
an
d
1
in
d
icate
s
an
o
m
alo
u
s
b
eh
av
io
r
.
Th
ese
o
u
tp
u
ts
ar
e
ag
g
r
eg
ated
t
h
r
o
u
g
h
a
weig
h
ted
v
o
tin
g
s
ch
em
e
,
d
esig
n
ed
to
lev
er
ag
e
th
e
co
m
p
lem
en
tar
y
s
tr
en
g
th
s
o
f
d
if
f
er
e
n
t
m
o
d
els.
Sp
ec
if
ically
,
th
e
L
i
g
h
tGB
M
m
o
d
el
is
ass
ig
n
ed
a
weig
h
t
o
f
1
.
0
,
r
ef
lectin
g
its
b
ala
n
ce
d
ca
p
ab
ilit
y
in
h
an
d
lin
g
s
tr
u
ctu
r
ed
d
ata.
T
h
e
is
o
latio
n
f
o
r
est
is
g
iv
en
a
lo
wer
weig
h
t
o
f
0
.
5
,
s
in
ce
it
is
ef
f
ec
tiv
e
f
o
r
o
u
tlier
d
etec
tio
n
b
u
t o
f
ten
g
en
e
r
ates h
ig
h
er
f
alse
-
p
o
s
itiv
e
r
ates in
co
m
p
lex
I
I
o
T
s
ce
n
ar
io
s
.
T
h
e
a
u
t
o
e
n
c
o
d
e
r
r
e
c
e
i
v
e
s
a
w
e
i
g
h
t
o
f
1
.
2
,
a
s
it
c
a
p
t
u
r
e
s
n
o
n
l
i
n
e
a
r
f
e
a
t
u
r
e
d
e
p
e
n
d
e
n
c
i
e
s
a
n
d
d
e
m
o
n
s
t
r
a
t
es
r
o
b
u
s
t
n
e
s
s
u
n
d
e
r
n
o
i
s
e
.
F
i
n
al
l
y
,
t
h
e
L
S
T
M
i
s
ass
i
g
n
e
d
t
h
e
h
i
g
h
e
s
t
w
e
i
g
h
t
o
f
1
.
5
,
d
u
e
t
o
i
t
s
a
b
il
i
t
y
t
o
c
a
p
t
u
r
e
t
e
m
p
o
r
a
l
d
y
n
a
m
i
c
s
a
c
r
o
s
s
s
e
q
u
e
n
ti
a
l
t
r
a
f
f
i
c
f
l
o
w
s
.
A
f
u
s
i
o
n
t
h
r
es
h
o
l
d
o
f
2
.
0
i
s
a
p
p
li
e
d
:
i
f
t
h
e
w
e
i
g
h
t
e
d
s
u
m
o
f
a
n
o
m
a
l
y
v
o
te
s
e
x
c
ee
d
s
t
h
i
s
v
a
l
u
e
,
t
h
e
s
am
p
l
e
is
c
la
s
s
i
f
i
e
d
a
s
a
n
o
m
a
l
o
u
s
.
T
h
is
t
h
r
es
h
o
l
d
is
e
m
p
i
r
i
c
a
l
l
y
c
h
o
s
e
n
t
o
p
r
i
o
r
i
ti
ze
r
e
c
a
l
l
,
t
h
e
r
e
b
y
r
e
d
u
c
i
n
g
t
h
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p
r
o
b
a
b
i
l
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t
y
o
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m
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s
e
d
a
t
ta
c
k
s
,
w
h
i
c
h
is
c
r
it
i
c
al
i
n
I
I
o
T
s
e
c
u
r
i
t
y
.
At
t
h
e
s
a
m
e
t
i
m
e,
t
h
e
w
e
i
g
h
te
d
d
e
s
i
g
n
h
e
l
p
s
m
a
i
n
t
a
i
n
p
r
e
c
is
i
o
n
,
p
r
o
v
i
d
i
n
g
a
b
al
a
n
c
e
d
a
n
d
r
e
l
i
a
b
l
e
d
e
t
e
c
t
i
o
n
s
t
r
a
t
e
g
y
.
T
h
e
a
n
o
m
a
ly
s
c
o
r
e
i
s
c
a
l
c
u
la
t
e
d
u
s
i
n
g
a
we
i
g
h
t
e
d
a
g
g
r
e
g
a
t
i
o
n
o
f
m
o
d
e
l
p
r
e
d
i
c
t
o
r
[
1
4
]
:
=
∑
=
1
wh
e
r
e
r
e
p
r
ese
n
t
t
h
e
b
in
ar
y
o
u
t
p
u
t
o
f
t
h
e
i
-
t
h
m
o
d
el
a
n
d
r
e
p
r
es
en
t
its
ass
ig
n
ed
we
ig
h
t
.
I
n
t
h
is
s
tu
d
y
th
e
wei
g
h
ts
ar
e
em
p
i
r
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ca
l
ly
d
et
e
r
m
in
e
d
b
ase
d
o
n
v
ali
d
ati
o
n
p
e
r
f
o
r
m
an
ce
.
ℎ
=
1
.
0
,
=
0
.
5
,
=
1
.
2
,
=
1
.
5
T
h
e
f
in
al
class
i
f
icatio
n
d
ec
is
io
n
is
d
ef
in
ed
as:
=
{
1
,
≥
0
,
<
wh
e
r
e
r
e
p
r
es
en
t
t
h
e
a
n
o
m
al
y
th
r
es
h
o
l
d
.
T
h
e
t
h
r
esh
o
ld
v
al
u
e
is
s
e
t
t
o
T
=
2
.
0
t
o
p
r
i
o
r
itiz
e
r
e
ca
ll
a
n
d
r
e
d
u
c
e
th
e
l
ik
eli
h
o
o
d
o
f
f
als
e
n
e
g
ati
v
e
s
,
w
h
i
c
h
is
c
r
i
tic
al
i
n
I
I
o
T
s
ec
u
r
it
y
e
n
v
ir
o
n
m
e
n
ts
.
3.
5
.
T
ra
ini
ng
a
nd
ev
a
lua
t
io
n se
t
up
T
h
e
f
in
al
co
m
p
o
n
en
t
o
f
th
e
f
r
am
ewo
r
k
in
v
o
lv
es
th
e
tr
ain
in
g
p
r
o
ce
s
s
an
d
ev
alu
atio
n
p
r
o
to
c
o
l
u
s
ed
to
v
alid
ate
s
y
s
tem
p
er
f
o
r
m
an
ce
.
E
ac
h
m
o
d
el:
au
to
en
co
d
er
,
is
o
latio
n
f
o
r
est
,
an
d
L
STM
was
tr
ain
ed
an
d
f
in
e
-
tu
n
ed
ac
c
o
r
d
in
g
to
its
s
p
ec
if
ic
ar
ch
itectu
r
e
an
d
lear
n
in
g
c
h
ar
ac
ter
is
tics
.
T
h
e
au
to
e
n
co
d
er
was
o
p
tim
ized
t
o
m
in
im
ize
r
ec
o
n
s
tr
u
ctio
n
e
r
r
o
r
,
th
e
is
o
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f
o
r
est
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f
ig
u
r
ed
with
ap
p
r
o
p
r
iate
tr
ee
d
ep
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an
d
s
u
b
s
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p
lin
g
s
ize,
an
d
th
e
L
S
T
M
was
tr
ain
ed
o
n
s
eq
u
en
tia
l
tr
af
f
ic
d
ata
to
ca
p
tu
r
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tem
p
o
r
al
d
ep
e
n
d
en
cies.
T
o
en
s
u
r
e
f
air
n
ess
an
d
r
o
b
u
s
t
n
ess
,
th
e
d
ataset
was
p
ar
titi
o
n
ed
in
to
tr
ain
in
g
,
v
alid
ati
o
n
,
an
d
test
in
g
s
u
b
s
ets
u
s
in
g
s
tr
atif
ied
s
am
p
lin
g
to
p
r
eser
v
e
th
e
im
b
alan
ce
r
at
io
b
etwe
en
n
o
r
m
al
an
d
an
o
m
alo
u
s
in
s
tan
ce
s
.
Hy
p
er
p
ar
a
m
eter
tu
n
in
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was
co
n
d
u
cted
th
r
o
u
g
h
g
r
id
s
ea
r
ch
a
n
d
cr
o
s
s
-
v
alid
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n
t
o
id
en
tif
y
o
p
tim
al
co
n
f
ig
u
r
atio
n
s
.
E
v
alu
atio
n
m
e
tr
ics
in
clu
d
ed
p
r
ec
is
io
n
,
r
ec
all
,
F1
-
s
co
r
e,
an
d
a
r
ea
u
n
d
er
th
e
r
ec
eiv
er
o
p
e
r
atin
g
ch
ar
ac
ter
is
tic
(
R
OC
)
cu
r
v
e
AUC,
with
p
ar
ticu
lar
em
p
h
asis
o
n
m
in
im
izin
g
f
alse
n
eg
ativ
es
d
u
e
to
th
eir
cr
itical
im
p
ac
t
o
n
in
tr
u
s
io
n
d
etec
tio
n
[
2
2
]
.
T
h
e
r
esu
lts
o
f
th
is
s
etu
p
p
r
o
v
id
e
d
a
r
ig
o
r
o
u
s
b
asis
f
o
r
ass
es
s
in
g
b
o
th
th
e
s
tan
d
alo
n
e
m
o
d
els an
d
th
e
e
f
f
ec
tiv
en
ess
o
f
th
e
p
r
o
p
o
s
ed
f
u
s
io
n
s
tr
ateg
y
.
4.
RE
SU
L
T
S
AND
AN
A
L
Y
SI
S
T
h
is
s
ec
tio
n
p
r
esen
ts
th
e
ex
p
e
r
im
en
tal
r
esu
lts
o
b
tain
ed
f
r
o
m
th
e
p
r
o
p
o
s
ed
h
y
b
r
id
in
tr
u
s
io
n
d
etec
tio
n
f
r
am
ewo
r
k
.
T
h
e
an
al
y
s
is
f
o
cu
s
es
o
n
ev
alu
atin
g
t
h
e
d
etec
tio
n
p
er
f
o
r
m
a
n
ce
o
f
in
d
iv
id
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m
o
d
els
as
well
as
th
e
o
v
er
all
h
y
b
r
id
s
y
s
tem
.
T
h
e
e
x
p
er
im
en
ts
wer
e
co
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d
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cte
d
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s
in
g
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B
o
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ataset,
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v
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m
e
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k
tr
af
f
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e
n
er
ated
f
r
o
m
r
ea
lis
tic
I
o
T
attac
k
s
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n
ar
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s
.
T
h
e
ev
alu
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aim
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to
ass
es
s
h
o
w
d
if
f
er
en
t
d
etec
tio
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p
a
r
ad
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m
s
p
er
f
o
r
m
u
n
d
er
h
i
g
h
ly
im
b
alan
ce
d
I
I
o
T
tr
af
f
ic
c
o
n
d
itio
n
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4
7
5
2
Hyb
r
id
ma
ch
in
e
lea
r
n
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fr
a
mewo
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k
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r
a
n
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ly
d
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tio
n
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in
d
u
s
tr
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l I
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… (
I
Dewa
Ma
d
e
Wid
ia
)
351
4
.
1
.
E
x
perim
ent
a
l e
nv
ir
o
nm
ent
T
h
e
ex
p
er
im
en
ts
wer
e
co
n
d
u
c
ted
u
s
in
g
Go
o
g
le
C
o
lab
Pro
+,
a
clo
u
d
-
b
ased
co
m
p
u
tin
g
p
lat
f
o
r
m
th
at
p
r
o
v
id
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s
ca
lab
le
c
o
m
p
u
tatio
n
al
r
eso
u
r
ce
s
f
o
r
m
ac
h
in
e
lear
n
in
g
w
o
r
k
lo
a
d
s
.
T
h
e
p
latf
o
r
m
was
s
elec
ted
t
o
ef
f
icien
tly
p
r
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ce
s
s
th
e
lar
g
e
-
s
ca
le
B
o
T
-
I
o
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d
ataset,
wh
ic
h
co
n
t
ain
s
m
illi
o
n
s
o
f
n
etw
o
r
k
tr
a
f
f
ic
r
ec
o
r
d
s
r
ep
r
esen
tativ
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o
f
m
o
d
e
r
n
cy
b
e
r
th
r
ea
ts
[
1
3
]
.
Mo
d
el
tr
ain
in
g
a
n
d
ev
alu
atio
n
wer
e
p
er
f
o
r
m
ed
u
s
in
g
Py
th
o
n
3
.
1
0
.
T
en
s
o
r
Flo
w
2
.
x
was
em
p
lo
y
ed
f
o
r
th
e
au
to
en
c
o
d
er
an
d
L
STM
m
o
d
els,
wh
ile
Scik
it
-
lear
n
was
u
s
ed
f
o
r
is
o
latio
n
f
o
r
est,
p
r
ep
r
o
ce
s
s
in
g
,
an
d
ev
alu
atio
n
m
etr
ics.
L
ig
h
tGB
M
was
u
tili
ze
d
f
o
r
th
e
b
aselin
e
class
if
ier
.
All
ex
p
er
im
e
n
ts
wer
e
ex
ec
u
te
d
u
s
in
g
a
h
ig
h
-
p
e
r
f
o
r
m
an
ce
e
n
v
ir
o
n
m
e
n
t
eq
u
ip
p
ed
with
m
u
lti
-
co
r
e
C
PUs
,
s
u
b
s
tan
tial
m
em
o
r
y
ca
p
ac
ity
,
an
d
GPU
ac
ce
ler
atio
n
.
T
h
is
c
o
n
f
ig
u
r
atio
n
en
s
u
r
e
d
s
u
f
f
icien
t
co
m
p
u
tatio
n
al
r
eso
u
r
ce
s
an
d
r
ep
r
o
d
u
ci
b
ilit
y
f
o
r
e
v
alu
atin
g
th
e
p
r
o
p
o
s
ed
f
r
a
m
ewo
r
k
u
n
d
e
r
r
ea
lis
tic
o
p
er
atin
g
c
o
n
d
itio
n
s
.
4
.
2
.
P
er
f
o
r
m
a
nce
ev
a
lua
t
io
n
T
h
e
p
e
r
f
o
r
m
a
n
c
e
o
f
t
h
e
p
r
o
p
o
s
e
d
f
r
a
m
e
w
o
r
k
w
a
s
e
v
a
l
u
a
t
e
d
b
y
c
o
m
p
a
r
i
n
g
f
o
u
r
i
n
d
i
v
i
d
u
a
l
m
o
d
e
l
s
—
L
i
g
h
t
G
B
M
,
i
s
o
l
a
t
i
o
n
f
o
r
e
s
t
,
a
u
t
o
e
n
c
o
d
e
r
,
a
n
d
L
S
T
M
—
w
i
t
h
t
h
e
p
r
o
p
o
s
e
d
h
y
b
r
i
d
i
n
t
r
u
s
i
o
n
d
e
t
e
c
t
i
o
n
f
r
a
m
e
w
o
r
k
.
A
p
p
r
o
x
i
m
a
t
e
l
y
7
0
%
o
f
t
h
e
d
a
t
a
s
e
t
w
a
s
u
s
e
d
f
o
r
t
r
a
i
n
i
n
g
,
w
h
i
l
e
t
h
e
r
e
m
a
i
n
i
n
g
3
0
%
w
a
s
r
e
s
e
r
v
e
d
f
o
r
t
e
s
t
i
n
g
.
A
l
l
m
o
d
e
l
s
w
e
r
e
e
v
a
l
u
a
t
e
d
u
s
i
n
g
a
b
i
n
a
r
y
c
l
a
s
s
i
f
i
c
a
t
i
o
n
t
a
s
k
t
h
a
t
d
i
s
t
i
n
g
u
i
s
h
e
s
n
o
r
m
a
l
t
r
a
f
f
i
c
f
r
o
m
a
n
o
m
a
l
o
u
s
t
r
a
f
f
i
c
.
T
o
p
r
o
v
id
e
a
co
m
p
r
eh
en
s
iv
e
ass
es
s
m
en
t,
p
r
ec
is
io
n
,
r
ec
all,
F1
-
s
co
r
e,
an
d
a
r
ea
u
n
d
er
th
e
R
OC
cu
r
v
e
wer
e
em
p
lo
y
ed
.
T
h
ese
m
etr
ic
s
ev
alu
ate
n
o
t
o
n
ly
o
v
er
all
cl
ass
if
icatio
n
p
er
f
o
r
m
an
ce
b
u
t
also
th
e
ab
ilit
y
to
m
in
im
ize
f
alse
p
o
s
itiv
es
an
d
f
alse
n
eg
ativ
es,
b
o
th
o
f
wh
ich
ar
e
cr
itical
in
I
I
o
T
e
n
v
ir
o
n
m
en
ts
[
2
3
]
,
[
2
4
]
.
Par
ticu
lar
em
p
h
asis
was
p
lac
ed
o
n
r
ec
all
b
ec
au
s
e
m
is
s
ed
attac
k
s
m
ay
lead
to
s
ig
n
if
ican
t
o
p
er
ati
o
n
al
an
d
s
ec
u
r
ity
co
n
s
eq
u
e
n
ce
s
.
T
ab
le
2
s
u
m
m
ar
izes th
e
p
e
r
f
o
r
m
an
c
e
o
f
all
ev
alu
ate
d
m
o
d
els
.
T
ab
le
2
.
Per
f
o
r
m
an
ce
co
m
p
a
r
is
o
n
o
f
in
d
iv
id
u
al
m
o
d
els an
d
th
e
p
r
o
p
o
s
ed
h
y
b
r
i
d
d
etec
tio
n
f
r
am
ewo
r
k
M
o
d
e
l
P
r
e
c
i
s
i
o
n
(
a
n
o
ma
l
y
)
R
e
c
a
l
l
(
a
n
o
ma
l
y
)
F1
-
sc
o
r
e
(
a
n
o
ma
l
y
)
AUC
Li
g
h
t
G
B
M
0
.
9
9
5
0
.
9
4
2
0
.
9
6
8
0
.
9
8
8
I
so
l
a
t
i
o
n
f
o
r
e
st
0
.
8
7
9
0
.
5
0
0
0
.
6
4
0
0
.
1
2
9
A
u
t
o
e
n
c
o
d
e
r
0
.
9
9
9
0
.
9
3
0
0
.
9
6
4
0
.
7
8
4
LSTM
0
.
9
5
0
0
.
9
2
0
0
.
9
3
5
0
.
5
0
0
H
y
b
r
i
d
mo
d
e
l
(
p
r
o
p
o
s
e
d
)
0
.
9
9
9
0
.
9
7
0
0
.
9
8
5
0
.
7
8
7
T
h
e
r
esu
lts
s
h
o
w
th
at
L
ig
h
t
GB
M
ac
h
iev
es
th
e
h
ig
h
est
AUC
(
0
.
9
8
8
)
,
in
d
icatin
g
s
tr
o
n
g
r
an
k
in
g
p
er
f
o
r
m
an
ce
wh
en
d
is
tin
g
u
is
h
in
g
b
etwe
en
n
o
r
m
al
a
n
d
an
o
m
alo
u
s
tr
af
f
ic.
Ho
we
v
er
,
th
e
p
r
o
p
o
s
ed
h
y
b
r
i
d
f
r
am
ewo
r
k
ac
h
iev
es
th
e
h
ig
h
est
r
ec
all
(
0
.
9
7
0
)
a
n
d
F1
-
s
co
r
e
(
0
.
9
8
5
)
,
d
em
o
n
s
tr
atin
g
s
u
p
er
io
r
ca
p
a
b
ilit
y
in
id
en
tify
in
g
a
n
o
m
alo
u
s
tr
af
f
ic
wh
ile
m
in
im
izin
g
m
is
s
ed
attac
k
s
.
I
s
o
latio
n
f
o
r
est
ex
h
ib
its
th
e
wea
k
est
o
v
er
all
p
er
f
o
r
m
a
n
ce
,
p
ar
ticu
lar
ly
in
ter
m
s
o
f
r
ec
all
an
d
AUC,
wh
er
ea
s
au
to
en
co
d
er
an
d
L
STM
p
r
o
v
id
e
co
m
p
lem
en
tar
y
d
etec
tio
n
ca
p
ab
ilit
ies
th
r
o
u
g
h
r
e
co
n
s
tr
u
ctio
n
-
b
ased
lear
n
in
g
an
d
tem
p
o
r
al
p
atter
n
an
aly
s
is
.
B
y
co
m
b
i
n
in
g
th
ese
h
eter
o
g
e
n
eo
u
s
d
etec
tio
n
p
ar
ad
ig
m
s
t
h
r
o
u
g
h
weig
h
ted
f
u
s
io
n
,
th
e
p
r
o
p
o
s
ed
f
r
am
ewo
r
k
ac
h
iev
es a
m
o
r
e
b
alan
ce
d
p
er
f
o
r
m
a
n
ce
th
an
an
y
in
d
iv
id
u
al
m
o
d
el.
Alth
o
u
g
h
th
e
h
y
b
r
id
m
o
d
el
p
r
o
d
u
c
es
a
lo
wer
AUC
th
an
L
ig
h
tGB
M,
th
is
r
esu
lt
r
e
f
lects
th
e
d
esig
n
o
b
jectiv
e
o
f
th
e
p
r
o
p
o
s
ed
f
r
am
ewo
r
k
.
T
h
e
weig
h
ted
f
u
s
io
n
s
tr
ateg
y
p
r
io
r
itizes
r
ec
all
an
d
f
alse
-
n
eg
ativ
e
r
ed
u
ctio
n
r
ath
er
th
an
r
an
k
in
g
p
er
f
o
r
m
an
ce
alo
n
e.
I
n
h
i
g
h
ly
im
b
alan
ce
d
I
I
o
T
e
n
v
ir
o
n
m
en
t
s
,
r
ed
u
cin
g
m
is
s
ed
attac
k
s
is
o
f
ten
m
o
r
e
im
p
o
r
t
an
t
th
an
m
ar
g
in
al
im
p
r
o
v
em
en
ts
in
AUC
b
ec
au
s
e
u
n
d
etec
ted
in
tr
u
s
io
n
s
m
ay
ca
u
s
e
s
ev
er
e
o
p
er
atio
n
al
an
d
s
ec
u
r
ity
im
p
ac
ts
.
Ov
er
all,
th
e
p
r
o
p
o
s
ed
f
r
a
m
ewo
r
k
ac
h
iev
es
a
f
av
o
r
ab
le
b
ala
n
ce
b
etwe
en
d
etec
tio
n
s
en
s
itiv
ity
an
d
class
if
icatio
n
r
eliab
ilit
y
,
m
ak
in
g
it
s
u
itab
le
f
o
r
p
r
ac
tica
l
an
o
m
aly
d
etec
tio
n
in
in
d
u
s
tr
ial
I
o
T
en
v
ir
o
n
m
en
ts
[
2
5
]
.
4
.
3
.
H
y
brid m
o
del per
f
o
r
m
a
nce
T
h
e
ef
f
ec
tiv
en
ess
o
f
th
e
p
r
o
p
o
s
ed
h
y
b
r
id
f
r
a
m
ewo
r
k
is
f
u
r
th
er
illu
s
tr
ated
th
r
o
u
g
h
t
h
e
co
n
f
u
s
io
n
m
atr
ix
an
d
R
OC
cu
r
v
e
an
aly
s
is
s
h
o
wn
in
Fig
u
r
es
5
an
d
6
.
Fig
u
r
e
5
p
r
esen
ts
th
e
co
n
f
u
s
io
n
m
atr
ix
o
f
th
e
p
r
o
p
o
s
ed
h
y
b
r
id
I
DS.
T
h
e
m
o
d
el
co
r
r
ec
tly
id
e
n
tifie
s
6
5
9
,
7
5
3
an
o
m
alo
u
s
in
s
ta
n
ce
s
wh
ile
m
is
class
if
y
in
g
o
n
ly
th
r
ee
an
o
m
alies
as
n
o
r
m
al
t
r
af
f
ic.
I
n
a
d
d
itio
n
,
o
n
ly
3
6
n
o
r
m
al
in
s
tan
ce
s
ar
e
in
co
r
r
ec
tly
class
if
ied
as
an
o
m
alies.
T
h
ese
r
esu
lts
in
d
i
ca
te
a
v
er
y
lo
w
f
alse
-
n
e
g
ativ
e
r
ate
an
d
d
em
o
n
s
tr
ate
th
e
f
r
a
m
ewo
r
k
’
s
ab
ilit
y
t
o
d
etec
t m
alicio
u
s
ac
tiv
ities
r
eliab
ly
wh
ile
m
ain
tain
in
g
a
lo
w
f
alse
-
alar
m
r
ate.
Fig
u
r
e
6
co
m
p
ar
es
th
e
R
OC
cu
r
v
es
o
f
all
ev
alu
ated
m
o
d
el
s
.
L
ig
h
tGB
M
ac
h
iev
es
th
e
h
i
g
h
est
AUC
(
0
.
9
8
8
)
,
r
e
f
lectin
g
s
tr
o
n
g
r
an
k
in
g
ca
p
a
b
ilit
y
.
T
h
e
p
r
o
p
o
s
e
d
h
y
b
r
id
f
r
am
ew
o
r
k
ac
h
iev
es
an
AUC
o
f
0
.
7
8
7
,
co
m
p
ar
ab
le
to
th
e
au
to
e
n
co
d
er
(
0
.
7
8
4
)
,
wh
ile
p
r
o
v
id
in
g
im
p
r
o
v
ed
r
ec
all
an
d
f
alse
-
n
eg
ativ
e
r
ed
u
ctio
n
.
I
n
co
n
tr
ast,
is
o
latio
n
f
o
r
est
an
d
L
STM
ex
h
i
b
it
s
u
b
s
tan
tially
lo
wer
AUC
v
alu
es,
in
d
icatin
g
lim
ited
d
is
cr
im
in
ativ
e
ca
p
ab
ilit
y
u
n
d
e
r
th
e
h
ig
h
ly
im
b
alan
c
e
d
co
n
d
it
io
n
s
o
f
th
e
e
v
alu
ated
d
atasets
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
5
2
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
43
,
No
.
1
,
J
u
ly
20
26
:
345
-
35
4
352
Fig
u
r
e
5
.
C
o
n
f
u
s
io
n
m
atr
i
x
o
f
th
e
p
r
o
p
o
s
ed
h
y
b
r
id
m
o
d
el
Fig
u
r
e
6
.
R
OC
cu
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5.
CO
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Evaluation Warning : The document was created with Spire.PDF for Python.
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DATA AV
AI
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AB
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p
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s
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ab
le
r
eq
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est.
RE
F
E
R
E
NC
E
S
[
1
]
L.
D
.
X
u
,
E.
L.
X
u
,
a
n
d
L
.
L
i
,
“
I
n
d
u
st
r
y
4
.
0
:
st
a
t
e
o
f
t
h
e
a
r
t
a
n
d
f
u
t
u
r
e
t
r
e
n
d
s
,
”
I
n
t
e
r
n
a
t
i
o
n
a
l
J
o
u
r
n
a
l
o
f
Pr
o
d
u
c
t
i
o
n
Re
s
e
a
rc
h
,
v
o
l
.
5
6
,
n
o
.
8
,
p
p
.
2
9
4
1
–
2
9
6
2
,
2
0
1
8
,
d
o
i
:
1
0
.
1
0
8
0
/
0
0
2
0
7
5
4
3
.
2
0
1
8
.
1
4
4
4
8
0
6
.
[
2
]
B
.
A
l
o
t
a
i
b
i
,
“
A
s
u
r
v
e
y
o
n
i
n
d
u
s
t
r
i
a
l
i
n
t
e
r
n
e
t
o
f
t
h
i
n
g
s
s
e
c
u
r
i
t
y
:
r
e
q
u
i
r
e
me
n
t
s
,
a
t
t
a
c
k
s
,
A
I
-
b
a
se
d
s
o
l
u
t
i
o
n
s,
a
n
d
e
d
g
e
c
o
m
p
u
t
i
n
g
o
p
p
o
r
t
u
n
i
t
i
e
s,
”
S
e
n
s
o
rs
,
v
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l
.
2
3
,
n
o
.
1
7
,
A
r
t
.
n
o
.
7
4
7
0
,
2
0
2
3
,
d
o
i
:
1
0
.
3
3
9
0
/
s
2
3
1
7
7
4
7
0
.
[
3
]
M
.
N
u
a
i
m
i
,
L.
C
.
F
o
u
r
a
t
i
,
a
n
d
B
.
B
.
H
a
med
,
“
I
n
t
e
l
l
i
g
e
n
t
a
p
p
r
o
a
c
h
e
s
t
o
w
a
r
d
i
n
t
r
u
s
i
o
n
d
e
t
e
c
t
i
o
n
s
y
st
e
ms
f
o
r
i
n
d
u
st
r
i
a
l
i
n
t
e
r
n
e
t
o
f
t
h
i
n
g
s:
a
s
y
s
t
e
ma
t
i
c
c
o
m
p
r
e
h
e
n
si
v
e
r
e
v
i
e
w
,
”
J
o
u
rn
a
l
o
f
N
e
t
w
o
rk
a
n
d
C
o
m
p
u
t
e
r
Ap
p
l
i
c
a
t
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o
n
s
,
v
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l
.
2
1
5
,
A
r
t
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o
.
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0
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6
3
7
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3
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1
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6
/
j
.
j
n
c
a
.
2
0
2
3
.
1
0
3
6
3
7
.
[
4
]
A
.
A
l
d
h
a
h
e
r
i
,
F
.
A
l
w
a
h
e
d
i
,
M
.
A
.
F
e
r
r
a
g
,
a
n
d
A
.
B
a
t
t
a
h
,
“
D
e
e
p
l
e
a
r
n
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n
g
f
o
r
c
y
b
e
r
t
h
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a
t
d
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t
e
c
t
i
o
n
i
n
I
o
T
n
e
t
w
o
r
k
s
:
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r
e
v
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e
w
,
”
I
n
t
e
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n
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t
o
f
T
h
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n
g
s
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n
d
C
y
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e
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:
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m
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.
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