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[
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Evaluation Warning : The document was created with Spire.PDF for Python.
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Vo
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15
,
No
.
1
,
Ma
r
ch
20
26
:
25
7
-
26
6
258
en
h
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b
o
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p
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u
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[
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4
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[
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[
6
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[
7
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.
B
y
m
in
im
izin
g
th
e
am
o
u
n
t
o
f
d
ata
s
en
t
o
v
e
r
th
e
n
e
two
r
k
,
th
e
Fo
g
-
to
-
T
h
i
n
g
s
ap
p
r
o
ac
h
h
elp
s
lo
wer
laten
cy
an
d
co
n
s
er
v
es
n
etwo
r
k
r
eso
u
r
ce
s
.
I
n
lar
g
e
-
s
ca
le,
c
en
tr
alize
d
ex
t
r
em
e
en
v
ir
o
n
m
en
ts
,
it
is
n
ec
es
s
ar
y
to
s
to
r
e
d
ata
at
a
ce
n
tr
al
s
i
te
f
o
r
m
o
n
ito
r
i
n
g
an
d
f
u
tu
r
e
u
s
e
[
6
]
.
Fo
g
co
m
p
u
tin
g
ca
n
also
p
lay
a
r
o
le
b
y
s
to
r
in
g
d
ata
at
an
ap
p
r
o
p
r
iate
lo
ca
l
s
ite
th
at
is
ac
ce
s
s
ib
le
to
all
p
ar
ticip
atin
g
n
o
d
es.
I
n
th
e
ch
allen
g
i
n
g
c
o
n
d
itio
n
s
o
f
s
m
ar
t
ag
r
icu
ltu
r
e
,
ed
g
e
c
o
m
p
u
tin
g
ca
n
en
h
a
n
ce
c
o
m
m
u
n
icatio
n
ef
f
icien
cy
,
lo
ad
d
is
tr
ib
u
tio
n
,
an
d
n
etwo
r
k
r
elia
b
ilit
y
,
m
ar
k
in
g
a
s
ig
n
if
ican
t st
ep
to
war
d
m
o
r
e
d
ep
en
d
ab
le
o
p
er
atio
n
s
[
8
]
,
[
9
]
.
El
-
Gh
am
r
y
et
a
l.
[
1
0
]
in
tr
o
d
u
ce
s
a
co
n
v
o
lu
tio
n
al
n
eu
r
al
n
e
two
r
k
(
C
NN
)
b
ased
i
n
tr
u
s
io
n
d
etec
tio
n
s
y
s
tem
(
I
DS)
f
o
r
s
m
ar
t
f
a
r
m
i
n
g
,
lev
er
a
g
in
g
d
ee
p
lear
n
in
g
tech
n
iq
u
es
to
s
ec
u
r
e
a
g
r
icu
lt
u
r
al
I
o
T
n
etwo
r
k
s
.
E
v
alu
ated
u
s
in
g
th
e
NSL
-
KD
D
d
ataset,
th
e
s
y
s
tem
em
p
h
a
s
izes
d
ata
p
r
e
-
p
r
o
ce
s
s
in
g
,
f
ea
tu
r
e
s
elec
tio
n
,
an
d
h
y
p
er
p
ar
am
eter
o
p
tim
izatio
n
to
ac
h
iev
e
o
v
er
9
9
%
d
etec
tio
n
ac
cu
r
ac
y
,
p
r
ec
is
io
n
,
an
d
F1
-
s
co
r
es.
W
h
ile
it
s
h
o
wca
s
es
th
e
im
p
o
r
tan
ce
o
f
m
ac
h
in
e
lear
n
in
g
(
ML
)
f
o
r
s
ec
u
r
in
g
s
m
a
r
t
f
ar
m
in
g
,
class
im
b
alan
ce
in
th
e
d
ataset
m
ay
im
p
ac
t
d
etec
tio
n
p
e
r
f
o
r
m
an
ce
f
o
r
r
ar
e
r
atta
ck
ty
p
es.
Alan
az
i
a
n
d
Alr
ash
d
i
[
1
1
]
,
a
s
m
ar
t
ag
r
icu
ltu
r
e
s
y
s
tem
in
teg
r
atin
g
d
ee
p
lear
n
i
n
g
m
eth
o
d
s
lik
e
C
NN
an
d
lo
n
g
s
h
o
r
t
-
ter
m
m
em
o
r
y
(
L
STM
)
is
d
ev
elo
p
e
d
to
d
etec
t
an
o
m
alies
in
r
ea
l
-
tim
e.
Desig
n
ed
to
o
p
er
ate
at
th
e
n
etwo
r
k
ed
g
e,
it
r
ed
u
ce
s
laten
cy
an
d
en
ab
les
tim
ely
in
ter
v
en
tio
n
s
f
o
r
cr
o
p
h
ea
lth
an
d
r
eso
u
r
ce
m
an
ag
em
e
n
t.
T
h
e
s
tu
d
y
h
ig
h
lig
h
ts
d
is
tr
ib
u
ted
d
en
ial
o
f
s
er
v
ice
(
DDo
S
)
attac
k
p
r
ev
en
tio
n
,
wh
ich
co
u
ld
d
is
r
u
p
t
ag
r
icu
ltu
r
al
o
p
e
r
atio
n
s
,
b
u
t
it
ac
k
n
o
wled
g
es
th
at
o
th
er
c
y
b
er
s
ec
u
r
ity
th
r
ea
ts
ex
is
t,
th
o
u
g
h
t
h
ey
ar
e
n
o
t
d
ee
p
ly
e
x
p
lo
r
e
d
.
Ald
h
y
an
i
an
d
Alk
ah
tan
i
[
1
2
]
d
is
cu
s
s
es
u
s
in
g
d
ee
p
lear
n
in
g
,
p
ar
ticu
lar
ly
C
NN
an
d
L
STM
,
f
o
r
c
y
b
er
t
h
r
ea
t
d
etec
tio
n
in
Ag
r
icu
ltu
r
e
4
.
0
.
I
t
s
tr
ess
es
th
e
n
ee
d
t
o
p
r
o
tec
t
I
o
T
n
etwo
r
k
s
f
r
o
m
DDo
S
attac
k
s
.
T
h
e
m
o
d
els
aim
to
e
n
h
an
ce
ag
r
icu
ltu
r
al
o
u
tp
u
t
q
u
ality
an
d
q
u
an
tity
th
r
o
u
g
h
AI
a
n
d
clo
u
d
co
m
p
u
tin
g
,
wh
ile
ad
d
r
ess
in
g
cy
b
e
r
s
ec
u
r
i
ty
r
is
k
s
.
Ho
wev
er
,
ch
allen
g
es
lik
e
f
alse
alar
m
s
o
r
m
is
s
ed
d
etec
tio
n
s
co
u
ld
im
p
ac
t
th
e
s
y
s
tem
’
s
ef
f
icien
cy
an
d
s
ec
u
r
ity
.
Z
way
ed
et
a
l.
[
1
3
]
p
r
esen
ts
a
h
y
b
r
id
f
ea
tu
r
e
s
elec
tio
n
a
p
p
r
o
ac
h
with
B
iLST
M
f
o
r
in
tr
u
s
io
n
d
etec
tio
n
in
f
o
g
co
m
p
u
tin
g
en
v
ir
o
n
m
en
ts
,
h
a
n
d
lin
g
th
e
co
m
p
lex
ities
o
f
h
ig
h
-
d
im
en
s
io
n
al
I
o
T
d
ata.
W
ith
ac
cu
r
ac
y
r
a
tes
o
f
9
8
.
4
2
%
o
n
th
e
T
ON_
I
o
T
d
at
aset
an
d
9
8
.
7
%
o
n
th
e
B
o
T
-
I
o
T
d
ataset,
th
is
m
eth
o
d
im
p
r
o
v
es
b
o
th
ef
f
icien
cy
an
d
ac
cu
r
ac
y
,
s
h
o
wca
s
in
g
d
ee
p
lear
n
in
g
’
s
r
o
le
in
s
ec
u
r
in
g
I
o
T
n
etwo
r
k
s
.
Dash
et
a
l.
[
1
4
]
in
tr
o
d
u
ce
s
a
d
ee
p
lear
n
in
g
f
r
a
m
ewo
r
k
f
o
r
a
n
o
m
aly
d
etec
tio
n
in
I
o
T
n
etwo
r
k
s
u
s
in
g
B
iLST
M
an
d
g
ated
r
ec
u
r
r
en
t
u
n
it
(
GR
U
)
,
o
p
tim
ized
b
y
th
e
J
AYA
alg
o
r
ith
m
.
T
h
e
m
o
d
els,
J
AYA
-
B
iLS
T
MI
DS a
n
d
J
AYA
-
G
R
UI
DS,
ac
h
iev
ed
ac
cu
r
ac
y
r
ates
o
f
9
9
.
6
5
%
an
d
9
9
.
4
2
%
,
r
esp
ec
tiv
ely
,
with
m
in
im
al
f
alse
alar
m
s
.
A
f
o
g
co
m
p
u
t
in
g
f
r
a
m
ewo
r
k
f
o
r
U
n
m
an
n
ed
ae
r
ial
v
eh
icle
(
U
AV
)
ass
i
s
ted
s
m
ar
t
f
ar
m
in
g
,
as
d
is
cu
s
s
ed
in
[
1
5
]
,
ad
d
r
e
s
s
es
en
er
g
y
-
r
elate
d
attac
k
s
,
f
o
cu
s
in
g
o
n
DDo
S
an
d
u
n
a
u
th
o
r
ize
d
ac
ce
s
s
.
B
y
u
s
in
g
ML
f
o
r
in
t
r
u
s
io
n
d
etec
tio
n
,
th
e
s
y
s
tem
aim
s
to
s
ec
u
r
e
UAV
o
p
er
atio
n
s
,
en
h
a
n
cin
g
d
ata
r
eliab
ilit
y
an
d
a
g
r
i
cu
ltu
r
al
p
r
o
d
u
ctiv
ity
.
Fin
ally
,
L
awa
ll
et
a
l.
[
1
6
]
,
a
f
r
am
ewo
r
k
f
o
r
m
itig
atin
g
DDo
S
attac
k
s
in
I
o
T
n
etwo
r
k
s
v
ia
f
o
g
co
m
p
u
tin
g
c
o
m
b
i
n
es
s
ig
n
atu
r
e
-
an
d
an
o
m
aly
-
b
ased
d
etec
tio
n
.
ML
en
ab
les
r
ap
id
attac
k
d
etec
tio
n
,
im
p
r
o
v
i
n
g
r
eso
u
r
ce
ef
f
icien
c
y
an
d
s
ec
u
r
ity
.
T
h
e
m
eth
o
d
o
l
o
g
y
in
cl
u
d
es
co
m
p
ar
in
g
th
e
k
-
NN
class
if
ier
’
s
p
er
f
o
r
m
an
ce
to
o
t
h
er
m
o
d
els,
d
em
o
n
s
tr
atin
g
en
h
an
ce
d
ac
cu
r
ac
y
in
n
etwo
r
k
tr
af
f
ic
a
n
o
m
aly
d
etec
tio
n
.
T
h
e
s
tu
d
ies
h
ig
h
lig
h
t
d
ee
p
le
ar
n
in
g
’
s
p
o
ten
tial
in
I
o
T
s
ec
u
r
ity
,
u
r
g
in
g
s
o
lu
tio
n
s
f
o
r
class
im
b
alan
ce
,
r
ea
l
-
tim
e
s
ca
lab
ilit
y
,
an
d
ev
o
lv
i
n
g
th
r
ea
ts
.
T
h
e
in
teg
r
atio
n
o
f
d
iv
e
r
s
e
s
en
s
o
r
s
in
s
m
ar
t
f
ar
m
in
g
co
m
m
u
n
icatio
n
b
r
i
n
g
s
f
o
r
th
n
u
m
er
o
u
s
s
ec
u
r
ity
ch
allen
g
es
.
T
h
is
is
p
ar
ticu
lar
ly
s
ig
n
if
ican
t
in
ex
p
an
s
iv
e
n
etwo
r
k
s
,
wh
er
e
th
e
p
r
esen
ce
o
f
h
eter
o
g
en
e
o
u
s
s
en
s
o
r
s
ca
n
p
o
ten
tially
co
m
p
r
o
m
is
e
th
e
s
y
s
tem
’
s
in
teg
r
ity
.
I
n
a
Fo
g
-
to
-
T
h
i
n
g
s
ar
ch
itectu
r
e,
estab
lis
h
in
g
a
r
o
b
u
s
t
co
m
m
u
n
icatio
n
f
r
am
e
wo
r
k
is
cr
itical
to
f
ac
ilit
at
in
g
s
ea
m
less
in
ter
ac
tio
n
s
[
1
7
]
.
Ma
licio
u
s
ac
to
r
s
with
in
th
e
n
etwo
r
k
m
a
y
d
is
r
u
p
t
th
e
co
m
m
u
n
icatio
n
in
f
r
astru
ctu
r
e,
lead
in
g
to
er
r
atic
an
d
u
n
p
r
ed
ictab
le
in
ter
ac
tio
n
s
[
1
8
]
.
T
h
ese
co
m
p
lex
s
ce
n
ar
io
s
n
ec
ess
i
tate
ef
f
ec
tiv
e
s
ec
u
r
it
y
m
ea
s
u
r
es
to
ad
d
r
ess
th
e
ev
o
lv
in
g
ch
allen
g
es.
An
ef
f
ec
tiv
e
I
DS
ca
n
r
ed
u
ce
th
e
lik
elih
o
o
d
o
f
attac
k
s
b
y
id
en
tify
in
g
m
alicio
u
s
en
titi
es
with
in
th
e
n
etwo
r
k
p
r
o
m
p
tly
[
1
9
]
.
I
n
r
ec
e
n
t
y
ea
r
s
,
d
ee
p
lear
n
in
g
(
DL
)
-
b
ased
I
D
S
h
av
e
b
ec
o
m
e
in
cr
ea
s
in
g
ly
p
o
p
u
lar
d
u
e
to
th
eir
r
ap
id
an
o
m
aly
d
etec
tio
n
ca
p
ab
ilit
ies
[
2
0
]
.
Ad
d
itio
n
ally
,
DL
-
b
ased
I
DS
y
ield
m
o
r
e
p
r
ec
is
e
o
u
tco
m
es
co
m
p
ar
ed
to
tr
ad
itio
n
al
ML
m
eth
o
d
s
[
2
1
]
.
I
n
th
ese
s
y
s
te
m
s
,
th
e
m
o
d
el
is
in
itial
ly
tr
ain
ed
o
n
an
ex
ten
s
iv
e
d
ataset
th
at
r
ef
lects
p
o
ten
tia
l
attac
k
s
with
in
th
e
s
p
ec
if
ic
ap
p
licatio
n
ar
ea
.
S
u
b
s
eq
u
e
n
tly
,
th
e
s
y
s
tem
is
im
p
lem
en
ted
in
a
r
ea
l
-
tim
e
s
m
ar
t
f
ar
m
in
g
en
v
ir
o
n
m
en
t,
w
h
er
e
it
d
etec
ts
s
im
ilar
at
tack
p
atter
n
s
.
W
h
ile
DL
-
b
ased
I
DS
o
f
f
er
s
r
o
b
u
s
t
m
o
n
i
to
r
in
g
o
f
p
o
ten
tial
th
r
ea
ts
,
d
ev
elo
p
in
g
an
ap
p
r
o
p
r
iate
I
DS
r
em
ain
s
a
co
m
p
lex
ch
allen
g
e
[
2
2
]
,
[
2
3
]
.
B
ef
o
r
e
d
ev
elo
p
in
g
a
DL
-
b
ased
I
DS,
v
ar
io
u
s
f
ac
to
r
s
,
i
n
clu
d
i
n
g
r
eso
u
r
ce
u
s
ag
e,
co
m
p
atib
ilit
y
,
s
ec
u
r
ity
r
eq
u
ir
e
m
en
ts
,
s
y
s
tem
f
lex
ib
ilit
y
,
laten
cy
an
d
co
s
t,
m
u
s
t b
e
c
o
n
s
id
er
ed
[
2
4
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
I
SS
N:
2252
-
8
7
7
6
E
n
h
a
n
ce
d
s
ma
r
t fa
r
min
g
s
ec
u
r
ity
w
ith
cla
s
s
-
a
w
a
r
e
in
tr
u
s
io
n
d
etec
tio
n
in
fo
g
…
(
S
elva
r
a
j
P
a
la
n
is
a
my
)
259
T
h
is
p
ap
er
p
r
esen
ts
an
ef
f
e
ctiv
e
DL
-
d
r
iv
e
n
I
DS
d
esig
n
ed
f
o
r
f
o
g
-
ass
is
ted
s
m
ar
t
f
a
r
m
in
g
i
n
ch
allen
g
in
g
I
o
T
en
v
ir
o
n
m
en
t
s
.
T
h
is
I
DS
u
tili
ze
s
a
h
y
b
r
id
ap
p
r
o
ac
h
,
in
co
r
p
o
r
atin
g
an
au
to
en
co
d
er
n
e
u
r
al
n
etwo
r
k
f
o
r
a
n
o
m
aly
d
etec
tio
n
an
d
i
n
itial
b
in
ar
y
class
if
icat
io
n
.
T
h
e
en
co
d
ed
f
ea
t
u
r
es
in
t
h
e
laten
t
s
p
ac
e
ar
e
f
u
r
th
er
an
aly
ze
d
u
s
in
g
a
So
f
t
Ma
x
class
if
ier
to
ac
h
iev
e
m
u
l
ti
-
class
attac
k
class
if
icatio
n
,
wh
ich
is
cr
u
cial
f
o
r
im
p
r
o
v
e
d
p
r
ev
en
tio
n
an
d
d
et
ec
tin
g
th
r
ea
ts
at
th
e
n
etwo
r
k
ed
g
e.
T
h
e
p
r
o
p
o
s
ed
m
o
d
el
e
f
f
ec
tiv
ely
d
etec
ts
a
wid
e
r
an
g
e
o
f
attac
k
ty
p
es
in
s
m
ar
t
f
ar
m
in
g
,
in
cl
u
d
in
g
b
ac
k
d
o
o
r
,
DDo
S
,
in
jectio
n
,
p
ass
wo
r
d
attac
k
s
,
r
an
s
o
m
war
e,
s
ca
n
n
in
g
,
XSS,
an
d
o
th
er
s
.
T
h
is
ap
p
r
o
ac
h
u
ti
lizes
a
clas
s
-
awa
r
e
au
to
en
co
d
er
th
at
co
m
b
in
es
a
r
ec
o
n
s
tr
u
ctio
n
o
b
jectiv
e
with
an
in
teg
r
ated
class
if
icatio
n
lay
er
.
Du
r
in
g
tr
ain
in
g
,
th
e
m
o
d
el
o
p
tim
izes
b
o
th
r
ec
o
n
s
tr
u
ctio
n
a
n
d
class
if
icatio
n
er
r
o
r
s
,
allo
win
g
it
to
lear
n
th
e
s
tr
u
ctu
r
e
o
f
ea
ch
class
wh
ile
ca
r
r
y
in
g
o
u
t
d
ir
ec
t
class
if
icatio
n
.
T
o
ad
d
r
ess
th
e
ch
allen
g
es
o
f
clo
u
d
-
b
ased
d
ep
lo
y
m
en
t
in
ex
tr
em
e
en
v
ir
o
n
m
en
ts
,
we
p
r
o
p
o
s
e
a
Fo
g
-
to
-
T
h
in
g
s
d
ep
lo
y
m
en
t
ar
ch
itectu
r
e
f
o
r
th
e
I
DS.
E
v
alu
atio
n
s
o
n
th
e
T
O
N
_
I
o
T
[
2
5
]
d
atasets
d
em
o
n
s
tr
ate
th
e
m
o
d
el
’
s
s
tr
o
n
g
p
er
f
o
r
m
a
n
ce
ac
r
o
s
s
s
tan
d
ar
d
ev
alu
atio
n
m
etr
ics,
r
ein
f
o
r
cin
g
its
s
u
itab
ilit
y
f
o
r
s
u
ch
en
v
ir
o
n
m
en
ts
.
Fu
r
th
er
m
o
r
e,
to
estab
lis
h
th
e
ef
f
ec
tiv
e
n
ess
o
f
th
e
p
r
o
p
o
s
ed
I
DS,
we
co
m
p
ar
e
it
ag
ain
s
t
b
aselin
e
m
o
d
els an
d
r
ec
en
t state
-
of
-
th
e
-
a
r
t m
eth
o
d
s
.
T
h
e
s
tr
u
ctu
r
e
o
f
th
is
a
r
ticle
is
as
f
o
llo
ws:
s
ec
tio
n
2
o
u
tlin
es
th
e
p
r
o
p
o
s
ed
DL
-
b
ased
attac
k
d
etec
tio
n
f
r
am
ewo
r
k
.
I
n
s
ec
tio
n
3
p
r
esen
ts
th
e
ev
alu
atio
n
o
f
th
e
p
r
o
p
o
s
ed
I
DS
an
d
co
m
p
ar
es
its
p
er
f
o
r
m
a
n
ce
with
s
tate
-
of
-
th
e
-
ar
t
m
eth
o
d
s
.
L
as
tly
,
s
ec
tio
n
4
co
n
clu
d
es
th
e
p
ap
er
a
n
d
d
is
cu
s
s
es
p
o
ten
t
ial
f
u
tu
r
e
r
esear
ch
d
ir
ec
tio
n
s
.
2.
RE
S
E
ARCH
M
E
T
H
O
D
T
h
is
s
ec
tio
n
p
r
o
p
o
s
es
a
class
-
awa
r
e
au
to
en
co
d
e
r
f
r
am
ew
o
r
k
f
o
r
an
o
m
al
y
d
etec
tio
n
a
n
d
attac
k
class
if
icatio
n
in
an
I
o
T
-
en
a
b
led
s
m
ar
t
f
ar
m
in
g
en
v
ir
o
n
m
en
t
,
u
s
in
g
b
o
t
h
b
i
n
ar
y
an
d
m
u
lti
-
cl
ass
ap
p
r
o
ac
h
es
f
o
r
ef
f
ec
tiv
e
attac
k
id
en
tific
atio
n
.
T
h
e
T
ON_
I
o
T
d
ataset
s
er
v
es
as
th
e
d
ata
s
o
u
r
ce
f
o
r
m
o
d
el
i
n
p
u
t.
As
illu
s
tr
ated
in
Fig
u
r
e
1
,
th
e
m
eth
o
d
em
p
l
o
y
s
an
au
to
e
n
co
d
e
r
n
e
u
r
al
n
et
wo
r
k
f
o
r
an
o
m
aly
d
etec
tio
n
,
p
er
f
o
r
m
in
g
an
i
n
itial
b
in
ar
y
class
if
icatio
n
.
T
h
e
en
co
d
ed
r
ep
r
esen
tatio
n
in
th
e
laten
t
s
p
ac
e
is
th
en
p
r
o
ce
s
s
ed
b
y
a
So
f
tMa
x
class
if
ier
,
en
ab
lin
g
m
u
lti
-
class
class
if
i
ca
tio
n
o
f
attac
k
s
—
an
ess
en
tial
s
tep
f
o
r
en
h
an
ce
d
p
r
e
v
en
tio
n
.
T
h
e
f
r
am
ewo
r
k
is
im
p
lem
en
ted
wi
th
in
th
e
s
m
ar
t
f
ar
m
in
g
s
y
s
tem
,
wh
ich
co
n
s
is
ts
o
f
th
r
ee
lay
er
s
:
s
en
s
o
r
,
f
o
g
,
an
d
clo
u
d
.
T
h
is
ap
p
r
o
ac
h
s
p
ec
if
ica
lly
tar
g
ets th
e
in
ter
m
ed
iar
y
f
o
g
lay
er
with
in
th
e
s
m
ar
t f
ar
m
i
n
g
ar
ch
itectu
r
e.
Fig
u
r
e
1
.
Ar
c
h
itectu
r
e
o
f
th
e
p
r
o
p
o
s
ed
class
-
awa
r
e
au
to
e
n
co
d
er
with
m
u
lti
-
class
class
if
ier
f
o
r
s
m
ar
t f
a
r
m
in
g
2
.
1
.
T
O
N_
I
o
T
da
t
a
s
et
T
h
e
T
ON_
I
o
T
d
ataset
s
er
v
es
as
a
r
ec
en
t
test
b
ed
f
o
r
an
I
I
o
T
n
etwo
r
k
,
p
r
o
v
id
i
n
g
th
r
ee
d
is
t
in
ct
ty
p
es
o
f
d
ata:
n
etwo
r
k
d
ata,
o
p
er
ati
n
g
s
y
s
tem
d
ata,
an
d
telem
etr
y
d
ata.
I
n
th
is
s
tu
d
y
,
th
e
telem
etr
y
d
atasets
f
r
o
m
I
o
T
a
n
d
I
I
o
T
s
en
s
o
r
s
,
o
r
g
an
i
ze
d
ac
r
o
s
s
s
ev
en
f
iles
,
ar
e
u
t
i
lized
.
T
h
e
s
ev
e
n
f
iles
in
th
e
telem
etr
y
d
ataset
r
ep
r
esen
t
d
ata
o
b
s
er
v
atio
n
s
f
r
o
m
s
ev
en
s
en
s
o
r
s
ass
o
ciate
d
with
wea
th
er
,
f
r
id
g
e,
g
ar
ag
e
d
o
o
r
,
GPS
tr
ac
k
er
,
Mo
d
b
u
s
,
m
o
tio
n
lig
h
t,
an
d
t
h
er
m
o
s
tat.
T
h
ese
s
en
s
o
r
s
p
r
o
v
id
e
d
ata
p
o
i
n
ts
s
u
ch
as
tem
p
er
atu
r
e,
h
u
m
id
ity
,
p
r
ess
u
r
e,
d
o
o
r
o
p
en
/clo
s
e
s
ta
tu
s
,
latitu
d
e
an
d
l
o
n
g
itu
d
e,
a
n
d
lig
h
t
o
n
/o
f
f
s
tatu
s
.
I
t
in
cl
u
d
es
eig
h
t
class
es:
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
7
7
6
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
,
Vo
l.
15
,
No
.
1
,
Ma
r
ch
20
26
:
25
7
-
26
6
260
s
ev
en
attac
k
ty
p
es
—
b
ac
k
d
o
o
r
,
DDo
S,
in
jectio
n
,
p
ass
wo
r
d
,
r
an
s
o
m
war
e,
s
ca
n
n
in
g
,
an
d
XSS
—
a
s
well
a
s
a
n
o
r
m
al
class
.
I
t h
as 3
,
2
7
0
,
0
2
2
n
o
r
m
al
in
s
tan
ce
s
an
d
5
2
7
,
3
1
1
attac
k
in
s
tan
ce
s
,
to
talin
g
3
,
7
9
7
,
3
3
3
d
ata
p
o
in
ts
.
2
.
2
.
Da
t
a
prepro
ce
s
s
ing
T
h
e
d
ata
p
r
ep
r
o
ce
s
s
in
g
s
tag
e
in
v
o
lv
es
la
b
elin
g
,
attr
i
b
u
te
p
ad
d
in
g
,
an
d
in
teg
r
atin
g
s
ev
en
d
ata
f
iles
in
to
a
s
in
g
le
s
o
u
r
ce
d
ataset.
T
o
cr
ea
te
a
co
m
m
o
n
f
ea
tu
r
e
s
p
ac
e,
att
r
ib
u
te
p
ad
d
in
g
with
a
z
er
o
lab
el
is
ap
p
lied
f
o
r
an
y
m
is
s
in
g
attr
ib
u
tes.
Z
-
s
co
r
e
n
o
r
m
aliza
tio
n
(
Z
-
s
ca
lin
g
)
is
u
s
ed
to
s
tan
d
ar
d
ize
th
e
v
alu
es
b
y
tr
an
s
f
o
r
m
in
g
th
e
d
ata,
as
s
h
o
wn
in
(
1
)
,
s
h
if
tin
g
th
e
m
ea
n
to
0
an
d
s
ca
lin
g
it
to
h
av
e
a
s
tan
d
ar
d
d
ev
iatio
n
o
f
1
.
T
h
is
o
p
tim
ized
d
ataset
(
o
DAT
A)
,
th
en
s
er
v
es a
s
in
p
u
t to
th
e
d
etec
tio
n
m
o
d
el.
=
(
−
)
(
1
)
2
.
3
.
Ano
m
a
ly
det
ec
t
io
n us
in
g
t
he
a
uto
enco
der
T
h
e
au
to
en
co
d
er
,
a
n
eu
r
al
n
etwo
r
k
ar
ch
itectu
r
e
b
ased
o
n
an
u
n
s
u
p
er
v
is
ed
lear
n
i
n
g
ap
p
r
o
ac
h
,
is
u
tili
ze
d
f
o
r
a
n
o
m
aly
d
etec
tio
n
in
th
is
ex
p
e
r
im
en
t.
I
t
ex
tr
ac
ts
h
ier
ar
ch
ical
f
ea
tu
r
es
to
im
p
r
o
v
e
b
in
ar
y
an
o
m
al
y
d
etec
tio
n
[
2
6
]
.
T
h
e
n
o
r
m
alize
d
d
ata
f
r
o
m
th
e
p
r
ep
r
o
ce
s
s
in
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Fig
u
r
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Ad
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Du
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al
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1
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an
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Au
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atin
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=
1
(
1
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(
−
(
)
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(
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′
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(
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Fig
u
r
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2
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T
h
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au
to
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co
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r
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r
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itectu
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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t J I
n
f
&
C
o
m
m
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T
ec
h
n
o
l
I
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N:
2252
-
8
7
7
6
E
n
h
a
n
ce
d
s
ma
r
t fa
r
min
g
s
ec
u
r
ity
w
ith
cla
s
s
-
a
w
a
r
e
in
tr
u
s
io
n
d
etec
tio
n
in
fo
g
…
(
S
elva
r
a
j
P
a
la
n
is
a
my
)
261
2
.
4
.
M
ulti
-
cla
s
s
cla
s
s
if
ica
t
io
n wit
h
S
o
f
t
M
a
x
la
y
er
Af
ter
tr
ain
in
g
th
e
au
to
en
c
o
d
e
r
o
n
n
o
r
m
al
d
ata,
a
s
ec
o
n
d
p
h
ase
o
f
tr
ain
i
n
g
is
c
o
n
d
u
cted
t
o
o
p
tim
iz
e
th
e
in
teg
r
ate
d
So
f
tMa
x
lay
er
with
in
th
e
laten
t
s
p
ac
e.
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r
in
g
th
is
p
h
ase,
b
o
th
n
o
r
m
al
an
d
attac
k
d
ata
a
r
e
in
tr
o
d
u
ce
d
,
e
n
ab
lin
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th
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f
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x
class
if
ier
to
ca
te
g
o
r
ize
s
am
p
les
u
s
in
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laten
t
f
ea
t
u
r
es.
T
h
e
d
im
e
n
s
io
n
ality
r
ed
u
ctio
n
ca
p
ab
ilit
y
o
f
th
e
au
t
o
en
co
d
e
r
en
h
a
n
ce
s
its
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f
ec
tiv
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ess
in
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u
lti
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class
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s
s
if
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.
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h
e
d
ata
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th
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laten
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ar
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lab
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s
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s
.
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s
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s
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e
2
D
laten
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t
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e
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ttlen
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k
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u
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t
t
h
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p
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b
a
b
ilit
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d
is
tr
ib
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s
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o
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m
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o
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r
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DDo
S,
in
j
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,
p
ass
wo
r
d
,
r
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o
m
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s
ca
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n
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o
r
XSS
.
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ap
p
r
o
ac
h
u
tili
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s
a
class
-
awa
r
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au
to
en
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e
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th
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m
b
in
es
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o
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jectiv
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with
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in
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r
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.
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,
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el
o
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tim
izes
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r
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o
n
s
tr
u
ctio
n
an
d
class
if
icatio
n
er
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o
r
s
(
0
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0
2
6
2
8
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,
allo
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th
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tr
u
ctu
r
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wh
ile
ca
r
r
y
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g
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t d
ir
ec
t c
lass
if
icati
o
n
.
T
h
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s
im
u
ltan
eo
u
s
o
p
tim
iz
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en
ab
les th
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ad
ju
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tm
en
t o
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t f
ac
to
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s
f
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r
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r
a
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d
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as sh
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th
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.
=
(
∗
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+
(
∗
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(
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I
n
th
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o
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d
β
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e
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eig
h
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ac
to
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h
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ete
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p
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n
t
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er
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o
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f
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n
cti
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n
.
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h
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allo
ws
th
e
m
o
d
el
to
d
etec
t
an
o
m
alies
(
v
ia
r
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n
s
tr
u
ctio
n
er
r
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r
)
an
d
class
if
y
attac
k
ty
p
es (
u
s
in
g
th
e
So
f
tMa
x
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u
tp
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t)
as p
ar
t
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in
teg
r
ated
,
e
n
d
-
to
-
en
d
s
y
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tem
.
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
e
ev
alu
atio
n
o
f
th
e
p
r
o
p
o
s
e
d
class
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awa
r
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au
to
en
co
d
e
r
f
r
a
m
ewo
r
k
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n
d
u
cted
o
n
b
o
th
b
in
ar
y
a
n
d
m
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th
e
T
ON_
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o
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d
ataset.
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r
b
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ar
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p
e
r
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ce
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ass
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s
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s
in
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a
co
n
f
u
s
io
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m
at
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ix
alo
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g
with
s
tan
d
ar
d
ev
alu
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m
etr
ics.
I
n
th
e
m
u
lti
-
class
class
if
icatio
n
,
th
e
f
r
am
e
wo
r
k
is
test
ed
ac
r
o
s
s
eig
h
t
clas
s
es,
w
ith
p
er
f
o
r
m
an
ce
m
ea
s
u
r
ed
u
s
in
g
s
tan
d
ar
d
m
etr
ics.
Ad
d
itio
n
ally
,
th
e
p
r
o
p
o
s
ed
m
eth
o
d
is
c
o
m
p
ar
e
d
with
est
ab
lis
h
ed
ap
p
r
o
ac
h
es.
T
h
e
d
ata
s
am
p
les
u
s
ed
in
t
h
is
r
esear
ch
ex
p
e
r
im
en
t
ar
e
lis
ted
in
T
ab
le
1
an
d
th
e
h
y
p
er
p
ar
am
ete
r
s
u
s
ed
to
f
in
e
tu
n
e
t
h
e
m
o
d
el
is
s
h
o
wn
i
n
T
ab
le
2
.
T
ab
le
1
.
Ov
e
r
v
iew
o
f
e
x
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er
im
en
tal
d
ata
f
o
r
th
e
p
r
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p
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s
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eth
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C
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t
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scri
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s
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lects
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p
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cc
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ately
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ize
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eg
ativ
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in
s
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s
.
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r
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s
h
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9
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,
r
ep
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esen
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th
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er
all
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ied
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el.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
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I
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2
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ra
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esting
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Fig
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3
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m
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lo
s
s
f
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ata.
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ata.
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h
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So
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x
t
r
ain
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g
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ep
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ile
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ep
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ich
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ef
lectin
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th
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m
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s
o
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g
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i
n
g
lear
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n
g
p
r
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ce
s
s
.
Fig
u
r
e
3
.
L
o
s
s
f
u
n
ctio
n
s
o
f
th
e
p
r
o
p
o
s
ed
m
o
d
el:
au
to
en
c
o
d
er
lo
s
s
,
So
f
tMa
x
lo
s
s
,
an
d
jo
in
t l
o
s
s
3
.
3
.
E
v
a
lua
t
i
o
n o
n t
he
bin
a
ry
cla
s
s
cla
s
s
if
ica
t
io
n m
o
du
le
T
h
e
o
v
e
r
a
l
l
p
e
r
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n
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h
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m
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is
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l
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s
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at
ed
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n
F
i
g
u
r
e
4
,
w
h
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c
h
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t
a
i
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l
ts
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n
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m
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s
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cla
s
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at
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o
n
t
a
s
k
s
.
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h
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a
u
t
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d
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s
h
o
w
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F
i
g
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r
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4
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a
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p
a
r
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c
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l
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r
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ca
l
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m
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it
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m
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p
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t
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s
.
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v
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F
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g
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4
(
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,
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n
0
.
9
7
.
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t
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h
i
g
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e
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al
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n
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ig
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t
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_
I
o
T
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J I
n
f
&
C
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m
m
u
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T
ec
h
n
o
l
I
SS
N:
2252
-
8
7
7
6
E
n
h
a
n
ce
d
s
ma
r
t fa
r
min
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ec
u
r
ity
w
ith
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s
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w
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in
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etec
tio
n
in
fo
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…
(
S
elva
r
a
j
P
a
la
n
is
a
my
)
263
(
a)
(
b
)
Fig
u
r
e
4
.
Per
f
o
r
m
an
c
e
m
etr
ics o
f
(
a)
au
to
en
c
o
d
er
an
d
(
b
)
So
f
tMa
x
class
if
ier
3
.
4
.
E
v
a
lua
t
i
o
n o
n m
ulti
-
cla
s
s
cla
s
s
if
ica
t
io
n m
o
du
le
T
h
e
So
f
tMa
x
class
if
ier
m
o
d
el
ex
ce
ls
at
id
en
tify
in
g
th
e
n
o
r
m
al
class
,
ac
cu
r
ately
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ed
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g
o
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er
3
m
illi
o
n
in
s
tan
ce
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,
as
s
h
o
wn
in
Fig
u
r
e
5
,
d
em
o
n
s
tr
atin
g
its
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f
ec
tiv
en
ess
in
r
ec
o
g
n
izin
g
n
o
n
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o
m
alo
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s
tr
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ic.
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h
e
m
o
d
el
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o
r
m
s
well
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g
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k
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t
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o
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ies
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ch
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e
ar
e
o
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is
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icatio
n
s
.
Fig
u
r
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5
.
C
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u
s
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m
atr
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x
f
o
r
m
u
lti
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class
if
ier
3
.
5
.
Dis
cus
s
io
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Ov
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th
e
b
in
a
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icatio
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s
to
o
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t
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er
f
o
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m
th
e
m
u
lti
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s
if
icatio
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ac
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ac
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ac
r
o
s
s
all
m
o
d
els.
T
h
e
p
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ed
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el
s
u
r
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o
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1
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1
8
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