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h
i
g
h
-
d
i
m
e
n
s
i
o
n
a
l
a
n
d
i
m
b
a
l
a
n
c
e
d
d
a
t
a
s
et
s
i
n
te
n
s
i
f
y
t
h
e
c
o
m
p
l
e
x
i
t
i
es
o
f
t
h
r
e
a
t
d
e
t
ec
t
i
o
n
,
l
e
ad
i
n
g
t
o
a
n
i
n
c
r
e
a
s
e
d
o
c
c
u
r
r
e
n
c
e
o
f
f
a
ls
e
p
o
s
i
ti
v
e
s
an
d
u
n
d
e
t
e
c
t
e
d
at
t
a
c
k
s
[
9
]
,
[
1
0
]
.
T
h
i
s
n
e
c
es
s
i
t
a
te
s
t
h
e
d
e
v
el
o
p
m
e
n
t
o
f
a
d
v
a
n
c
e
d
,
d
a
t
a
-
d
r
i
v
e
n
a
p
p
r
o
a
c
h
e
s
t
h
a
t
l
e
v
e
r
a
g
e
m
a
c
h
i
n
e
l
e
a
r
n
i
n
g
a
n
d
d
ee
p
l
e
a
r
n
i
n
g
t
e
c
h
n
o
l
o
g
i
es
t
o
e
n
h
a
n
c
e
t
h
e
s
ec
u
r
i
t
y
o
f
b
a
n
k
i
n
g
s
y
s
t
e
m
s
.
I
n
r
ec
e
n
t
y
ea
r
s
,
n
u
m
er
o
u
s
ap
p
r
o
ac
h
es
h
a
v
e
b
ee
n
e
x
p
lo
r
e
d
t
o
ad
d
r
ess
th
ese
ch
allen
g
es.
Fo
r
in
s
tan
ce
,
Gh
en
i
an
d
Yaseen
[
1
1
]
in
tr
o
d
u
ce
d
a
two
-
s
tep
clu
s
ter
in
g
-
b
ased
in
tr
u
s
io
n
d
etec
tio
n
m
o
d
el
u
tili
zin
g
th
e
C
I
C
I
o
T
2
0
2
3
d
ataset.
L
ev
er
a
g
in
g
g
ain
i
n
g
–
s
h
ar
i
n
g
k
n
o
wled
g
e
(
GSK)
o
p
tim
izatio
n
a
n
d
m
u
ltil
ay
er
p
er
ce
p
tr
o
n
(
ML
P),
it
ac
h
iev
ed
9
9
.
2
6
%
a
cc
u
r
ac
y
an
d
a
6
2
.
4
5
%
d
ataset
s
ize
r
ed
u
ctio
n
,
en
h
an
cin
g
e
f
f
icac
y
an
d
s
p
ee
d
.
Ho
wev
er
,
r
elian
ce
o
n
GSK
m
ay
lim
it
g
en
er
aliza
tio
n
to
d
iv
er
s
e
d
atasets
.
Al
-
Fatlawi
et
a
l.
[
1
2
]
p
r
o
p
o
s
ed
a
f
r
au
d
d
etec
tio
n
m
o
d
el
f
o
r
b
an
k
in
g
s
y
s
tem
s
u
s
in
g
g
en
etic
al
g
o
r
ith
m
s
f
o
r
f
ea
t
u
r
e
s
elec
tio
n
an
d
class
if
icatio
n
.
T
h
e
Statl
o
g
(
Ger
m
an
C
r
ed
it
Data
)
d
ataset
was
u
s
ed
,
an
d
r
esu
lts
s
h
o
wed
im
p
r
o
v
ed
p
r
e
cisi
o
n
(
9
0
.
4
%)
an
d
ac
cu
r
ac
y
(
9
1
.
0
3
%)
p
o
s
t
-
f
ea
tu
r
e
s
elec
tio
n
.
Ho
wev
er
,
lim
itat
io
n
s
in
clu
d
e
o
v
er
f
itti
n
g
r
is
k
s
with
d
ec
is
io
n
tr
ee
s
an
d
p
o
ten
tial
in
ef
f
icie
n
cy
f
o
r
ev
o
lv
in
g
f
r
a
u
d
s
ce
n
ar
i
o
s
.
Dasar
i
an
d
Kalu
r
i
[
1
3
]
p
r
esen
ted
a
p
r
iv
ac
y
-
p
r
eser
v
i
n
g
f
ed
er
ated
lear
n
in
g
(
FL)
f
r
a
m
ewo
r
k
(
2
P3
FL)
u
s
in
g
Fed
Av
g
an
d
Fed
Pro
x
al
g
o
r
ith
m
s
with
in
th
e
f
lo
we
r
f
r
am
ewo
r
k
.
I
t
em
p
l
o
y
s
th
e
C
r
ed
itC
ar
d
an
d
C
I
C
I
DS
d
atasets
.
Ach
iev
in
g
9
9
.
5
7
%
ac
cu
r
ac
y
o
n
th
e
C
r
ed
itC
ar
d
da
taset,
lim
itatio
n
s
in
clu
d
e
d
ata
q
u
ality
is
s
u
es
an
d
ch
al
len
g
es
with
m
o
d
el
c
o
n
v
e
r
g
e
n
ce
.
T
h
e
ap
p
r
o
ac
h
im
p
r
o
v
es
p
r
iv
ac
y
an
d
p
e
r
f
o
r
m
an
ce
in
d
is
tr
ib
u
ted
f
in
an
cial
s
y
s
tem
s
.
Hu
s
s
ain
et
a
l.
[
1
4
]
p
r
o
p
o
s
ed
an
en
h
an
ce
d
in
tellig
en
t
in
tr
u
s
io
n
d
etec
tio
n
s
y
s
tem
(
NI
DS)
f
o
r
e
-
co
m
m
er
ce
u
s
in
g
an
e
x
ten
d
ed
b
ac
k
wa
r
d
o
r
ac
le
m
atch
in
g
(
B
OM
)
alg
o
r
ith
m
.
T
ested
o
n
NSL
-
KDD
d
atase
ts
an
d
r
ea
l
tr
af
f
ic
s
ce
n
ar
io
s
,
it
ac
h
iev
ed
a
5
.
1
7
%
h
ig
h
er
d
etec
tio
n
r
ate
an
d
0
.
2
2
%
f
e
wer
f
alse
alar
m
s
.
Desp
ite
im
p
r
o
v
e
d
p
ac
k
et
an
al
y
s
is
,
lim
itatio
n
s
in
clu
d
e
h
ig
h
p
ac
k
et
d
r
o
p
r
ates
u
n
d
er
h
ea
v
y
tr
af
f
ic.
Ud
d
in
et
a
l.
[
1
5
]
in
t
r
o
d
u
ce
d
a
d
u
al
-
tier
ad
ap
ti
v
e
I
DS
u
s
in
g
one
-
class
class
if
ier
s
(
OC
C
)
with
s
em
i
-
s
u
p
er
v
is
ed
lea
r
n
in
g
an
d
clu
s
ter
in
g
(
u
s
f
AD
an
d
DB
SC
AN)
.
T
ested
o
n
1
0
d
atasets
(
e.
g
.
,
NSL
-
KDD,
UNSW
-
N
B
1
5
)
,
it
id
en
tifie
s
k
n
o
w
n
/u
n
k
n
o
wn
attac
k
s
,
o
v
er
co
m
in
g
ze
r
o
-
d
a
y
d
etec
tio
n
ch
allen
g
es.
R
esu
lts
s
h
o
wed
ac
cu
r
ac
y
im
p
r
o
v
em
e
n
ts
af
ter
r
etr
ain
in
g
.
L
im
itatio
n
s
in
clu
d
e
h
ig
h
r
eso
u
r
ce
r
eq
u
ir
em
e
n
ts
f
o
r
clu
s
ter
in
g
a
n
d
s
ca
lab
ilit
y
co
n
ce
r
n
s
.
Vam
s
ik
r
is
h
n
a
et
a
l.
[
1
6
]
im
p
lem
e
n
ted
a
h
ier
ar
c
h
ical
an
o
m
aly
I
DS
u
s
in
g
ar
tific
ial
n
eu
r
al
n
etwo
r
k
s
(
ANN)
in
clo
u
d
co
m
p
u
tin
g
en
v
ir
o
n
m
e
n
t.
I
t
p
r
o
ce
s
s
es
h
ig
h
-
tr
af
f
ic
d
ata
ef
f
icie
n
tly
,
ac
h
iev
in
g
s
u
p
er
io
r
ac
cu
r
ac
y
(
9
8
.
5
%
)
co
m
p
a
r
ed
to
d
ec
is
io
n
tr
ee
s
(
9
1
.
3
%).
Ho
wev
er
,
ANN
’
s
r
elian
ce
o
n
p
r
e
-
s
elec
t
ed
f
ea
tu
r
es
li
m
it
a
d
ap
tab
ilit
y
.
E
v
alu
atio
n
u
s
ed
d
atasets
with
m
ix
ed
n
o
r
m
al
an
d
m
alicio
u
s
tr
af
f
ic
to
en
h
an
ce
r
o
b
u
s
tn
ess
.
B
u
ild
in
g
o
n
th
ese
in
s
ig
h
ts
,
t
h
is
s
tu
d
y
em
p
lo
y
s
th
e
C
I
C
I
DS2
0
1
7
an
d
C
SECICIDS2
0
1
8
d
atasets
,
wh
ich
r
ep
licate
r
ea
lis
tic
n
etwo
r
k
tr
af
f
ic
s
ce
n
ar
io
s
an
d
en
c
o
m
p
ass
d
iv
er
s
e
attac
k
ty
p
es,
in
clu
d
in
g
Do
S,
DDo
S
,
B
o
tn
et,
an
d
W
eb
-
b
ased
th
r
ea
ts
[
1
7
]
,
[
1
8
]
.
T
h
ese
d
atasets
ar
e
well
-
s
u
ited
f
o
r
tr
ain
in
g
I
DS
an
d
f
o
r
tify
i
n
g
b
an
k
in
g
s
ec
to
r
s
ec
u
r
ity
.
Key
p
r
ep
r
o
ce
s
s
in
g
s
tep
s
-
s
u
ch
as
d
ata
in
teg
r
atio
n
,
en
c
o
d
in
g
,
an
d
s
tan
d
ar
d
izatio
n
-
ar
e
ap
p
lied
to
en
s
u
r
e
co
m
p
atib
ilit
y
an
d
b
o
o
s
t
m
o
d
el
ef
f
icien
cy
.
T
o
ad
d
r
ess
th
e
h
ig
h
-
d
im
en
s
io
n
al
ch
ar
ac
ter
is
tics
o
f
th
e
d
ata
b
y
u
tili
zin
g
a
b
asic
au
to
en
co
d
e
r
f
o
r
d
im
en
s
io
n
ali
ty
r
ed
u
ctio
n
.
T
h
e
au
to
e
n
co
d
e
r
p
r
eser
v
es
ess
en
tial
f
ea
tu
r
es
wh
ile
r
e
d
u
cin
g
in
p
u
t
d
im
en
s
io
n
s
,
t
h
er
eb
y
lo
wer
in
g
co
m
p
u
tatio
n
al
o
v
er
h
ea
d
.
T
o
d
etec
t
attac
k
s
,
t
h
e
s
tu
d
y
im
p
lem
en
ts
DB
SC
A
N,
a
d
en
s
ity
-
b
ased
clu
s
ter
in
g
a
lg
o
r
ith
m
.
DB
SC
AN
s
tan
d
s
o
u
t
f
r
o
m
tr
a
d
itio
n
al
tech
n
iq
u
es
b
y
id
en
tify
i
n
g
clu
s
ter
s
o
f
ar
b
itra
r
y
s
h
a
p
es
an
d
r
e
co
g
n
izin
g
n
o
is
e
p
o
i
n
ts
,
en
a
b
lin
g
it
to
e
f
f
ec
tiv
el
y
d
etec
t
co
m
p
le
x
attac
k
p
atter
n
s
with
o
u
t
r
eq
u
ir
i
n
g
p
r
io
r
k
n
o
wled
g
e
o
f
th
e
cl
u
s
ter
co
u
n
t.
T
h
e
p
r
o
p
o
s
e
d
f
r
am
ewo
r
k
u
n
if
ies
th
ese
m
eth
o
d
s
in
to
a
co
m
p
r
eh
en
s
iv
e
s
o
lu
tio
n
,
p
r
o
v
id
in
g
an
ad
ap
tiv
e
an
d
ef
f
ec
tiv
e
s
tr
ateg
y
f
o
r
s
ec
u
r
in
g
b
a
n
k
in
g
s
y
s
tem
s
.
T
h
is
s
tu
d
y
ad
v
an
ce
s
th
e
f
ield
o
f
cy
b
er
s
ec
u
r
ity
b
y
s
h
o
wca
s
in
g
h
o
w
th
e
in
teg
r
atio
n
o
f
au
to
e
n
co
d
e
r
s
an
d
DB
SC
A
N
im
p
r
o
v
es
th
e
d
ete
ctio
n
o
f
b
o
th
estab
lis
h
ed
an
d
e
m
er
g
in
g
t
h
r
ea
ts
in
th
e
b
an
k
i
n
g
d
o
m
ain
.
2.
RE
S
E
ARCH
M
E
T
H
O
D
T
h
e
p
r
o
p
o
s
ed
m
eth
o
d
,
a
d
ap
tiv
e
I
DS,
is
d
esig
n
ed
to
d
etec
t
in
tr
u
s
io
n
s
in
b
a
n
k
in
g
d
o
m
ain
u
s
in
g
clo
u
d
d
atasets
an
d
class
if
y
th
em
in
to
m
u
ltip
le
class
es,
as
s
h
o
wn
in
th
e
ar
ch
itectu
r
e
in
Fig
u
r
e
1
.
T
h
is
ar
ch
itectu
r
e
in
clu
d
es d
ataset
p
r
ep
r
o
ce
s
s
in
g
,
d
im
en
s
io
n
ality
r
e
d
u
ctio
n
,
an
d
m
u
lt
iclass
attac
k
d
etec
tio
n
.
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
A
d
a
p
tive
in
tr
u
s
io
n
d
etec
tio
n
s
ystem
w
ith
DB
S
C
A
N
to
en
h
a
n
ce
b
a
n
kin
g
…
(
S
a
th
iya
s
ee
la
n
P
eriya
s
a
my
)
249
2
.
1
.
Da
t
a
s
et
s
T
h
e
C
I
C
I
DS2
0
1
7
[
1
9
]
a
n
d
C
SECICIDS2
0
1
8
[
2
0
]
d
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u
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Dim
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[
2
2
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d
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th
e
au
to
e
n
co
d
er
is
u
s
ed
to
ex
tr
ac
t
th
e
2
1
-
d
im
en
s
io
n
al
laten
t
f
ea
tu
r
es
f
r
o
m
th
e
in
p
u
t
d
ata.
T
h
ese
f
ea
tu
r
es
cr
ea
te
a
co
n
d
e
n
s
ed
v
er
s
io
n
o
f
th
e
o
r
ig
in
al
d
ata,
h
ig
h
lig
h
tin
g
k
e
y
p
atter
n
s
an
d
r
elatio
n
s
h
i
p
s
.
B
y
r
ed
u
cin
g
t
h
e
f
ea
tu
r
e
s
p
ac
e
f
r
o
m
8
2
to
2
1
d
im
en
s
io
n
s
,
c
o
m
p
u
tatio
n
al
co
m
p
lex
ity
is
r
e
d
u
ce
d
,
allo
win
g
f
o
r
q
u
ick
er
p
r
o
ce
s
s
in
g
in
s
u
b
s
eq
u
en
t
task
s
.
T
h
e
au
to
en
co
d
e
r
n
a
tu
r
ally
elim
in
ates
n
o
is
e,
r
etain
in
g
o
n
l
y
th
e
m
o
s
t
s
ig
n
if
ican
t
f
ea
tu
r
es.
T
h
e
c
o
m
p
r
ess
ed
f
ea
tu
r
e
s
p
ac
e
s
tr
ea
m
l
in
es
th
e
clu
s
ter
in
g
p
r
o
c
ess
f
o
r
d
etec
tin
g
attac
k
s
,
en
h
an
cin
g
b
o
th
ac
cu
r
ac
y
a
n
d
r
eliab
ilit
y
.
T
h
is
d
im
en
s
io
n
alit
y
r
e
d
u
ctio
n
n
o
t
o
n
ly
p
r
e
p
ar
es
th
e
d
ataset
f
o
r
th
e
DB
S
C
AN
-
b
ased
attac
k
d
etec
t
io
n
m
o
d
el
b
u
t
also
en
s
u
r
es
s
ca
lab
ilit
y
an
d
r
o
b
u
s
tn
ess
,
wh
ich
ar
e
ess
en
tial
f
o
r
in
tr
u
s
io
n
d
etec
tio
n
in
b
a
n
k
in
g
en
v
ir
o
n
m
en
ts
.
2
.
4
.
At
t
a
c
k
det
ec
t
io
n us
ing
DB
SCA
N
T
h
e
attac
k
d
etec
tio
n
p
h
ase
e
m
p
lo
y
s
th
e
d
e
n
s
ity
-
b
ased
s
p
a
tial
clu
s
ter
in
g
o
f
ap
p
licatio
n
s
with
n
o
is
e
(
DB
SC
AN
)
alg
o
r
ith
m
is
u
s
e
d
to
d
etec
t
an
d
class
if
y
m
alicio
u
s
ac
tiv
ities
with
in
th
e
r
e
d
u
ce
d
f
ea
tu
r
e
s
p
ac
e
.
T
h
is
s
tep
p
lay
s
a
cr
u
cial
r
o
l
e
in
id
en
tify
i
n
g
in
tr
u
s
io
n
s
a
n
d
ca
teg
o
r
izin
g
th
e
m
in
to
d
i
s
tin
ct
attac
k
ty
p
es,
f
ac
ilit
atin
g
ef
f
ec
tiv
e
m
itig
atio
n
,
esp
ec
ially
in
th
e
c
o
n
tex
t
o
f
b
an
k
in
g
.
T
h
e
attac
k
d
etec
tio
n
m
o
d
u
le
u
tili
ze
s
th
e
21
-
d
im
e
n
s
io
n
al
f
ea
tu
r
e
s
et
p
r
o
d
u
ce
d
b
y
th
e
b
asic
au
to
e
n
c
o
d
er
(
b
AE
)
d
u
r
in
g
th
e
d
im
en
s
io
n
ality
r
ed
u
ctio
n
p
r
o
ce
s
s
,
o
f
f
er
in
g
a
co
m
p
ac
t
a
n
d
n
o
is
e
-
f
r
ee
r
e
p
r
esen
tatio
n
o
f
th
e
C
I
C
I
DS2
0
1
7
an
d
C
SECI
C
I
DS2
0
1
8
d
atasets
f
o
r
cl
u
s
ter
in
g
.
DB
SC
AN,
a
d
en
s
ity
-
b
ased
clu
s
ter
in
g
alg
o
r
ith
m
[
2
3
]
,
o
r
g
an
izes
d
ata
p
o
in
ts
b
ased
o
n
t
h
eir
s
p
atial
clo
s
en
ess
w
ith
in
th
e
f
ea
tu
r
e
s
p
ac
e,
wh
ich
m
a
k
es
it
p
ar
ticu
lar
ly
ef
f
ec
tiv
e
f
o
r
i
n
tr
u
s
io
n
d
etec
tio
n
.
I
ts
ab
ilit
y
to
id
en
tify
ar
b
itra
r
il
y
s
h
ap
ed
clu
s
ter
s
an
d
m
an
a
g
e
n
o
is
e
allo
ws
it
to
is
o
late
an
o
m
alies,
p
o
ten
tially
in
d
icatin
g
n
ew
o
r
p
r
e
v
io
u
s
ly
u
n
s
ee
n
attac
k
ty
p
es.
T
h
e
ess
en
tial
p
ar
am
eter
s
f
o
r
DB
SC
AN
ar
e
E
p
s
ilo
n
(
ε)
,
s
et
to
0
.
2
,
wh
ic
h
d
eter
m
in
es
th
e
m
ax
im
u
m
allo
wab
le
d
is
ta
n
ce
b
etwe
en
p
o
in
ts
to
b
e
g
r
o
u
p
ed
to
g
eth
er
,
an
d
Min
Pts
,
s
et
to
6
0
0
,
wh
ich
d
ef
in
es
th
e
m
in
im
u
m
n
u
m
b
er
o
f
p
o
in
ts
n
ee
d
e
d
to
f
o
r
m
a
d
en
s
e
r
e
g
io
n
.
T
h
e
alg
o
r
ith
m
class
if
ies
p
o
in
t
s
in
to
th
r
ee
ca
teg
o
r
ies:
co
r
e
p
o
in
ts
,
wh
ich
h
av
e
a
m
in
im
u
m
o
f
6
0
0
n
eig
h
b
o
r
s
with
in
a
r
ad
iu
s
o
f
0
.
2
;
b
o
r
d
er
p
o
in
ts
,
wh
ich
ar
e
with
in
a
co
r
e
p
o
in
t
’
s
n
eig
h
b
o
r
h
o
o
d
b
u
t
h
av
e
f
ewe
r
th
an
6
0
0
n
eig
h
b
o
r
s
;
an
d
n
o
is
e
p
o
in
ts
,
wh
ich
d
o
n
o
t
b
elo
n
g
to
an
y
clu
s
ter
an
d
ar
e
co
n
s
id
er
ed
o
u
tlier
s
o
r
an
o
m
alies.
C
o
r
e
an
d
b
o
r
d
e
r
p
o
in
ts
ar
e
clu
s
ter
ed
to
g
eth
er
b
ased
o
n
d
en
s
ity
c
o
n
n
ec
tiv
ity
,
with
ar
ea
s
o
f
h
ig
h
d
en
s
ity
f
o
r
m
in
g
s
ep
ar
ate
clu
s
ter
s
th
at
co
r
r
esp
o
n
d
to
d
if
f
er
e
n
t ty
p
es o
f
attac
k
s
DB
S
C
AN
clu
s
ter
s
ar
e
th
en
m
ap
p
ed
to
th
e
attac
k
ca
teg
o
r
ies
d
ef
in
ed
in
t
h
e
C
I
C
I
DS2
0
1
7
a
n
d
C
SECI
C
I
DS2
0
1
8
d
atasets
.
C
I
C
I
DS2
0
1
7
ca
teg
o
r
ies
in
clu
d
e
Do
S
Hu
lk
,
Po
r
tScan
,
DDo
S,
Do
S
Go
ld
en
E
y
e,
FTP
-
Patato
r
,
SS
H
-
Patato
r
,
Do
S
Slo
wlo
r
is
,
Do
S
Slo
wHT
T
PTe
s
t,
B
o
t,
W
eb
Attack
–
B
r
u
te
Fo
r
ce
,
a
n
d
W
eb
Attack
–
XSS.
C
SECI
C
I
DS2
0
1
8
ca
teg
o
r
ies
in
clu
d
e
HOI
C
,
L
OI
C
-
UDP,
an
d
L
OI
C
-
HT
T
P
i
n
th
e
DDo
S
f
am
ily
;
Hu
lk
,
Go
ld
en
E
y
e,
Slo
wHT
T
P
T
est,
an
d
Slo
wlo
r
is
in
th
e
Do
S f
am
ily
; FT
P a
n
d
S
SH in
th
e
B
r
u
te
Fo
r
ce
f
am
ily
;
an
d
B
o
t,
I
n
f
iltra
tio
n
,
an
d
W
eb
.
T
h
e
p
r
im
a
r
y
attac
k
ty
p
e
f
o
r
ea
ch
clu
s
ter
is
as
s
ig
n
ed
b
ased
o
n
th
e
m
ajo
r
ity
lab
el
o
f
th
e
p
o
in
ts
it
co
n
tain
s
,
wh
er
ea
s
o
u
tlier
s
(
n
o
is
e
p
o
in
t
s
)
ar
e
ex
am
in
ed
f
u
r
th
er
as
p
o
t
en
tial
n
o
v
el
o
r
r
ar
e
attac
k
ty
p
es
th
at
n
ee
d
m
o
r
e
d
e
tailed
in
v
esti
g
atio
n
.
T
h
e
o
u
tco
m
e
o
f
th
is
p
h
ase
is
a
m
u
lti
-
cla
s
s
cla
s
s
if
icatio
n
o
f
attac
k
s
th
at
in
clu
d
es p
r
ed
ef
i
n
e
d
ca
teg
o
r
ies.
E
ac
h
d
ata
p
o
i
n
t in
th
e
test
in
g
s
et
is
ass
ig
n
ed
a
s
p
ec
if
ic
attac
k
lab
el
b
ased
o
n
its
clu
s
ter
,
m
ar
k
ed
as
n
o
is
e,
o
r
class
if
ied
as
b
en
ig
n
(
n
o
r
m
al)
if
it
is
id
en
tifie
d
as
n
o
n
-
m
alicio
u
s
tr
af
f
ic.
Key
o
u
tc
o
m
es
in
clu
d
e
clu
s
ter
ass
ig
n
m
en
ts
,
wh
er
e
DB
S
C
AN
eith
er
g
r
o
u
p
s
p
o
in
ts
o
r
d
esig
n
ates
th
em
as
n
o
is
e;
attac
k
ca
teg
o
r
ies,
wh
er
e
clu
s
ter
s
ar
e
ass
o
ciate
d
with
co
r
r
esp
o
n
d
in
g
attac
k
t
y
p
es;
an
d
a
n
o
m
aly
d
etec
tio
n
,
wh
er
e
n
o
is
e
p
o
i
n
ts
ar
e
f
lag
g
e
d
as p
o
ten
tial a
n
o
m
a
lies
f
o
r
f
u
r
th
e
r
an
aly
s
is
.
DB
S
C
AN
ef
f
icien
tly
m
an
ag
es n
o
is
e
b
y
is
o
latin
g
n
ew
attac
k
s
th
at
d
o
n
o
t c
o
n
f
o
r
m
to
ex
is
tin
g
clu
s
ter
s
an
d
ca
n
d
etec
t
clu
s
ter
s
o
f
an
y
s
h
ap
e,
e
n
s
u
r
in
g
a
p
r
ec
is
e
r
ep
r
esen
tatio
n
o
f
v
a
r
io
u
s
attac
k
p
atter
n
s
.
As
an
un
s
u
p
er
v
is
ed
lear
n
in
g
alg
o
r
ith
m
,
DB
SC
A
N
d
o
es
n
o
t
r
ely
o
n
lab
eled
d
ata
f
o
r
tr
ain
in
g
,
m
ak
in
g
it
id
ea
l
f
o
r
r
ea
l
-
wo
r
ld
s
itu
atio
n
s
wh
er
e
attac
k
lab
els
m
ay
n
o
t
b
e
r
ea
d
ily
av
ailab
le.
T
h
is
is
p
ar
ticu
lar
ly
im
p
o
r
tan
t
in
th
e
b
an
k
in
g
s
ec
to
r
,
wh
er
e
th
e
s
en
s
itiv
ity
o
f
th
e
s
y
s
tem
allo
ws
f
o
r
th
e
ea
r
ly
i
d
en
tific
atio
n
o
f
an
o
m
alies
in
h
ig
h
-
d
im
en
s
io
n
al
tr
af
f
ic
d
ata.
DB
SC
AN
’
s
ab
ilit
y
to
h
an
d
le
n
o
i
s
y
an
d
im
b
alan
ce
d
d
ata
is
es
s
en
tial
f
o
r
d
y
n
am
ic
b
an
k
in
g
n
etwo
r
k
s
th
at
ex
p
er
i
en
ce
f
lu
ctu
atin
g
tr
af
f
ic
p
atter
n
s
.
I
ts
ab
ilit
y
to
clas
s
if
y
in
to
m
u
ltip
le
ca
teg
o
r
ies
d
iv
id
es
attac
k
s
in
to
1
1
d
is
ti
n
ct
g
r
o
u
p
s
,
o
f
f
er
in
g
v
alu
ab
l
e
in
s
ig
h
ts
f
o
r
th
e
im
p
lem
en
tatio
n
o
f
tar
g
eted
m
itig
atio
n
s
tr
ateg
ies.
B
y
tr
an
s
f
o
r
m
in
g
t
h
e
r
ed
u
ce
d
f
ea
tu
r
e
s
p
ac
e
in
to
m
ea
n
in
g
f
u
l
clu
s
ter
s
th
at
r
ep
r
esen
t
s
p
ec
if
ic
attac
k
ty
p
es,
th
e
DB
SC
AN
-
b
ased
p
r
o
ce
s
s
p
r
o
v
id
es
ef
f
ec
tiv
e
in
tr
u
s
io
n
d
etec
tio
n
,
cu
s
to
m
ized
to
m
ee
t
th
e
s
ec
u
r
ity
d
em
a
n
d
s
o
f
t
h
e
b
a
n
k
in
g
s
ec
to
r
,
th
er
eb
y
s
tr
en
g
th
en
in
g
o
v
er
all
n
etwo
r
k
r
esil
ien
ce
an
d
s
ec
u
r
ity
.
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
A
d
a
p
tive
in
tr
u
s
io
n
d
etec
tio
n
s
ystem
w
ith
DB
S
C
A
N
to
en
h
a
n
ce
b
a
n
kin
g
…
(
S
a
th
iya
s
ee
la
n
P
eriya
s
a
my
)
251
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
is
s
ec
tio
n
p
r
o
v
id
es
a
d
etail
ed
an
aly
s
is
o
f
th
e
ex
p
e
r
im
en
t
al
r
esu
lts
,
em
p
h
asizin
g
th
e
p
e
r
f
o
r
m
a
n
ce
ev
alu
atio
n
o
f
th
e
p
r
o
p
o
s
ed
ad
ap
tiv
e
I
DS
m
o
d
el
a
g
ain
s
t
co
n
v
en
tio
n
al
ap
p
r
o
ac
h
es.
K
ey
m
etr
ics
s
u
ch
as
p
r
ec
is
io
n
,
r
ec
all,
F
-
m
ea
s
u
r
e,
a
cc
u
r
ac
y
,
an
d
AUC
-
R
O
C
cu
r
v
es
ar
e
u
s
ed
to
h
ig
h
lig
h
t
th
e
m
o
d
el
’
s
ef
f
ec
ti
v
en
ess
in
o
v
er
c
o
m
in
g
t
h
e
ch
allen
g
es o
f
in
tr
u
s
io
n
d
etec
tio
n
.
T
h
e
ch
ar
t
in
Fig
u
r
e
2
d
ep
icts
th
e
m
ea
n
s
q
u
ar
ed
er
r
o
r
(
MSE
)
tr
en
d
s
o
v
er
4
0
e
p
o
ch
s
f
o
r
C
I
C
I
DS2
0
1
7
an
d
C
SE
-
C
I
C
I
DS2
0
1
8
d
atasets
d
u
r
in
g
tr
ai
n
in
g
a
n
d
test
in
g
,
u
s
in
g
an
a
u
to
en
co
d
er
f
o
r
d
im
e
n
s
io
n
ality
r
ed
u
ctio
n
.
I
n
itially
,
MSE
d
e
cr
ea
s
es
s
h
ar
p
ly
,
s
tab
ilizin
g
ar
o
u
n
d
ep
o
c
h
1
5
.
B
o
th
d
atasets
s
h
o
w
co
n
s
is
ten
t
co
n
v
er
g
en
ce
b
el
o
w
th
e
th
r
esh
o
ld
(
2
.
6
9
8
6
)
,
i
n
d
icatin
g
e
f
f
ec
t
iv
e
r
ec
o
n
s
tr
u
ctio
n
o
f
f
ea
tu
r
es.
T
r
ain
in
g
e
r
r
o
r
s
f
o
r
C
I
C
I
DS2
0
1
7
an
d
C
SE
-
C
I
C
I
DS2
0
1
8
ar
e
m
ar
g
in
ally
lo
wer
th
an
test
er
r
o
r
s
,
r
ef
le
ctin
g
th
e
m
o
d
el
’
s
g
en
er
aliza
tio
n
ca
p
ab
ilit
y
.
T
h
i
s
s
u
g
g
ests
th
e
au
to
en
co
d
er
s
u
cc
ess
f
u
lly
r
ed
u
ce
s
d
im
en
s
io
n
s
wh
ile
p
r
eser
v
in
g
cr
itical
p
atter
n
s
.
Fig
u
r
e
2
.
T
r
en
d
s
o
f
MSE
d
u
r
i
n
g
d
im
e
n
s
io
n
ality
r
ed
u
ctio
n
T
h
e
p
e
r
f
o
r
m
an
ce
o
f
th
e
p
r
o
p
o
s
ed
m
o
d
el
is
ev
al
u
ated
a
g
ai
n
s
t
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
in
e
(
SVM
)
,
lo
n
g
s
h
o
r
t
-
ter
m
m
em
o
r
y
(
L
STM
)
,
an
d
K
-
Me
an
s
o
n
th
e
C
I
C
-
I
DS2
0
1
7
d
ataset.
Fig
u
r
e
3
illu
s
tr
ates
a
d
etailed
co
m
p
ar
is
o
n
o
f
th
eir
r
esp
ec
tiv
e
m
etr
ics.
T
h
e
p
r
o
p
o
s
ed
m
o
d
el
s
ig
n
i
f
ican
tly
o
u
tp
er
f
o
r
m
s
th
e
o
th
e
r
s
,
ac
h
iev
i
n
g
th
e
h
ig
h
est
p
r
ec
is
io
n
(
0
.
9
9
4
8
)
,
r
ec
all
(
0
.
9
9
0
7
)
,
F
-
m
ea
s
u
r
e
(
0
.
9
9
2
7
)
,
a
n
d
ac
cu
r
ac
y
(
0
.
9
8
8
6
)
.
W
h
ile
SVM,
L
STM
,
an
d
K
-
Me
an
s
s
h
o
w
c
o
m
p
ar
ab
le
r
esu
lts
with
s
lig
h
t
v
ar
iatio
n
s
,
th
eir
m
etr
ics
ar
e
co
n
s
is
ten
tly
lo
wer
.
T
h
is
h
ig
h
lig
h
ts
th
e
p
r
o
p
o
s
ed
m
o
d
el
’
s
s
u
p
er
io
r
ab
ilit
y
to
d
e
tect
an
d
class
if
y
m
alicio
u
s
ac
tiv
ities
ac
cu
r
ately
,
d
em
o
n
s
tr
atin
g
its
ef
f
ec
tiv
e
n
ess
f
o
r
in
tr
u
s
io
n
d
etec
tio
n
.
Fig
u
r
e
3
.
Per
f
o
r
m
an
c
e
ev
alu
at
io
n
o
f
t
h
e
p
r
o
p
o
s
ed
m
o
d
el
o
n
t
h
e
C
I
C
-
I
DS2
0
1
7
d
ataset
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.
1
5
,
No
.
1
,
Ma
r
ch
20
2
6
:
24
7
-
25
6
252
T
ab
le
1
s
h
o
ws
th
e
p
er
f
o
r
m
a
n
c
e
o
f
th
e
p
r
o
p
o
s
ed
m
o
d
el
co
m
p
ar
ed
to
SVM,
L
STM
,
an
d
K
-
Me
an
s
o
n
th
e
C
SE
-
C
I
C
-
I
DS2
0
1
8
d
ataset.
T
h
e
p
r
o
p
o
s
ed
m
o
d
el
ac
h
iev
es
th
e
h
ig
h
est
p
r
ec
is
io
n
(
0
.
9
9
6
6
)
,
r
ec
all
(
0
.
9
9
0
1
)
,
F
-
m
ea
s
u
r
e
(
0
.
9
9
3
3
)
,
an
d
ac
c
u
r
ac
y
(
0
.
9
8
8
8
)
,
s
ig
n
if
ican
tly
o
u
tp
e
r
f
o
r
m
i
n
g
th
e
o
th
er
m
o
d
els.
W
h
ile
SVM,
L
STM
,
an
d
K
-
Me
an
s
ex
h
ib
it
r
ea
s
o
n
ab
le
r
esu
lts
,
th
eir
p
er
f
o
r
m
an
ce
m
etr
ics
ar
e
co
n
s
is
ten
tly
lo
wer
,
p
ar
ticu
lar
ly
in
p
r
ec
is
io
n
an
d
r
e
ca
ll.
T
h
is
h
ig
h
lig
h
ts
th
e
p
r
o
p
o
s
ed
m
o
d
el
’
s
s
u
p
er
io
r
ef
f
ec
tiv
e
n
ess
an
d
r
eliab
ilit
y
in
d
etec
tin
g
a
n
d
class
if
y
in
g
m
alicio
u
s
ac
tiv
ities
in
th
e
d
ataset.
T
ab
le
1
.
Per
f
o
r
m
an
ce
ev
alu
ati
o
n
o
f
th
e
p
r
o
p
o
s
ed
m
o
d
el
o
n
t
h
e
C
SE
-
C
I
C
-
I
DS2
0
1
8
d
ataset
M
e
a
su
r
e
s
S
V
M
[
2
4
]
LSTM
[
2
5
]
K
-
M
e
a
n
s
[
2
6
]
P
r
o
p
o
se
d
P
r
e
c
i
s
i
o
n
0
.
8
3
0
4
0
.
8
2
7
0
0
.
8
1
2
0
0
.
9
9
6
6
R
e
c
a
l
l
0
.
9
2
8
7
0
.
9
2
4
6
0
.
8
9
9
5
0
.
9
9
0
1
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-
M
e
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s
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r
e
0
.
8
7
6
8
0
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8
7
3
1
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.
9
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3
A
c
c
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r
a
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y
0
.
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6
8
9
0
.
9
6
7
9
0
.
9
6
3
2
0
.
9
8
8
8
Fig
u
r
e
4
p
r
esen
ts
th
e
AUC
-
R
OC
cu
r
v
es
f
o
r
th
e
DB
SC
AN
-
b
ased
I
DS
ap
p
lied
to
C
I
C
I
DS2
0
1
7
a
n
d
C
SE
-
C
I
C
I
DS2
0
1
8
d
atasets
.
T
h
e
cu
r
v
es
s
h
o
w
a
h
ig
h
tr
u
e
p
o
s
itiv
e
r
ate
(
T
PR
)
f
o
r
lo
w
f
alse
p
o
s
itiv
e
r
ates
(
FP
R
)
,
in
d
icatin
g
s
tr
o
n
g
cla
s
s
if
icatio
n
p
er
f
o
r
m
a
n
ce
.
B
o
t
h
d
atasets
ac
h
iev
e
n
ea
r
-
p
e
r
f
ec
t
AUC
s
co
r
es,
r
ef
lectin
g
th
e
m
o
d
el
’
s
ab
il
ity
to
d
is
tin
g
u
is
h
b
etwe
en
b
en
ig
n
an
d
m
alicio
u
s
tr
af
f
ic
ef
f
ec
ti
v
ely
.
T
h
e
o
v
e
r
lap
o
f
cu
r
v
es
s
u
g
g
ests
co
n
s
is
ten
t
p
er
f
o
r
m
an
ce
ac
r
o
s
s
d
atasets
.
T
h
e
d
ash
ed
d
iag
o
n
al
lin
e
r
ep
r
esen
ts
r
an
d
o
m
g
u
ess
in
g
,
wh
ich
th
e
m
o
d
el
s
ig
n
if
ican
tly
o
u
t
p
er
f
o
r
m
s
.
Fig
u
r
e
4
.
AUC
-
R
OC
cu
r
v
es f
o
r
th
e
ad
a
p
tiv
e
I
DS a
cr
o
s
s
d
at
asets
Fig
u
r
e
5
s
h
o
ws
th
e
ac
cu
r
ac
y
co
m
p
ar
is
o
n
o
f
th
e
p
r
o
p
o
s
ed
a
d
ap
tiv
e
I
DS
m
o
d
el
with
SVM,
L
STM
,
an
d
K
-
Me
an
s
ac
r
o
s
s
th
e
C
SE
-
C
I
C
-
I
DS2
0
1
8
an
d
C
I
C
-
I
DS2
0
1
7
d
atasets
.
T
h
e
p
r
o
p
o
s
e
d
m
o
d
el
co
n
s
is
ten
tly
o
u
tp
er
f
o
r
m
s
e
x
i
s
tin
g
m
eth
o
d
s
,
ac
h
iev
in
g
an
ac
cu
r
ac
y
o
f
0
.
9
8
8
8
o
n
C
SECICIDS2
0
1
8
an
d
0
.
9
8
8
6
o
n
C
I
C
I
DS2
0
1
7
.
W
h
ile
SVM,
L
STM
,
an
d
K
-
Me
an
s
ex
h
i
b
it
co
m
p
etitiv
e
ac
cu
r
ac
ies
(
r
a
n
g
in
g
b
etwe
en
0
.
9
6
3
2
an
d
0
.
9
7
3
1
)
,
th
e
p
r
o
p
o
s
ed
m
o
d
el
’
s
s
u
p
er
io
r
p
e
r
f
o
r
m
an
ce
h
i
g
h
lig
h
ts
its
r
o
b
u
s
tn
ess
a
n
d
r
eli
ab
ilit
y
f
o
r
in
tr
u
s
io
n
d
etec
tio
n
ac
r
o
s
s
d
iv
er
s
e
d
atas
ets.
T
h
is
em
p
h
asizes
its
ad
ap
t
ab
ilit
y
an
d
ef
f
ec
tiv
en
ess
in
a
d
d
r
ess
in
g
co
m
p
lex
th
r
ea
t p
atter
n
s
.
T
h
e
p
r
o
p
o
s
ed
ad
a
p
tiv
e
I
D
S
m
o
d
el
d
em
o
n
s
tr
ates
r
em
ar
k
ab
le
p
er
f
o
r
m
an
ce
ac
r
o
s
s
m
u
ltip
le
b
en
ch
m
ar
k
s
,
co
n
s
is
ten
tly
s
u
r
p
ass
in
g
tr
ad
itio
n
al
m
eth
o
d
s
lik
e
SVM,
L
STM
,
an
d
K
-
Me
an
s
in
b
o
th
C
I
C
I
DS2
0
1
7
an
d
C
SECICID
S2
0
1
8
d
atasets
.
T
h
e
au
t
o
en
co
d
er
’
s
ef
f
icien
t
d
im
e
n
s
io
n
ality
r
ed
u
ctio
n
is
ev
id
e
n
t
f
r
o
m
th
e
MSE
tr
e
n
d
s
,
w
h
er
e
c
o
n
v
er
g
en
ce
b
elo
w
th
e
s
et
th
r
e
s
h
o
ld
af
f
i
r
m
s
th
e
m
o
d
el
’
s
ab
il
ity
to
r
etain
cr
itical
f
ea
tu
r
e
p
atter
n
s
.
T
h
e
s
lig
h
tly
lo
wer
tr
ain
in
g
er
r
o
r
s
co
m
p
ar
e
d
to
test
in
g
e
r
r
o
r
s
r
ef
lect
th
e
m
o
d
el
’
s
ex
ce
llen
t
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
A
d
a
p
tive
in
tr
u
s
io
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ystem
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ith
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a
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ce
b
a
n
kin
g
…
(
S
a
th
iya
s
ee
la
n
P
eriya
s
a
my
)
253
g
en
er
aliza
tio
n
ca
p
a
b
ilit
y
.
Per
f
o
r
m
an
ce
m
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u
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m
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el
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s
s
u
p
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ity
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ith
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h
ig
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est
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r
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is
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,
r
ec
all,
F
-
m
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r
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,
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n
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a
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d
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th
e
p
r
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ap
p
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m
alicio
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s
ac
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ticu
lar
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n
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r
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p
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ec
t
AUC
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R
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cu
r
v
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is
h
in
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etwe
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m
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ic.
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m
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el
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ess
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atasets
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atter
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er
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Fig
u
r
e
5
.
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ac
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alicio
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ile
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ates
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m
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ac
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r
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g
9
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is
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s
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a
cy
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ad
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d
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r
atin
g
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ea
l
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tim
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th
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ea
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tellig
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f
o
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d
y
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itig
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s
tr
ateg
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s
.
T
h
is
r
esear
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u
n
d
er
s
co
r
es
th
e
p
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ten
tial
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f
co
m
b
in
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g
m
ac
h
in
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lear
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i
n
g
tech
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iq
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es with
clu
s
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alg
o
r
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m
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b
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in
cr
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s
s
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as b
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k
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.
F
UNDING
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NF
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M
A
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Au
th
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s
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.
AUTHO
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NS ST
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C
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s
h
ip
d
is
p
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tes,
an
d
f
ac
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co
llab
o
r
atio
n
.
Evaluation Warning : The document was created with Spire.PDF for Python.
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RE
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NC
E
S
[
1
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E.
G
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h
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[
2
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.
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[
4
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.
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Evaluation Warning : The document was created with Spire.PDF for Python.