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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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Vo
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1
6
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No
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Feb
r
u
ar
y
20
2
6
:
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3
7
-
449
438
Ma
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n
b
o
th
d
etec
t
an
d
p
r
o
ac
tiv
ely
r
esp
o
n
d
to
cy
b
er
th
r
ea
ts
in
r
ea
l
-
tim
e.
T
o
ad
d
r
ess
th
is
g
ap
,
th
is
p
ap
er
p
r
o
p
o
s
es
a
n
ad
v
an
ce
d
r
ea
l
-
tim
e
in
tr
u
s
io
n
d
etec
tio
n
an
d
p
r
o
tectio
n
s
y
s
tem
(
I
DPS)
th
at
co
m
b
in
es
m
ac
h
in
e
lear
n
in
g
,
n
etwo
r
k
f
o
r
en
s
ics,
an
d
a
u
to
m
ated
b
lo
ck
in
g
to
d
etec
t,
an
aly
ze
,
an
d
m
itig
ate
c
y
b
er
t
h
r
ea
ts
in
r
ea
l
-
tim
e.
T
h
e
f
r
am
ewo
r
k
in
teg
r
ates
n
etwo
r
k
tr
af
f
ic
an
al
y
s
is
u
s
in
g
W
ir
esh
ar
k
with
m
ac
h
in
e
lear
n
in
g
al
g
o
r
ith
m
s
to
class
if
y
an
d
b
lo
ck
m
alicio
u
s
ac
tiv
ities
,
co
n
tr
ib
u
tin
g
to
e
n
h
an
ce
d
cy
b
er
s
ec
u
r
ity
p
r
ac
tices.
T
h
aseen
et
a
l.
[
9
]
s
p
ec
if
ically
an
aly
ze
d
W
ir
esh
ar
k
PC
AP
f
ile
s
,
wh
er
ea
s
o
u
r
wo
r
k
u
s
es
th
e
C
I
C
I
DS
-
2
0
1
7
d
ataset,
wh
ich
is
wid
ely
r
ec
o
g
n
ized
an
d
co
n
s
is
ts
o
f
d
if
f
er
e
n
t ty
p
es o
f
attac
k
s
alo
n
g
with
u
p
-
to
-
d
ate
n
etwo
r
k
attac
k
p
atter
n
s
.
T
h
e
p
r
im
ar
y
aim
o
f
th
is
wo
r
k
is
to
d
ev
elo
p
a
p
r
ac
tical,
s
ca
lab
le,
an
d
d
ep
lo
y
ab
le
m
ac
h
in
e
lear
n
in
g
-
b
ased
f
r
am
ewo
r
k
th
at
n
o
t
o
n
l
y
d
etec
ts
in
tr
u
s
io
n
s
with
h
ig
h
ac
cu
r
ac
y
b
u
t
also
in
itiates
au
to
m
ated
n
etwo
r
k
-
lev
el
co
u
n
ter
m
ea
s
u
r
es.
Ou
r
ce
n
tr
al
an
aly
s
is
is
th
at
co
m
b
in
in
g
r
ea
l
-
tim
e
p
ac
k
et
a
n
aly
s
is
with
ML
-
d
r
iv
en
d
etec
tio
n
an
d
au
to
n
o
m
o
u
s
b
lo
ck
in
g
s
ig
n
if
ican
tly
en
h
an
ce
s
th
e
ef
f
ec
tiv
en
ess
o
f
in
tr
u
s
io
n
d
etec
tio
n
s
y
s
tem
s
.
T
h
e
k
ey
c
o
n
tr
ib
u
tio
n
s
o
f
o
u
r
wo
r
k
ar
e
as f
o
llo
ws:
a.
W
e
h
av
e
u
s
ed
t
h
e
C
I
C
I
DS
-
2
0
1
7
d
ataset
to
tr
ain
o
u
r
i
n
tr
u
s
io
n
d
etec
tio
n
m
o
d
el
an
d
em
p
lo
y
ed
W
ir
esh
ar
k
,
a
wid
ely
u
s
ed
p
ac
k
et
-
ca
p
tu
r
in
g
an
d
n
etwo
r
k
an
aly
s
is
to
o
l,
to
test
th
e
m
o
d
el
’
s
ac
cu
r
ac
y
with
r
ea
l
-
tim
e
d
ata.
b.
W
e
h
av
e
d
etec
ted
ab
n
o
r
m
al
p
ac
k
ets
th
at
ar
e
a
u
to
m
atica
lly
lo
g
g
ed
in
to
a
d
y
n
am
ically
u
p
d
ated
d
atab
ase,
wh
ich
h
elp
s
p
r
e
v
en
t p
o
ten
tial
th
r
ea
ts
b
y
s
to
r
in
g
d
etails f
o
r
f
u
r
th
er
a
n
aly
s
is
an
d
ac
tio
n
.
c.
W
e
h
av
e
d
ev
elo
p
ed
a
n
in
ter
a
ctiv
e
web
ap
p
licatio
n
f
o
r
v
is
u
aliza
tio
n
an
d
an
aly
s
is
,
wh
ic
h
p
r
o
v
id
es
r
ea
l
-
tim
e
in
s
ig
h
ts
in
to
n
o
r
m
al
a
n
d
ab
n
o
r
m
al
n
etwo
r
k
p
ac
k
ets,
en
h
an
cin
g
u
s
er
u
n
d
er
s
tan
d
in
g
o
f
n
etwo
r
k
tr
a
f
f
ic.
d.
A
p
r
o
to
ty
p
e
b
lo
ck
e
r
s
y
s
tem
is
d
ev
elo
p
ed
th
at
r
etr
iev
es
m
alicio
u
s
I
P
ad
d
r
ess
es
id
en
tifie
d
f
r
o
m
t
h
e
d
atab
ase
in
r
ea
l tim
e
an
d
p
r
o
a
ctiv
ely
b
lo
ck
s
th
ese
ad
d
r
ess
es,
th
er
eb
y
im
p
r
o
v
in
g
n
etwo
r
k
s
e
cu
r
ity
.
e.
T
h
e
p
r
o
p
o
s
ed
s
y
s
tem
d
em
o
n
s
tr
ated
an
ac
cu
r
ac
y
o
f
8
1
%
i
n
class
if
y
in
g
n
etwo
r
k
tr
af
f
ic,
o
u
tp
e
r
f
o
r
m
in
g
s
im
ilar
m
o
d
els in
s
ca
lab
ilit
y
an
d
d
etec
tio
n
tim
e.
T
h
e
r
em
ain
d
er
o
f
th
is
p
a
p
er
i
s
s
tr
u
ctu
r
ed
as
f
o
llo
ws:
s
ec
tio
n
2
r
ev
iews
th
e
r
elev
an
t
lite
r
atu
r
e
an
d
p
r
ev
io
u
s
r
esear
ch
in
th
e
f
iel
d
.
Sectio
n
3
o
u
tlin
es
th
e
f
o
u
n
d
atio
n
al
co
n
ce
p
ts
an
d
th
e
in
tu
itiv
e
ap
p
r
o
ac
h
u
n
d
er
ly
i
n
g
o
u
r
wo
r
k
.
T
h
e
d
e
tailed
m
eth
o
d
o
lo
g
y
is
p
r
esen
t
ed
in
s
ec
tio
n
4
.
Sectio
n
5
d
is
cu
s
s
es
th
e
r
esu
lts
o
b
tain
ed
a
n
d
p
r
o
v
id
es
a
c
o
m
p
r
eh
en
s
iv
e
an
aly
s
is
.
Fin
ally
,
s
ec
tio
n
6
co
n
clu
d
es
th
e
p
ap
er
a
n
d
s
u
g
g
ests
d
ir
ec
tio
n
s
f
o
r
f
u
tu
r
e
r
esear
ch
.
2.
L
I
T
E
R
AT
U
RE
R
E
VI
E
W
An
in
tr
u
s
io
n
d
etec
tio
n
s
y
s
tem
(
I
DS)
is
ess
en
tial
f
o
r
cy
b
e
r
s
ec
u
r
ity
,
m
o
n
it
o
r
in
g
n
etwo
r
k
tr
af
f
ic
f
o
r
m
alicio
u
s
ac
tiv
ities
[
1
0
]
.
De
tectio
n
tech
n
iq
u
es
in
clu
d
e
p
ac
k
et
an
aly
s
is
[
1
1
]
an
d
f
lo
w
d
ata
an
aly
s
is
[
3
]
.
Sig
n
atu
r
e
-
b
ased
I
DS
d
etec
ts
k
n
o
wn
th
r
ea
ts
b
u
t
s
tr
u
g
g
le
s
with
n
ew
attac
k
s
[
9
]
,
wh
ile
an
o
m
aly
-
b
ased
ap
p
r
o
ac
h
es
u
s
e
m
ac
h
in
es
to
id
en
tify
ze
r
o
-
d
ay
th
r
ea
ts
[
8
]
.
R
u
le
-
b
ased
an
d
clo
u
d
-
b
ased
m
et
h
o
d
s
en
h
a
n
ce
r
ea
l
-
tim
e
d
etec
tio
n
[
1
2
]
.
Ma
ch
in
e
lear
n
in
g
tech
n
i
q
u
es
lik
e
K
-
n
e
ar
est
n
eig
h
b
o
r
s
(
K
-
NN)
,
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
in
e
(
SVM)
,
an
d
co
n
v
o
l
u
tio
n
al
n
e
u
r
al
n
etwo
r
k
(
C
NN)
im
p
r
o
v
e
ac
cu
r
ac
y
[
1
3
]
,
[
8
]
,
an
d
h
y
b
r
id
s
y
s
tem
s
f
u
r
t
h
er
en
h
an
ce
d
etec
tio
n
r
ates
wit
h
r
e
d
u
ce
d
f
alse
p
o
s
itiv
es.
R
ec
en
t
s
tu
d
ies
p
r
o
p
o
s
e
in
te
g
r
ated
I
DS
m
o
d
els
lev
er
ag
in
g
ML
an
d
r
u
le
-
b
ased
ap
p
r
o
ac
h
es f
o
r
r
o
b
u
s
t
s
ec
u
r
ity
[
1
4
]
,
[
1
5
]
.
Usi
n
g
s
o
p
h
is
ticated
alg
o
r
ith
m
s
an
d
in
-
d
ep
th
ex
am
in
atio
n
o
f
ea
ch
p
ac
k
et
’
s
d
ata,
we
h
av
e
s
elec
ted
m
ac
h
in
e
lear
n
in
g
-
b
ased
d
ete
ctio
n
an
d
p
ac
k
et
an
aly
s
is
m
eth
o
d
s
f
o
r
in
tr
u
s
io
n
d
etec
tio
n
in
o
u
r
s
tu
d
y
.
B
y
co
m
b
in
in
g
th
ese
s
tr
ateg
ies,
we
aim
to
im
p
r
o
v
e
in
tr
u
s
io
n
d
etec
tio
n
s
y
s
tem
s
’
ca
p
ab
il
ity
to
id
en
tif
y
an
d
n
eu
tr
alize
o
n
lin
e
t
h
r
ea
ts
.
Usi
n
g
d
if
f
er
e
n
t
s
tr
ateg
ies,
a
v
ar
iety
o
f
m
ac
h
in
e
lear
n
in
g
m
o
d
els
h
av
e
b
ee
n
p
r
o
p
o
s
ed
o
n
th
e
C
I
C
I
DS
-
2
0
1
7
d
ataset.
I
n
o
r
d
er
to
id
en
tif
y
n
etwo
r
k
a
s
s
au
lts
in
th
e
C
I
C
I
DS
-
2
0
1
7
d
ataset,
Pan
war
et
a
l.
[
1
4
]
u
s
ed
eig
h
t
s
u
p
er
v
is
ed
cl
ass
if
icatio
n
alg
o
r
ith
m
s
,
i
n
clu
d
in
g
Ga
u
s
s
ian
NB
(
GNB),
B
er
n
o
u
lliNB
(
B
NB
)
,
d
ec
is
io
n
tr
ee
(
DT
)
,
K
-
n
ea
r
est
n
eig
h
b
o
r
s
(
K
-
NN)
,
lo
g
is
tic
r
eg
r
e
s
s
io
n
(
L
R
)
,
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
in
e
(
SVM)
,
r
an
d
o
m
f
o
r
est
(
R
F)
,
an
d
s
to
ch
asti
c
g
r
ad
ien
t
d
escen
t
(
SGD)
.
T
h
r
ee
m
ac
h
in
e
lear
n
in
g
m
o
d
els
wer
e
cr
ea
ted
b
y
E
lm
asri
et
a
l.
[
1
5
]
u
tili
zin
g
th
e
lo
ca
l
o
u
tlier
f
ac
t
o
r
(
L
OF)
,
im
p
r
o
v
e
d
k
-
n
ea
r
est
n
eig
h
b
o
r
s
(
KNN)
alg
o
r
ith
m
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
I
DP
S
:
A
ma
ch
in
e
lea
r
n
in
g
fr
a
mewo
r
k
fo
r
r
ea
l
-
time
in
tr
u
s
i
o
n
d
etec
tio
n
a
n
d
…
(
R
a
is
a
F
a
b
ih
a
)
439
Var
io
u
s
s
tu
d
ies
h
ig
h
lig
h
t
th
e
ef
f
ec
tiv
en
ess
o
f
ML
m
o
d
els
in
d
etec
tin
g
n
etwo
r
k
attac
k
s
u
s
in
g
th
e
C
I
C
I
DS
-
2
0
1
7
d
ataset
[
1
6
]
,
[
1
7
]
,
[
1
8
]
,
[
1
9
]
.
W
ir
esh
ar
k
,
a
p
o
wer
f
u
l
n
etwo
r
k
p
r
o
to
c
o
l
an
al
y
ze
r
,
is
wid
ely
u
s
ed
f
o
r
d
ig
ital
f
o
r
en
s
ics
an
d
cy
b
er
s
ec
u
r
ity
.
Kam
b
le
et
a
l
.
[
2
0
]
d
em
o
n
s
tr
ated
its
u
tili
ty
in
d
ata
co
llectio
n
an
d
m
o
n
ito
r
in
g
,
wh
ile
So
e
p
en
o
[
2
1
]
em
p
h
asized
its
s
u
p
er
io
r
ity
o
v
er
T
C
Pd
u
m
p
an
d
NetFlo
w
f
o
r
r
ea
l
-
tim
e
p
ac
k
et
an
aly
s
is
.
Do
d
iy
a
et
a
l.
[
2
2
]
u
tili
ze
d
W
ir
esh
ar
k
to
id
en
tif
y
in
d
icato
r
s
o
f
co
m
p
r
o
m
is
e
(
I
OC
s
)
f
o
r
m
alwa
r
e
d
etec
tio
n
.
C
h
au
d
h
ar
y
et
a
l.
[
2
3
]
ex
p
lo
r
ed
n
etwo
r
k
tr
af
f
i
c
an
aly
s
is
(
NT
A)
with
W
ir
esh
ar
k
,
d
e
v
elo
p
in
g
g
eo
lo
ca
tio
n
-
b
ased
v
is
u
aliza
tio
n
f
o
r
s
ec
u
r
ity
tr
ac
k
in
g
.
Ma
b
s
a
li
et
a
l.
[
2
4
]
u
s
ed
W
ir
esh
ar
k
to
d
etec
t
T
C
P
S
YN
f
lo
o
d
attac
k
s
,
an
aly
zi
n
g
tr
a
f
f
ic
p
atter
n
s
an
d
v
u
ln
er
a
b
ilit
ies.
Ou
r
s
tu
d
y
b
u
ild
s
o
n
th
ese
in
s
ig
h
ts
b
y
estab
lis
h
in
g
a
d
atab
ase
to
s
to
r
e
p
ac
k
et
d
etails
an
d
d
ev
elo
p
i
n
g
a
we
b
ap
p
licatio
n
f
o
r
in
tr
u
s
io
n
d
et
ec
tio
n
an
aly
s
is
an
d
v
is
u
aliza
tio
n
.
3.
SYST
E
M
ARCH
I
T
E
CT
U
R
E
T
h
er
e
ca
n
b
e
m
an
y
u
s
er
s
in
a
n
etwo
r
k
s
y
s
tem
th
at
ar
e
c
o
n
n
e
cted
v
ia
r
o
u
ter
s
to
s
er
v
er
s
.
W
h
en
an
en
d
u
s
er
’
s
d
ev
ice
tr
an
s
m
its
a
p
ac
k
et,
it
is
p
r
o
ce
s
s
ed
as
well
a
s
s
ec
u
r
ity
ch
ec
k
ed
b
y
th
e
d
ef
au
lt
p
r
e
-
c
o
n
f
i
g
u
r
ed
r
o
u
ter
f
ir
ewa
ll
s
y
s
tem
a
n
d
th
e
n
p
ass
ed
t
h
r
o
u
g
h
th
e
s
er
v
er
’
s
d
ef
a
u
lt
g
atew
ay
to
war
d
s
th
e
d
esti
n
atio
n
d
e
v
ice.
Un
f
o
r
tu
n
atel
y
,
cy
b
e
r
cr
im
in
als
h
av
e
b
ec
o
m
e
s
o
ad
v
an
ce
d
t
h
at
th
ey
ca
n
ea
s
ily
b
r
ea
k
th
r
o
u
g
h
th
at
d
ef
au
lt
s
ec
u
r
ity
s
y
s
tem
an
d
co
m
m
it
cr
im
es
with
o
u
t
leav
in
g
a
tr
ac
e.
T
h
er
ef
o
r
e,
a
m
o
r
e
s
ec
u
r
e
i
n
tellig
en
t
s
ec
u
r
ity
ap
p
r
o
ac
h
is
n
ee
d
ed
,
an
d
o
u
r
I
DPS is
a
s
u
itab
le
to
o
l.
I
DPS
is
tr
ain
ed
u
s
in
g
th
e
K
-
NN
ML
Alg
o
r
ith
m
t
h
at
ch
ec
k
s
v
ar
io
u
s
p
a
r
am
eter
s
o
f
a
p
ac
k
et
p
ass
in
g
th
r
o
u
g
h
th
e
s
er
v
er
,
an
d
it
tr
ies
to
ca
teg
o
r
ize
th
e
p
ac
k
et
in
to
eith
er
n
o
r
m
al
o
r
n
o
n
-
n
o
r
m
al
p
ac
k
ets.
No
r
m
al
p
ac
k
ets
ar
e
p
ass
ed
,
wh
ile
s
u
s
p
icio
u
s
o
n
es
tr
ig
g
er
I
P
b
lo
ck
i
n
g
.
A
d
y
n
a
m
ic
d
atab
ase
s
to
r
es
s
u
s
p
icio
u
s
p
ac
k
et
d
ata,
wh
ich
is
th
en
u
s
ed
to
g
en
er
ate
a
co
m
m
a
n
d
l
o
g
f
o
r
b
lo
ck
in
g
th
o
s
e
I
Ps
in
th
e
r
o
u
t
er
’
s
f
ir
ewa
ll.
T
h
is
p
r
o
ce
s
s
r
ep
ea
ts
,
u
p
d
atin
g
th
e
d
atab
ase
with
s
u
s
p
icio
u
s
n
ew
d
ata.
I
n
Fig
u
r
e
1
,
we
s
ee
th
at
o
u
r
I
DPS
s
y
s
tem
is
in
teg
r
ated
in
to
a
n
etwo
r
k
v
ia
a
p
r
im
ar
y
r
o
u
ter
.
T
h
e
I
DS
an
aly
ze
s
p
ac
k
et
tr
af
f
ic,
allo
win
g
n
o
r
m
al
p
ac
k
ets
to
p
ass
an
d
is
o
latin
g
s
u
s
p
icio
u
s
o
n
es.
Su
s
p
icio
u
s
p
ac
k
et
d
ata
is
s
to
r
ed
in
a
tem
p
o
r
ar
y
d
ata
b
ase,
wh
ich
is
th
en
u
s
ed
to
g
en
er
ate
a
co
m
m
an
d
lo
g
f
o
r
b
lo
c
k
in
g
m
alicio
u
s
i
n
ter
n
et
p
r
o
to
c
o
l
(
I
P)
ad
d
r
ess
es.
Simu
ltan
eo
u
s
ly
,
au
th
o
r
ized
p
er
s
o
n
n
el
ar
e
aler
t
ed
,
en
a
b
lin
g
r
ap
id
I
P
b
l
o
ck
in
g
v
ia
th
e
co
m
m
a
n
d
lo
g
,
en
s
u
r
in
g
n
etwo
r
k
s
ec
u
r
ity
an
d
f
ac
ilit
atin
g
f
o
r
en
s
ic
an
aly
s
is
.
Fig
u
r
e
1
.
Sy
s
tem
ar
c
h
itectu
r
e
4.
M
E
T
H
O
DO
L
O
G
Y
T
h
is
s
ec
tio
n
d
escr
ib
es
th
e
p
r
o
p
o
s
ed
in
tr
u
s
io
n
d
etec
tio
n
an
d
p
r
o
tectio
n
s
y
s
tem
b
ased
o
n
a
r
ea
l
-
tim
e,
m
ac
h
in
e
lear
n
i
n
g
-
d
r
iv
en
ap
p
r
o
ac
h
.
T
h
e
p
h
ases
o
f
th
e
m
et
h
o
d
o
lo
g
y
a
r
e
v
is
u
alize
d
in
F
ig
u
r
e
2
.
R
ea
l
-
tim
e
n
etwo
r
k
tr
af
f
ic
d
ata
is
co
llected
u
s
in
g
wir
esh
ar
k
,
f
o
llo
we
d
b
y
r
ig
o
r
o
u
s
clea
n
in
g
an
d
f
ea
tu
r
e
s
elec
tio
n
to
p
r
ep
ar
e
th
e
d
ata
f
o
r
th
e
d
ev
el
o
p
m
en
t
o
f
m
ac
h
in
e
lear
n
in
g
m
o
d
els.
T
h
e
tr
ain
ed
m
o
d
el
is
th
en
ev
alu
ated
u
s
in
g
W
ir
esh
ar
k
-
ca
p
tu
r
ed
d
ata
to
as
s
ess
its
ef
f
ec
tiv
en
ess
in
class
i
f
y
in
g
b
en
i
g
n
a
n
d
n
o
n
-
b
e
n
ig
n
tr
af
f
ic.
Fin
ally
,
th
e
d
ep
lo
y
e
d
m
o
d
el
p
r
ed
icts
th
e
n
atu
r
e
o
f
i
n
co
m
in
g
tr
af
f
ic
in
r
ea
l
-
tim
e,
en
ab
lin
g
t
h
e
id
en
tif
icatio
n
o
f
p
o
te
n
tial
in
tr
u
s
io
n
s
an
d
th
e
im
p
lem
en
ta
tio
n
o
f
a
p
p
r
o
p
r
iate
s
ec
u
r
ity
m
ea
s
u
r
es.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
1
6
,
No
.
1
,
Feb
r
u
ar
y
20
2
6
:
4
3
7
-
449
440
Fig
u
r
e
2
.
Ph
ases
o
f
th
e
m
eth
o
d
o
lo
g
y
4
.
1
.
P
ha
s
es o
f
m
et
ho
do
lo
g
y
4
.
1
.
1.
Da
t
a
co
llect
io
n pro
ce
s
s
T
o
en
s
u
r
e
m
o
d
el
r
o
b
u
s
tn
ess
an
d
f
air
n
ess
,
we
h
av
e
p
er
f
o
r
m
ed
co
m
p
r
eh
en
s
iv
e
d
ata
p
r
e
p
r
o
ce
s
s
in
g
,
in
clu
d
in
g
clea
n
in
g
,
b
alan
cin
g
,
an
d
s
p
litt
in
g
th
e
d
ataset.
T
h
e
p
u
b
licly
av
ailab
le
C
I
C
I
DS2
0
1
7
d
ataset
(
7
9
f
ea
tu
r
es)
f
r
o
m
C
I
C
I
DS
[
1
1
]
is
ch
o
s
en
f
o
r
its
r
ec
e
n
t
n
etw
o
r
k
tr
af
f
ic
d
ata
(
5
d
a
y
s
)
en
c
o
m
p
ass
in
g
d
iv
er
s
e
attac
k
s
,
in
clu
d
in
g
DDo
S
[
2
5
]
,
p
o
r
t
s
ca
n
[
2
6
]
,
B
o
tn
et
[
2
7
]
,
I
n
f
iltra
tio
n
[
2
8
]
,
web
attac
k
s
:
B
r
u
te
Fo
r
ce
Attack
,
XSS
attac
k
[
2
9
]
an
d
SQL
in
je
ctio
n
attac
k
[
3
0
]
.
Fig
u
r
e
3
d
es
cr
ib
es
th
e
f
iles
co
n
tain
ed
with
in
th
e
C
I
C
I
DS2
0
1
7
d
ataset.
T
h
is
f
ig
u
r
e
o
f
f
er
s
a
co
m
p
r
e
h
en
s
iv
e
o
v
er
v
iew
o
f
th
e
d
ata
s
tr
u
ctu
r
e
a
n
d
o
r
g
a
n
izatio
n
,
en
h
an
cin
g
u
n
d
er
s
tan
d
i
n
g
o
f
th
e
d
ataset
’
s
co
n
ten
ts
.
Fig
u
r
e
3
.
Descr
ip
tio
n
o
f
f
iles
co
n
tain
in
g
t
h
e
C
I
C
I
DS 2
0
1
7
d
a
taset
4.
1.
2.
Da
t
a
p
re
pro
ce
s
s
ing
T
h
e
r
aw
d
ata
is
th
o
r
o
u
g
h
l
y
cl
ea
n
e
d
,
ad
d
r
ess
i
n
g
t
h
e
m
is
s
i
n
g
v
al
u
es
b
y
im
p
u
t
ati
o
n
an
d
d
i
v
id
i
n
g
t
h
e
m
in
t
o
tr
ai
n
i
n
g
a
n
d
tes
ti
n
g
s
ets
f
o
r
t
h
e
m
o
d
el
ev
al
u
a
ti
o
n
i
n
an
u
n
b
i
ase
d
w
ay
.
T
o
a
d
d
r
e
s
s
class
im
b
ala
n
c
e,
s
y
n
th
eti
c
m
i
n
o
r
it
y
o
v
e
r
s
a
m
p
li
n
g
t
ec
h
n
i
q
u
e
(
S
MO
T
E
)
is
u
s
e
d
to
g
e
n
e
r
ate
s
y
n
th
eti
c
d
ata
p
o
in
ts
f
o
r
t
h
e
m
in
o
r
it
y
class
,
en
s
u
r
i
n
g
a
b
al
an
ce
d
d
a
taset
f
o
r
ef
f
e
cti
v
e
m
ac
h
i
n
e
l
e
ar
n
i
n
g
m
o
d
el
t
r
ai
n
i
n
g
.
T
h
is
p
r
e
p
r
o
c
ess
i
n
g
s
t
ep
s
ig
n
i
f
ic
a
n
tl
y
im
p
r
o
v
es t
h
e
r
e
li
ab
i
lit
y
o
f
t
h
e
m
o
d
el
b
y
r
e
d
u
ci
n
g
b
i
as
in
tr
o
d
u
ce
d
b
y
u
n
ev
e
n
cl
ass
d
is
t
r
i
b
u
ti
o
n
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
I
DP
S
:
A
ma
ch
in
e
lea
r
n
in
g
fr
a
mewo
r
k
fo
r
r
ea
l
-
time
in
tr
u
s
i
o
n
d
etec
tio
n
a
n
d
…
(
R
a
is
a
F
a
b
ih
a
)
441
4.
1.
3.
F
ea
t
ure
s
elec
t
io
n
Featu
r
e
s
elec
tio
n
p
lay
s
a
cr
u
ci
al
r
o
le
in
im
p
r
o
v
in
g
m
o
d
el
ef
f
icien
cy
an
d
ac
cu
r
ac
y
b
y
id
en
ti
f
y
in
g
th
e
m
o
s
t
r
elev
an
t
n
etwo
r
k
p
ar
a
m
eter
s
f
o
r
class
if
icatio
n
.
Af
ter
d
ata
p
r
e
-
p
r
o
ce
s
s
in
g
,
a
cr
u
cial
s
tep
in
v
o
lv
es
s
elec
tin
g
th
e
m
o
s
t
in
f
o
r
m
ativ
e
f
ea
tu
r
es
f
o
r
in
tr
u
s
io
n
d
etec
t
io
n
.
R
ath
er
th
an
r
an
d
o
m
ly
ch
o
o
s
in
g
f
ea
tu
r
es,
th
e
s
elec
tio
n
f
o
cu
s
es
o
n
th
o
s
e
with
clea
r
s
ig
n
if
ican
ce
to
id
en
tify
n
etwo
r
k
th
r
ea
ts
.
Ke
y
f
ea
tu
r
es
s
u
ch
as
‘
Destin
atio
n
Po
r
t
’
(
d
is
tin
g
u
is
h
in
g
n
etwo
r
k
s
er
v
ices)
,
T
C
P
f
lag
s
(
in
s
ig
h
ts
in
to
co
n
n
ec
tio
n
an
d
d
ata
f
lo
w)
,
an
d
‘
C
o
n
g
esti
o
n
W
in
d
o
w
R
ed
u
c
ed
’
an
d
‘
E
C
N
-
E
ch
o
’
(
in
d
ica
to
r
s
o
f
co
n
g
esti
o
n
c
o
n
tr
o
l
a
n
d
n
etwo
r
k
h
ea
lth
)
en
h
an
ce
s
th
e
ab
ilit
y
o
f
th
e
m
o
d
el
to
d
if
f
er
e
n
tiate
n
o
r
m
al
tr
af
f
ic
f
r
o
m
in
tr
u
s
io
n
s
.
As
d
etailed
in
T
ab
le
1
,
th
is
tar
g
eted
s
elec
tio
n
en
s
u
r
es th
at
th
e
m
o
d
el
lear
n
s
r
elev
an
t
p
atter
n
s
f
o
r
ac
c
u
r
ate
d
etec
tio
n
.
T
ab
le
1
.
L
is
t o
f
s
elec
ted
f
ea
t
u
r
es
No
N
o
f
e
a
t
u
r
e
n
a
m
e
1
D
e
st
i
n
a
t
i
o
n
P
o
r
t
2
F
i
n
3
S
y
n
4
R
e
se
t
5
P
u
sh
6
A
c
k
n
o
w
l
e
d
g
e
7
U
r
g
e
n
t
8
C
o
n
g
e
st
i
o
n
W
i
n
d
o
w
R
e
d
u
c
e
d
9
EC
N
-
Ec
h
o
4.
1.
4.
M
a
chine
lea
rning
m
o
del dev
elo
pm
ent
T
o
id
en
tif
y
th
e
b
est
-
p
er
f
o
r
m
i
n
g
m
o
d
el
f
o
r
o
u
r
i
n
tr
u
s
io
n
d
e
tectio
n
task
,
we
h
a
v
e
ev
al
u
ated
m
u
ltip
le
class
if
ier
s
b
a
s
ed
o
n
s
tan
d
ar
d
p
er
f
o
r
m
a
n
ce
m
etr
ics.
E
s
tab
lis
h
ed
class
if
icatio
n
alg
o
r
ith
m
s
(
Gau
s
s
ian
n
aïv
e
B
ay
es
(
GNB)
,
d
ec
is
io
n
t
r
ee
(
DT
)
,
r
an
d
o
m
f
o
r
est
(
R
F)
,
l
o
g
is
tic
r
eg
r
ess
io
n
(
L
R
)
,
g
r
ad
ien
t
b
o
o
s
tin
g
,
an
d
K
-
n
ea
r
est
n
eig
h
b
o
rs
(K
-
NN)
)
ar
e
s
elec
ted
f
o
r
th
eir
ef
f
ec
tiv
en
ess
in
h
an
d
lin
g
co
m
p
lex
n
etwo
r
k
tr
af
f
ic.
T
h
e
p
r
ep
r
o
ce
s
s
ed
d
ata
is
s
p
lit
f
o
r
tr
ain
in
g
an
d
test
in
g
,
allo
win
g
ea
ch
alg
o
r
ith
m
to
o
p
tim
ize
its
p
ar
am
eter
s
f
o
r
ac
cu
r
ate
in
tr
u
s
io
n
d
etec
tio
n
.
Per
f
o
r
m
an
ce
is
ev
alu
ated
u
s
in
g
ac
cu
r
ac
y
,
p
r
ec
is
io
n
,
r
ec
all,
an
d
F1
-
s
co
r
e.
Am
o
n
g
th
e
m
o
d
els
ev
alu
ated
,
th
e
K
-
NN
ac
h
iev
es
th
e
h
ig
h
e
s
t
p
r
ec
is
io
n
(
8
1
%),
as
s
h
o
wn
i
n
Fig
u
r
e
4
,
m
ak
in
g
it
th
e
o
p
tim
al
c
h
o
ice
f
o
r
i
n
tr
u
s
io
n
d
etec
tio
n
.
R
ea
l
-
tim
e
d
e
tectio
n
ca
p
ab
ilit
ies
o
f
t
h
e
f
r
a
m
ewo
r
k
a
r
e
f
u
r
th
er
ass
es
s
ed
u
s
in
g
estab
lis
h
ed
m
etr
ics,
wh
ich
d
em
o
n
s
tr
ate
s
u
p
er
i
o
r
ac
cu
r
ac
y
o
v
er
ex
is
tin
g
a
p
p
r
o
ac
h
es.
Fig
u
r
e
4
.
C
o
m
p
a
r
is
o
n
o
f
ac
cu
r
ac
y
an
d
e
r
r
o
r
r
ate
b
etwe
en
al
g
o
r
ith
m
s
I
n
co
r
p
o
r
atin
g
K
-
NN
,
th
e
p
r
o
p
o
s
ed
f
r
am
ewo
r
k
is
r
ig
o
r
o
u
s
ly
ev
alu
ated
f
o
r
r
ea
l
-
tim
e
m
alicio
u
s
ac
tiv
ity
d
etec
tio
n
u
s
in
g
estab
lis
h
ed
m
etr
ics
(
p
r
ec
is
io
n
,
r
ec
all,
F1
-
s
co
r
e,
ac
cu
r
ac
y
)
.
Pr
o
m
is
in
g
r
esu
lts
in
d
icat
e
s
u
p
er
io
r
ac
cu
r
ac
y
co
m
p
ar
e
d
to
ex
is
tin
g
ap
p
r
o
ac
h
es
f
o
r
p
r
o
tectin
g
o
n
lin
e
en
v
ir
o
n
m
en
ts
.
W
e
p
r
esen
t
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
1
6
,
No
.
1
,
Feb
r
u
ar
y
20
2
6
:
4
3
7
-
449
442
co
m
p
ar
is
o
n
s
o
f
p
r
ec
is
io
n
,
r
e
ca
ll,
an
d
F1
-
s
co
r
e
f
o
r
ea
c
h
alg
o
r
ith
m
to
an
aly
ze
f
r
am
ew
o
r
k
p
er
f
o
r
m
a
n
ce
in
d
if
f
er
en
t
m
etr
ics.
Pre
cisi
o
n
e
v
alu
ates
th
e
ac
cu
r
ac
y
o
f
in
tr
u
s
io
n
d
etec
tio
n
s
,
r
ec
all
m
ea
s
u
r
es
h
o
w
well
ac
tu
al
in
tr
u
s
io
n
s
ar
e
id
en
tifie
d
,
an
d
F1
-
s
co
r
e
b
alan
ce
s
b
o
th
.
As
s
h
o
wn
in
Fig
u
r
e
5
,
d
if
f
e
r
en
t
a
lg
o
r
ith
m
s
ex
ce
l
in
s
p
ec
if
ic
s
ce
n
ar
io
s
:
n
aiv
e
B
ay
e
s
p
er
f
o
r
m
s
well
f
o
r
b
e
n
ig
n
tr
a
f
f
ic
(
p
r
ec
is
io
n
)
in
Fig
u
r
e
5
(
a)
,
in
f
iltra
tio
n
(
r
ec
all)
in
Fig
u
r
e
5
(
b
)
an
d
Po
r
tScan
(
F1
-
s
co
r
e
)
in
Fig
u
r
e
5
(
c)
,
wh
ile
K
-
NN
s
tr
u
g
g
les
with
B
o
t
attac
k
s
.
T
h
ese
v
ar
iatio
n
s
h
ig
h
lig
h
t th
e
tr
a
d
e
-
o
f
f
s
in
alg
o
r
ith
m
s
elec
tio
n
f
o
r
in
tr
u
s
io
n
d
etec
tio
n
.
(
a)
(
b
)
(
c)
Fig
u
r
e
5
.
C
o
m
p
a
r
is
o
n
o
f
m
ac
h
in
e
lear
n
in
g
al
g
o
r
ith
m
s
b
ased
o
n
(
a)
p
r
ec
is
io
n
,
(
b
)
r
ec
all
,
an
d
(
c)
F1
-
s
co
r
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
I
DP
S
:
A
ma
ch
in
e
lea
r
n
in
g
fr
a
mewo
r
k
fo
r
r
ea
l
-
time
in
tr
u
s
i
o
n
d
etec
tio
n
a
n
d
…
(
R
a
is
a
F
a
b
ih
a
)
443
4.
1.
5
.
T
esting
us
ing
t
he
wire
s
ha
rk
P
CAP
f
ile
W
ir
esh
ar
k
,
a
p
o
wer
f
u
l
n
etwo
r
k
an
aly
ze
r
[
3
1
]
,
is
u
s
ed
to
ca
p
tu
r
e
o
v
er
1
0
,
0
0
0
d
ata
p
o
in
ts
in
7
h
o
u
r
s
f
o
r
r
ea
l
-
tim
e
tr
af
f
ic
a
n
aly
s
is
.
T
h
e
d
ata
ar
e
p
r
e
-
p
r
o
ce
s
s
ed
,
in
clu
d
in
g
f
ea
tu
r
e
s
elec
tio
n
,
im
p
u
tatio
n
,
an
d
s
ca
lin
g
,
to
o
p
tim
ize
th
em
f
o
r
m
ac
h
in
e
lear
n
in
g
.
K
-
NN
is
ch
o
s
en
f
o
r
its
s
u
p
er
io
r
ac
cu
r
ac
y
in
in
tr
u
s
io
n
d
etec
tio
n
,
an
d
its
p
er
f
o
r
m
an
ce
is
ev
alu
ated
to
v
alid
ate
its
ef
f
ec
tiv
en
ess
i
n
d
is
tin
g
u
is
h
in
g
b
en
ig
n
f
r
o
m
m
alicio
u
s
tr
af
f
ic,
en
h
an
cin
g
n
etwo
r
k
s
ec
u
r
ity
.
5.
WE
B
AP
P
L
I
CAT
I
O
N
AND
P
RO
T
O
T
YP
E
DE
V
E
L
O
P
M
E
NT
Af
ter
v
alid
atin
g
th
e
ef
f
ec
tiv
e
n
ess
o
f
th
e
m
ac
h
in
e
lear
n
i
n
g
m
o
d
el,
we
h
av
e
f
o
c
u
s
ed
o
n
in
te
g
r
atin
g
it
in
to
a
d
ep
lo
y
ab
le
s
y
s
tem
f
o
r
p
r
ac
tical
u
s
e.
W
e
h
av
e
ch
o
s
en
a
web
ap
p
licatio
n
f
o
r
its
ac
ce
s
s
ib
ilit
y
an
d
cr
o
s
s
-
p
latf
o
r
m
co
m
p
atib
ilit
y
,
en
ab
li
n
g
s
ea
m
less
cy
b
er
s
ec
u
r
ity
d
at
a
an
aly
s
is
f
o
r
all
u
s
er
s
.
W
ith
in
tu
itiv
e
to
o
ls
an
d
v
is
u
aliza
tio
n
s
,
it
s
im
p
lifie
s
co
m
p
lex
i
n
s
ig
h
ts
,
em
p
o
we
r
in
g
n
o
n
-
tech
n
ical
u
s
er
s
to
ev
alu
ate
s
ec
u
r
ity
p
o
s
tu
r
e
an
d
m
ak
e
in
f
o
r
m
ed
d
ec
is
io
n
s
in
Fig
u
r
e
6
.
Ou
r
in
f
r
astru
ctu
r
e
f
ea
tu
r
es
a
well
-
d
esig
n
ed
XAM
PP
d
atab
ase
th
at
s
to
r
es
cr
itical
s
ec
u
r
ity
d
ata
,
in
clu
d
in
g
d
etec
ted
th
r
ea
ts
a
n
d
I
P
ad
d
r
ess
es,
en
s
u
r
in
g
e
f
f
icien
t
d
ata
m
an
a
g
em
en
t
f
o
r
p
r
o
ac
tiv
e
t
h
r
ea
t
m
itig
atio
n
.
A
p
r
o
to
ty
p
e
b
lo
ck
er
s
y
s
tem
,
im
p
lem
en
ted
u
s
in
g
C
is
co
Pack
et
T
r
ac
er
in
Fig
u
r
e
7
,
b
lo
ck
s
m
alicio
u
s
I
Ps
id
en
tifie
d
b
y
th
e
I
DPS
s
y
s
tem
.
T
h
is
in
teg
r
ated
f
r
am
ewo
r
k
en
h
an
ce
s
cy
b
er
s
ec
u
r
ity
b
y
en
ab
lin
g
r
ea
l
-
tim
e
th
r
ea
t d
etec
tio
n
an
d
r
esp
o
n
s
e,
s
af
eg
u
ar
d
in
g
d
ig
ital
ass
ets.
Fig
u
r
e
6
.
No
n
-
b
e
n
ig
n
p
r
ed
icti
o
n
in
a
ta
b
le
o
n
we
b
s
ite
Fig
u
r
e
7
.
B
lo
ck
er
s
y
s
tem
p
r
o
t
o
ty
p
e
in
C
I
SC
O
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
1
6
,
No
.
1
,
Feb
r
u
ar
y
20
2
6
:
4
3
7
-
449
444
6.
P
E
RF
O
RM
A
NCE A
ND
RE
SUL
T
ANA
L
YS
I
S
K
-
n
ea
r
est
n
eig
h
b
o
r
s
(
KNN)
,
ch
o
s
en
f
o
r
its
h
ig
h
ac
cu
r
ac
y
(
8
1
%),
p
o
wer
s
o
u
r
web
-
b
ase
d
in
tr
u
s
io
n
d
etec
tio
n
s
y
s
tem
in
r
ea
l
tim
e.
I
t
in
teg
r
ates
with
a
s
ec
u
r
e
d
atab
ase
to
lo
g
g
e
d
th
r
ea
ts
an
d
b
lo
ck
m
alicio
u
s
I
P
ad
d
r
ess
es p
r
o
ac
tiv
ely
,
wh
ile
al
s
o
p
r
o
v
id
i
n
g
clea
r
v
is
u
aliza
tio
n
s
f
o
r
s
ec
u
r
ity
p
r
o
f
ess
io
n
als to
tak
e
ac
tio
n
.
6
.
1
.
Sy
s
t
e
m
f
ea
t
ures
Ou
r
f
ea
tu
r
e
s
elec
tio
n
p
r
o
ce
s
s
aim
s
to
id
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.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
1
6
,
No
.
1
,
Feb
r
u
ar
y
20
2
6
:
4
3
7
-
449
446
ACK
NO
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UNDING
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AUTHO
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R
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Stein
J
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Z
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A.
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C
:
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h
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s
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tate
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co
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lict o
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in
ter
est.
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NF
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NS
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h
is
s
tu
d
y
d
id
n
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v
o
lv
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u
m
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p
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ticip
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ts
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p
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s
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n
al
d
at
a,
o
r
id
en
tifia
b
le
in
f
o
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m
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.
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h
er
ef
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r
e,
in
f
o
r
m
e
d
co
n
s
en
t w
as n
o
t
r
eq
u
ir
ed
f
o
r
th
is
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esear
ch
.
E
T
H
I
CAL AP
P
RO
V
AL
T
h
is
s
tu
d
y
d
id
n
o
t
in
v
o
lv
e
h
u
m
an
p
ar
ticip
a
n
ts
,
an
im
als,
o
r
th
e
u
s
e
o
f
p
er
s
o
n
ally
id
en
ti
f
iab
le
d
ata.
T
h
er
ef
o
r
e,
eth
ical
ap
p
r
o
v
al
f
r
o
m
an
in
s
titu
tio
n
al
r
ev
iew
b
o
ar
d
o
r
eth
ics co
m
m
ittee
was n
o
t
r
eq
u
ir
ed
.
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