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Distrib
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ll
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tt
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e
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to
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c
o
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d
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g
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a
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u
sta
r
Re
se
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rc
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Ag
e
n
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e
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c
k
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re
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e
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o
m
in
g
m
o
re
p
o
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rfu
l
a
n
d
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o
m
m
o
n
.
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o
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g
m
a
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y
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th
e
r
issu
e
s,
d
istri
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te
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d
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m
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se
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ir
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ies
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re
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it
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e
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n
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c
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ll
y
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e
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,
m
a
k
in
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d
e
v
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d
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m
a
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o
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g
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n
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t
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su
lt
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g
i
n
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larg
e
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sc
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le
a
tt
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c
k
p
o
we
r.
T
h
e
a
tt
a
c
k
e
rs
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se
th
e
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i
n
telli
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t
o
d
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th
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ted
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n
d
m
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n
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g
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re
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tely
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rk
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se
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terv
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TIB
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se
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h
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h
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g
t
h
e
se
c
u
rit
y
lev
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ls
o
f
th
e
n
e
two
r
k
.
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h
e
p
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o
p
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se
d
m
o
d
e
l
is
c
o
m
p
a
re
d
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ly
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re
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ts
th
e
p
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d
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l
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a
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in
g
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tt
e
r
o
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tco
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s.
K
ey
w
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d
s
:
Dis
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ted
d
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Dis
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ted
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ef
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o
f
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er
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I
n
tr
u
s
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d
etec
tio
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Ma
ch
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e
lear
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No
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T
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ter
v
al
T
r
u
s
t f
ac
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s
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c
c
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rticle
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n
d
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r th
e
CC B
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se
.
C
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r
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s
p
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ing
A
uth
o
r
:
Ma
n
ju
J
ay
ak
u
m
a
r
R
ag
h
v
in
Sch
o
o
l o
f
E
lectr
o
n
ics
an
d
C
o
m
m
u
n
icatio
n
E
n
g
in
ee
r
in
g
,
R
e
v
a
Un
iv
er
s
ity
Yela
h
an
k
a,
Kar
n
ata
k
a,
I
n
d
ia
E
m
ail: m
an
ju
r
ag
h
v
in
@
g
m
ail.
co
m
1.
I
NT
RO
D
UCT
I
O
N
Desig
n
in
g
in
tr
u
s
io
n
d
etec
tio
n
s
y
s
tem
s
(
I
DS)
is
b
ec
o
m
i
n
g
in
cr
ea
s
in
g
ly
d
if
f
ic
u
lt
d
u
e
to
th
e
ev
e
r
-
ch
an
g
in
g
n
at
u
r
e
o
f
m
alicio
u
s
s
o
f
twar
e
(
m
alwa
r
e
)
.
Ma
l
war
e
au
th
o
r
s
u
s
e
v
a
r
io
u
s
e
v
asio
n
tactics
f
o
r
in
f
o
r
m
atio
n
co
n
ce
alm
en
t
to
a
v
o
id
d
etec
tio
n
b
y
an
I
DS,
m
a
k
in
g
th
e
id
en
tific
atio
n
o
f
u
n
k
n
o
wn
an
d
o
b
f
u
s
ca
ted
m
alwa
r
e
th
e
m
ajo
r
p
r
o
b
lem
in
to
d
ay
'
s
m
o
r
e
c
o
m
p
lex
m
al
icio
u
s
attac
k
s
.
Secu
r
ity
r
is
k
s
,
s
u
ch
as
ze
r
o
-
d
ay
attac
k
s
,
h
av
e
also
b
ee
n
o
n
t
h
e
r
is
e
an
d
ar
e
s
p
ec
if
ically
tar
g
e
tin
g
in
ter
n
et
u
s
er
s
.
Sin
ce
in
f
o
r
m
atio
n
tech
n
o
lo
g
y
is
n
o
w
in
teg
r
al
to
o
u
r
d
aily
liv
es,
co
m
p
u
ter
s
ec
u
r
ity
is
o
f
th
e
u
tm
o
s
t im
p
o
r
tan
ce
.
Mo
n
ito
r
in
g
n
etwo
r
k
p
er
f
o
r
m
a
n
ce
an
d
in
v
esti
g
atin
g
an
y
in
d
i
ca
tio
n
s
o
f
an
o
m
alies
o
v
er
th
e
n
etwo
r
k
is
th
e
p
r
im
ar
y
o
b
jectiv
e
o
f
an
I
DS.
I
n
tr
u
d
e
r
d
etec
tio
n
s
y
s
tem
s
h
av
e
r
ec
e
n
tly
b
eg
u
n
to
u
s
e
m
ac
h
in
e
lear
n
i
n
g
ap
p
r
o
ac
h
es
s
in
ce
th
ese
m
eth
o
d
s
h
av
e
s
h
o
wn
t
o
b
e
b
o
th
a
d
a
p
tab
le
an
d
ca
p
a
b
le
o
f
lear
n
in
g
,
wh
ich
allo
ws
f
o
r
a
f
aster
r
esp
o
n
s
e
tim
e.
I
n
t
h
is
p
ap
er
,
we
p
r
esen
t
a
m
o
d
el
f
o
r
d
etec
tin
g
an
d
class
if
y
in
g
in
tr
u
s
io
n
s
u
s
in
g
m
ac
h
in
e
lear
n
in
g
.
Dis
tr
ib
u
ted
d
en
ial
o
f
s
er
v
ice
is
a
m
ajo
r
th
r
ea
t
to
n
etwo
r
k
s
ec
u
r
ity
.
A
d
is
tr
ib
u
te
d
d
en
ial
o
f
s
er
v
ice
(
DDo
S)
attac
k
is
f
r
eq
u
e
n
tly
c
ar
r
ied
o
u
t
b
y
c
r
ea
tin
g
a
m
ass
iv
e
am
o
u
n
t
o
f
tr
a
f
f
ic
i
n
o
r
d
er
to
o
v
er
wh
elm
th
e
tar
g
et
s
y
s
tem
's r
eso
u
r
ce
s
[
1
]
.
T
h
is
attac
k
h
as c
au
s
ed
s
ig
n
if
ican
t d
am
ag
e
ac
r
o
s
s
th
e
I
n
ter
n
et
an
d
h
as r
esu
lted
in
m
ass
iv
e
f
in
an
cial
lo
s
s
.
T
o
p
r
ev
en
t
DDo
S
attac
k
s
,
r
ese
ar
ch
er
s
h
a
v
e
cr
ea
ted
a
n
u
m
b
er
o
f
d
etec
tio
n
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
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8
8
-
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8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
15
,
No
.
2
,
Ap
r
il
20
25
:
1
4
5
5
-
1
4
6
2
1456
tech
n
o
lo
g
ies
[
2
]
,
ea
ch
o
f
wh
i
ch
u
s
e
a
d
is
tin
ct
tech
n
o
lo
g
y
.
So
m
e
o
f
th
ese
s
y
s
tem
s
h
av
e
m
ad
e
u
s
e
o
f
d
ata
m
in
in
g
tech
n
i
q
u
es
[
3
]
,
s
u
ch
a
s
m
ac
h
in
e
lear
n
in
g
(
ML
)
ap
p
r
o
ac
h
es.
I
t
is
n
ev
er
t
h
eless
an
in
ter
esti
n
g
s
tu
d
y
to
s
u
g
g
est
m
o
r
e
ef
f
icien
t
d
etec
ti
o
n
s
y
s
tem
s
f
o
r
DDo
S
attac
k
s
[
4
]
.
R
esear
ch
er
s
ar
e
lo
o
k
i
n
g
f
o
r
lo
w
f
alse
alar
m
r
ates
as
well
as
a
h
ig
h
d
etec
tio
n
r
ate
[
5
]
.
A
d
etec
tio
n
en
g
in
e
m
u
s
t
b
e
ab
le
to
m
an
ag
e
a
lar
g
e
am
o
u
n
t
o
f
r
ea
l
-
tim
e
n
etwo
r
k
tr
af
f
ic.
T
h
is
s
u
g
g
ested
wo
r
k
p
r
o
v
id
es
a
n
o
v
el
an
d
m
o
r
e
e
f
f
icien
t
DDo
S
attac
k
d
etec
tio
n
s
y
s
tem
im
p
lem
en
tatio
n
tech
n
iq
u
e.
First,
a
tr
u
s
t
f
ac
to
r
v
alid
atio
n
m
o
d
el
is
cr
ea
ted
to
r
ed
u
ce
th
e
n
u
m
b
er
o
f
d
im
en
s
io
n
s
an
d
p
r
o
ce
s
s
in
g
n
e
ed
s
b
y
v
alid
atin
g
th
e
n
o
d
es,
a
n
d
th
en
a
m
ac
h
in
e
lear
n
in
g
a
p
p
r
o
ac
h
is
u
tili
ze
d
to
cr
ea
te
a
f
r
eq
u
en
t tim
e
in
ter
v
al
b
ased
b
alan
cin
g
m
o
d
u
le
[
6
]
.
T
h
e
s
ec
u
r
ity
o
f
I
DS
d
ep
e
n
d
s
o
n
th
e
n
ee
d
s
o
f
th
e
u
s
er
an
d
ca
n
b
e
im
p
lem
e
n
ted
eith
er
o
n
th
e
s
er
v
er
s
id
e
o
r
o
n
th
e
clien
t
s
id
e
[
7
]
.
Au
to
m
ated
d
ec
is
io
n
s
ar
e
m
ad
e
p
o
s
s
ib
le
b
y
co
m
b
in
i
n
g
I
DS
with
m
ac
h
in
e
lear
n
in
g
tec
h
n
iq
u
es
[
8
]
.
B
y
cl
ass
if
y
in
g
d
if
f
e
r
en
t
k
in
d
s
o
f
in
t
r
u
s
io
n
s
,
m
ac
h
i
n
e
lear
n
in
g
alg
o
r
ith
m
s
ca
n
p
r
o
ce
s
s
th
em
in
a
m
a
n
n
er
th
at
p
r
o
tects
th
e
n
etwo
r
k
'
s
in
teg
r
ity
,
co
n
f
id
en
tiality
,
an
d
s
ec
u
r
ity
[
9
]
.
An
o
th
e
r
m
is
co
n
ce
p
tio
n
is
th
at
d
is
tr
ib
u
ted
d
en
ial
o
f
s
er
v
ice
attac
k
s
ar
e
alwa
y
s
th
e
s
am
e
[
1
0
]
.
W
h
ile
s
o
m
e
D
Do
S
tech
n
iq
u
es
u
s
e
a
lo
t
o
f
r
eso
u
r
ce
s
,
o
th
er
s
u
s
e
v
er
y
litt
le.
T
h
e
r
ef
o
r
e,
t
h
er
e
m
a
y
b
e
c
o
u
n
tles
s
v
ar
ian
ts
o
f
DDo
S
attac
k
s
th
at
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
s
m
is
s
.
A
d
if
f
ic
u
lt
-
to
-
d
etec
t
ass
au
lt
s
tr
ateg
y
is
s
ig
n
atu
r
e
-
b
ased
lear
n
in
g
,
wh
ic
h
in
v
o
lv
es lea
r
n
in
g
to
r
ec
o
g
n
ize
n
ew
th
r
ea
ts
[
1
1
]
.
Sm
u
r
f
attac
k
s
,
u
s
er
d
atag
r
am
p
r
o
to
co
l
(
UDP)
f
lo
o
d
s
,
an
d
tr
an
s
m
is
s
io
n
co
n
tr
o
l p
r
o
to
co
l (
T
C
P)
f
lo
o
d
s
ar
e
o
n
l
y
a
f
ew
ex
am
p
les
o
f
th
e
m
an
y
ty
p
es
o
f
DDo
S
ass
au
lts
tar
g
etin
g
n
etwo
r
k
s
to
d
a
y
.
An
ass
au
lt
th
at
o
v
er
wh
elm
s
a
co
m
p
u
ter
n
etwo
r
k
b
y
s
en
d
in
g
m
ass
iv
e
am
o
u
n
ts
o
f
d
ata
to
th
e
tar
g
eted
s
y
s
tem
s
is
k
n
o
wn
as
a
UDP
o
r
T
C
P
f
lo
o
d
.
W
h
en
m
a
ch
in
es
g
et
p
in
g
r
e
q
u
ests
f
r
o
m
u
n
k
n
o
wn
s
o
u
r
ce
s
,
th
e
y
will
r
ea
ct.
T
h
e
liter
atu
r
e
d
ep
icts
r
ea
l
-
wo
r
ld
DDo
S
attac
k
s
itu
atio
n
s
u
s
in
g
b
en
ch
m
ar
k
d
atasets
[
1
2
]
.
Desp
ite
th
eir
in
itial
u
tili
ty
,
th
ese
d
atasets
ar
e
n
o
w
co
n
s
id
er
ed
o
u
td
ated
b
ec
a
u
s
e
attac
k
cr
iter
i
a
ar
e
co
n
s
tan
tly
ev
o
l
v
in
g
.
Ma
lwar
e
an
d
p
u
b
licly
av
ailab
le
to
o
ls
a
r
e
u
s
ed
b
y
at
tack
er
s
[
1
3
]
.
T
o
i
d
en
tify
DDo
S
attac
k
s
in
r
ea
l
-
tim
e,
m
o
r
e
r
ec
en
t
d
atasets
ar
e
r
eq
u
ir
ed
.
T
h
e
au
th
o
r
s
u
s
ed
th
e
C
I
C
DD
OS
2
0
1
9
d
ataset,
wh
ich
co
n
tain
s
a
wid
e
s
p
ec
tr
u
m
o
f
d
an
g
er
o
u
s
th
r
ea
ts
.
Attack
s
b
y
p
e
r
p
etr
ato
r
s
o
f
d
is
tr
ib
u
ted
d
en
ial
o
f
s
er
v
ice
h
av
e
r
eg
u
la
r
ly
b
ee
n
r
ec
o
g
n
ized
a
n
d
r
e
m
ed
ied
u
s
in
g
ML
ap
p
r
o
ac
h
es.
W
h
en
co
m
p
ar
ed
to
ML
alg
o
r
ith
m
s
,
tr
ad
it
io
n
al
DDo
S
attac
k
d
etec
tio
n
m
eth
o
d
s
ar
e
f
aster
,
m
o
r
e
ex
ac
t,
a
n
d
p
r
o
v
id
e
t
h
e
m
o
s
t
ac
cu
r
ate
r
esu
lts
[
1
4
]
.
D
Do
S
attac
k
s
ar
e
d
esig
n
ed
to
r
ed
u
ce
th
e
av
ailab
ilit
y
o
f
in
ter
n
et
s
er
v
ices
t
o
th
o
s
e
wh
o
ac
tu
ally
u
s
e
th
em
.
I
n
th
is
s
ce
n
ar
io
,
th
e
attac
k
er
i
n
s
talls
m
alwa
r
e
o
n
co
m
p
u
ter
s
v
ia
th
e
in
ter
n
et
w
ith
o
u
t
th
e
co
m
p
u
ter
u
s
er
'
s
o
r
o
wn
er
'
s
k
n
o
wled
g
e
o
r
c
o
n
s
e
n
t
wh
en
th
ey
v
is
it
m
alicio
u
s
web
s
ites
[
1
5
]
.
C
o
m
p
u
ter
s
th
at
ar
e
k
n
o
wn
as
b
o
t
m
ac
h
in
es
ar
e
ty
p
ically
c
o
m
p
r
o
m
is
ed
d
u
e
to
m
alwa
r
e.
T
h
e
attac
k
er
g
ets
m
alwa
r
e
o
n
t
o
s
ev
er
al
co
m
p
u
t
er
s
ac
r
o
s
s
m
u
ltip
le
p
lace
s
u
s
i
n
g
th
e
in
ter
n
et
as
a
m
ed
iu
m
to
b
u
ild
a
b
o
tn
et
[
1
6
]
.
I
n
o
r
d
e
r
to
id
en
tify
s
u
s
p
icio
u
s
b
eh
av
io
r
o
n
a
p
ar
ticu
lar
n
etwo
r
k
s
eg
m
en
t
o
r
d
e
v
ice
,
n
etwo
r
k
in
tr
u
s
io
n
d
etec
tio
n
s
y
s
tem
(
NI
DS)
an
aly
ze
s
p
ac
k
ets
s
en
t
o
v
er
th
e
n
etwo
r
k
[
1
7
]
.
I
n
o
r
d
er
t
o
id
en
tif
y
in
tr
u
s
io
n
s
,
h
o
s
t
in
tr
u
s
io
n
d
ete
ctio
n
s
y
s
tem
(
HI
DS)
watc
h
es
th
e
h
o
s
t'
s
b
eh
av
io
r
.
Als
o
,
th
er
e
ar
e
u
s
u
ally
th
r
ee
way
s
to
ca
teg
o
r
ize
an
I
DS:
b
y
s
ig
n
atu
r
e,
b
y
a
n
o
m
aly
,
o
r
b
y
h
y
b
r
id
d
etec
tio
n
[
1
8
]
.
W
e
f
in
d
p
atter
n
s
o
f
in
tr
u
s
io
n
s
th
at
h
ap
p
en
o
f
ten
a
n
d
u
tili
ze
th
em
to
f
o
r
etell
wh
en
th
ey
will
h
ap
p
en
ag
ain
.
A
h
y
b
r
id
ap
p
r
o
ac
h
o
f
an
o
m
aly
d
etec
tio
n
is
th
e
th
ir
d
ty
p
e
o
f
I
DS.
I
t
co
m
b
in
es
two
ex
is
tin
g
m
eth
o
d
s
o
f
d
etec
tio
n
in
o
r
d
e
r
to
e
n
h
an
ce
th
eir
ca
p
a
b
ilit
ies.
C
o
m
b
in
in
g
th
e
a
n
o
m
alo
u
s
ap
p
r
o
ac
h
w
ith
th
e
k
n
o
wn
m
is
u
s
e
m
eth
o
d
allo
ws
f
o
r
th
e
d
etec
tio
n
o
f
u
n
ex
p
ec
ted
attac
k
s
.
T
h
e
s
y
s
tem
'
s
o
v
er
all
p
er
f
o
r
m
an
ce
will b
e
en
h
a
n
ce
d
[
1
9
]
.
T
h
e
p
r
im
ar
y
o
b
jectiv
e
o
f
th
is
s
tu
d
y
is
to
d
esig
n
an
an
o
m
al
y
-
b
ased
s
y
s
tem
f
o
r
d
etec
tin
g
d
is
tr
ib
u
ted
d
en
ial
-
of
-
s
er
v
ice
attac
k
s
o
n
n
etwo
r
k
s
.
T
h
e
in
itial
s
tag
e
in
c
r
ea
tin
g
a
s
u
cc
ess
f
u
l
DDo
S
d
et
ec
tio
n
s
y
s
tem
is
to
g
ain
k
n
o
wled
g
e
o
f
th
e
tech
n
o
lo
g
ies
th
at
ar
e
alr
ea
d
y
in
u
s
e.
T
h
er
e
ar
e
p
r
im
ar
ily
t
h
r
ee
tec
h
n
o
lo
g
ies
u
s
ed
b
y
in
tr
u
s
io
n
d
etec
tio
n
s
y
s
tem
s
:
n
etwo
r
k
a
n
o
m
aly
d
etec
tio
n
,
h
o
s
t
in
tr
u
s
io
n
d
etec
tio
n
,
an
d
n
etwo
r
k
in
tr
u
s
io
n
d
etec
tio
n
[
2
0
]
.
Ma
ch
in
e
lea
r
n
in
g
is
u
s
ed
as
a
n
in
itial
m
eth
o
d
d
u
r
in
g
test
in
g
an
d
lear
n
i
n
g
,
an
d
it
g
ets
b
etter
with
tim
e.
I
t e
s
tab
lis
h
es a
s
y
s
t
em
th
at
o
p
tim
izes
p
er
f
o
r
m
a
n
c
e
b
y
iter
ativ
ely
p
r
o
ce
s
s
in
g
f
ee
d
b
ac
k
d
ata
[
2
1
]
.
I
n
th
is
r
esear
ch
wo
r
k
,
th
e
p
r
o
b
lem
s
f
ac
ed
b
y
co
m
p
an
ies
f
r
o
m
wh
ich
DDo
S
attac
k
s
ca
n
o
r
ig
in
ate
is
co
n
s
id
er
ed
an
d
s
u
g
g
ests
a
n
ew
d
ef
en
s
iv
e
m
eth
o
d
to
h
el
p
co
u
n
ter
th
ese
p
r
o
b
lem
s
.
I
n
ad
d
itio
n
to
s
to
r
in
g
in
f
o
r
m
atio
n
,
th
e
s
u
g
g
ested
s
y
s
tem
f
u
n
cti
o
n
s
as
a
s
en
s
o
r
,
a
n
d
th
e
ac
q
u
ir
ed
d
ata
ca
n
b
e
u
s
ed
to
d
eter
m
in
e
h
o
w
o
n
lin
e
tr
af
f
ic
is
class
if
ied
an
d
th
e
in
f
er
en
ce
s
th
at
ar
e
m
ad
e
f
r
o
m
r
an
d
o
m
ized
tr
a
f
f
ic
s
am
p
les
co
llected
o
n
n
etwo
r
k
d
ev
ices
u
s
in
g
s
tr
ea
m
p
r
o
to
co
l
[
2
2
]
.
T
h
e
p
r
o
p
o
s
al
will
n
o
t
r
eq
u
ir
e
s
o
f
twar
e
o
r
h
ar
d
war
e
u
p
d
ates,
an
d
it
is
co
m
p
atib
le
with
th
e
cu
r
r
en
t
in
ter
n
et
in
f
r
astru
ctu
r
e.
I
t
is
a
g
iv
en
th
at
th
e
p
r
iv
ac
y
o
f
th
e
u
s
er
s
'
d
ata
is
u
p
h
eld
at
all
s
tag
es o
f
s
y
s
tem
f
u
n
ctio
n
in
g
.
2.
P
RO
P
O
SE
D
M
O
D
E
L
L
o
ad
f
o
r
ec
asti
n
g
is
ch
allen
g
in
g
s
in
ce
th
er
e
ar
e
s
o
m
an
y
p
o
s
s
ib
le
v
ar
iab
les.
A
s
u
b
s
tan
tial r
e
latio
n
s
h
ip
b
etwe
en
lo
ad
ch
an
g
e
an
d
th
ese
v
ar
iab
les
h
as
n
o
t
b
ee
n
f
o
u
n
d
y
et
b
ec
a
u
s
e
th
er
e
ar
e
s
o
m
an
y
p
o
s
s
ib
le
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
Tr
u
s
t fa
cto
r
va
lid
a
tio
n
fo
r
d
is
tr
ib
u
ted
d
en
ia
l
o
f ser
vice
a
tta
ck
d
etec
tio
n
…
(
Ma
n
j
u
Ja
ya
k
u
ma
r
R
a
g
h
vin
)
1457
in
f
lu
en
ce
s
[
2
3
]
.
E
v
e
n
co
llectin
g
th
e
n
ec
ess
ar
y
d
ata
was a
p
ain
u
n
til r
ec
en
tly
.
W
e
ca
n
n
o
w
r
ec
o
r
d
an
d
ev
alu
ate
an
y
r
ep
er
cu
s
s
io
n
s
o
n
a
lar
g
e
s
ca
le
th
an
k
s
to
n
ew
s
m
ar
t
m
eter
n
etwo
r
k
s
,
ef
f
icien
t
s
en
s
in
g
m
eth
o
d
s
,
an
d
in
ter
n
et
o
f
th
in
g
s
(
I
o
T
)
tech
n
o
lo
g
y
[
2
4
]
.
B
ec
au
s
e
th
ey
h
av
e
s
o
m
an
y
s
en
s
o
r
s
,
s
m
ar
t
m
eter
s
ca
n
g
ath
er
a
lo
t
o
f
d
ata
ab
o
u
t
th
eir
s
u
r
r
o
u
n
d
in
g
s
with
o
u
t
h
u
m
a
n
in
ter
v
e
n
tio
n
.
T
h
ey
ca
n
also
ac
ce
s
s
th
e
d
ata
th
at
o
th
er
I
o
T
d
ev
ices
h
av
e
s
h
ar
ed
[
2
5
]
.
T
h
e
p
r
im
ar
y
co
m
m
an
d
ce
n
ter
will
r
ec
eiv
e
all
o
f
th
e
d
ata
in
th
i
s
u
p
lo
ad
[
2
6
]
.
T
h
is
will
allo
w
f
o
r
th
e
co
llectio
n
o
f
m
ass
iv
e
am
o
u
n
ts
o
f
d
ata
f
o
r
f
u
tu
r
e
r
esear
ch
.
Usi
n
g
co
n
s
u
m
p
tio
n
p
atter
n
s
as
in
p
u
ts
,
th
is
s
tu
d
y
d
ev
elo
p
s
a
m
eth
o
d
f
o
r
lo
ad
b
alan
cin
g
o
n
a
s
in
g
le
d
is
tr
ib
u
tio
n
tr
an
s
f
o
r
m
er
,
n
o
d
e,
o
r
f
ee
d
er
.
T
h
e
id
ea
b
e
h
in
d
th
is
m
eth
o
d
is
th
at
d
if
f
er
en
t
p
e
o
p
le
co
n
s
u
m
e
p
o
wer
at
d
if
f
er
e
n
t
tim
es
an
d
h
a
v
e
d
if
f
e
r
en
t
elec
tr
ical
n
ee
d
s
[
2
2
]
.
T
h
is
tech
n
o
lo
g
y
ca
n
also
b
e
u
s
ed
to
d
is
p
er
s
e
th
e
lo
ad
m
o
r
e
ev
en
ly
o
n
a
d
is
tr
ib
u
tio
n
tr
an
s
f
o
r
m
er
.
T
h
er
ef
o
r
e,
s
m
ar
t
g
r
id
s
th
at
h
av
e
m
ea
s
u
r
em
en
t
in
f
r
astru
ctu
r
e
ar
e
id
ea
l
f
o
r
u
s
in
g
th
e
ap
p
r
o
ac
h
.
C
o
n
s
eq
u
en
tly
,
th
e
m
eth
o
d
wo
r
k
s
well
with
s
m
ar
t g
r
id
s
.
I
n
th
e
p
r
o
p
o
s
ed
wo
r
k
,
a
f
r
e
q
u
en
t
tim
e
in
ter
v
al
b
alan
cin
g
m
o
d
u
le
with
n
o
d
e
tr
u
s
t
f
ac
to
r
v
alid
atio
n
(
FTI
B
M
-
NT
FV)
m
o
d
el
is
u
s
ed
to
id
en
tify
th
e
DDo
S
attac
k
s
in
th
e
s
y
s
tem
.
T
h
e
p
r
o
p
o
s
ed
wo
r
k
b
eg
in
s
b
y
tak
in
g
n
etwo
r
k
tr
af
f
ic
i
n
to
ac
c
o
u
n
t
u
s
in
g
a
d
ataset
th
at
r
ec
o
r
d
s
d
etails
o
f
n
etwo
r
k
tr
a
f
f
ic.
T
h
is
d
ataset
is
th
en
u
s
ed
to
d
etec
t D
Do
S.
Fro
m
th
e
ex
tr
ac
ted
d
ataset
f
ea
tu
r
es,
a
f
ea
tu
r
e
s
elec
tio
n
m
o
d
el
ch
o
o
s
es th
e
m
o
s
t a
cc
u
r
ate
an
d
r
elev
a
n
t f
ea
tu
r
es f
o
r
attac
k
id
en
tific
atio
n
.
T
h
e
p
r
o
p
o
s
ed
m
o
d
el
ar
c
h
itectu
r
e
is
in
d
icate
d
in
Fig
u
r
e
1
.
Fig
u
r
e
1
.
Pro
p
o
s
ed
m
o
d
el
ar
c
h
itectu
r
e
Featu
r
e
ex
tr
ac
tio
n
is
th
e
f
ir
s
t
s
tep
in
ef
f
ec
tiv
e
i
n
tr
u
s
io
n
d
etec
tio
n
.
I
t
in
v
o
lv
es
s
el
ec
tin
g
an
d
id
en
tify
in
g
s
ig
n
i
f
ican
t
q
u
aliti
es
o
r
ch
ar
ac
ter
is
tics
f
r
o
m
th
e
in
f
o
r
m
atio
n
.
I
n
o
r
d
e
r
to
m
a
k
e
s
u
r
e
th
e
lo
g
is
tic
r
eg
r
ess
io
n
m
o
d
el
ca
n
h
a
n
d
le
th
e
f
ea
tu
r
es
co
n
s
is
ten
tly
;
d
ata
p
r
ep
ar
atio
n
co
u
l
d
in
clu
d
e
s
tan
d
ar
d
izin
g
in
p
u
t
v
alu
es.
I
m
p
o
r
tan
t
f
ea
tu
r
es
with
wid
er
r
an
g
es
ar
e
f
ilter
ed
o
u
t
o
f
th
e
lear
n
in
g
p
r
o
ce
s
s
at
th
is
s
tag
e,
allo
win
g
th
e
m
o
d
el
to
f
u
n
ctio
n
at
its
b
est.
T
o
f
it th
e
m
o
d
el,
lo
g
is
tic
r
eg
r
es
s
io
n
is
u
s
ed
af
ter
th
e
d
ata
is
p
r
ep
ar
ed
.
E
s
tim
atin
g
th
e
p
ar
am
eter
s
(
c
o
ef
f
icien
ts
)
t
h
at
g
o
v
e
r
n
th
e
im
p
ac
t
o
f
ea
c
h
ch
ar
ac
ter
is
tic
o
n
t
h
e
in
cu
r
s
io
n
lik
elih
o
o
d
is
an
im
p
o
r
tan
t
p
ar
t
o
f
th
is
p
r
o
ce
s
s
.
T
o
f
o
r
ec
ast
th
e
lik
elih
o
o
d
o
f
ea
ch
d
ata
in
s
tan
ce
b
ein
g
lab
el
ed
as
an
in
tr
u
s
io
n
,
th
e
m
o
d
el
u
s
es
th
ese
co
ef
f
icien
ts
.
I
n
o
r
d
er
to
d
eter
m
in
e
th
e
p
ar
am
eter
v
alu
es
th
at
m
ax
im
i
ze
th
e
lik
elih
o
o
d
o
f
th
e
o
b
s
er
v
e
d
d
ata,
th
e
f
itti
n
g
p
r
o
ce
d
u
r
e
em
p
lo
y
s
m
ax
im
u
m
lik
elih
o
o
d
esti
m
atio
n
.
T
h
e
f
e
at
u
r
es
a
r
e
e
x
t
r
a
cte
d
f
r
o
m
t
h
e
d
a
tase
t
DS
co
n
s
i
d
er
ed
a
n
d
all
t
h
e
f
ea
t
u
r
es
a
r
e
e
x
t
r
a
ct
e
d
u
s
i
n
g
(
1
)
.
[
(
i
,
M
)
]
=
(
∑
{
|
|
−
(
)
|
|
|
|
−
(
)
|
|
}
∈
1
∗
)
(
1
)
Her
e,
is
th
e
co
u
n
t
o
f
to
tal
r
e
co
r
d
s
co
n
s
id
er
e
d
in
t
h
e
d
atase
t,
is
th
e
last
r
ec
o
r
d
in
th
e
d
at
aset,
is
th
e
o
p
tim
u
m
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r
esh
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ld
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e.
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h
e
f
ea
tu
r
e
s
et
is
ca
lcu
lated
b
ased
o
n
th
e
o
p
tim
u
m
th
r
esh
o
ld
s
elec
ted
f
o
r
a
n
aly
zin
g
th
e
tr
af
f
ic
r
ate.
T
h
e
o
p
tim
u
m
t
h
r
esh
o
ld
is
ca
lcu
lated
as
(
2
)
.
=
∑
∑
|
[
(
)
]
+
1
−
[
(
+
1
,
)
]
|
N
∑
∑
ma
x
(
(
,
)
)
(
2
)
L
et
{
1
,
2
.
.
.
.
.
.
.
}
is
th
e
s
et
o
f
p
ac
k
ets
tr
av
ellin
g
in
th
e
n
etwo
r
k
.
L
et
b
e
a
r
an
d
o
m
v
ar
ia
b
le
with
p
r
o
b
a
b
ilit
y
lev
els
.
Pre
-
P
r
o
c
e
ss
e
d
Ne
t
wo
r
k
T
r
a
f
f
i
c
D
a
t
a
s
e
t
Fe
a
t
u
r
e
E
x
t
r
a
c
t
i
o
n
a
n
d
F
e
a
t
u
r
e
S
e
l
e
c
t
i
o
n
N
o
d
e
Tr
u
st
F
a
c
t
o
r
V
a
l
i
d
a
t
i
o
n
A
p
p
l
y
F
r
e
q
u
e
n
t
T
i
me
I
n
t
e
r
v
a
l
B
a
l
a
n
c
i
n
g
M
o
d
u
l
e
Att
a
c
k
Pr
e
d
i
c
t
i
o
n
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.
15
,
No
.
2
,
Ap
r
il
20
25
:
1
4
5
5
-
1
4
6
2
1458
(
=
)
=
_
_
_
(
3
)
T
h
e
n
o
d
es tr
u
s
t f
ac
to
r
th
at
i
n
v
o
lv
ed
in
tr
a
n
s
m
is
s
io
n
is
ca
lcu
lated
as
(
4
)
.
(
N
(
i
)
)
=
∑
δ
=
1
(
⍵
−
(
)
)
+
min
(
)
+
ma
x
(
)
(
4
)
wh
er
e
is
th
e
to
tal
n
etwo
r
k
n
o
d
es
co
u
n
t,
δ
in
d
icate
s
th
e
n
etwo
r
k
r
a
n
g
e,
⍵
in
d
icate
s
n
o
d
es
with
in
th
e
r
an
g
e
,
r
ep
r
esen
ts
p
r
o
b
ab
ilit
y
in
d
ex
o
f
in
s
tan
t
n
o
d
e
in
th
e
r
a
n
g
e.
T
h
e
p
r
o
b
a
b
ilit
y
in
d
ex
o
f
an
in
s
tan
t
n
o
d
e
is
ca
lcu
lated
as
(
5
)
.
=
−
+
(
5
)
wh
er
e
is
th
e
to
tal
p
ac
k
ets
r
ec
eiv
ed
,
is
th
e
p
ac
k
ets
tr
an
s
f
er
r
ed
an
d
is
th
e
tim
e
tak
en
f
o
r
n
o
d
e
to
d
ata
tr
an
s
f
er
an
d
is
th
e
to
tal
p
ac
k
ets g
en
er
ated
th
at
is
tr
an
s
f
er
r
ed
to
v
ar
io
u
s
d
esti
n
atio
n
s
.
T
h
e
tr
u
s
t f
ac
to
r
v
alid
atio
n
o
f
a
ll th
e
n
o
d
es in
v
o
lv
ed
i
n
th
e
n
e
two
r
k
tr
an
s
m
is
s
io
n
is
v
alid
ated
as
(
6
)
.
(
(
N
(
i
)
)
(
)
{
(
(
)
)
=
|
(
)
|
+
(
FS
(
i
)
)
+
(
)
+
(
N
(
i
)
)
(
6
)
}
{
ℎ
‘
0’
.
}
E
ac
h
f
ea
tu
r
e
weig
h
t is ca
lcu
lated
f
r
o
m
ea
ch
p
ix
el
f
o
r
f
in
al
v
e
cto
r
g
en
e
r
atio
n
th
at
is
p
er
f
o
r
m
ed
as
(
7
)
.
(
(
)
)
=
∫
(
,
+
1
)
+
∫
(
(
)
)
+
⍵
+
∑
(
−
(
)
)
=
1
=
=
1
(
7
)
T
h
e
f
in
al
f
ea
tu
r
e
v
ec
to
r
s
et
is
g
en
er
ated
as
(
8
)
.
(
(
)
)
=
∑
(
(
(
,
+
1
)
)
=
1
)
−
1
{
∑
v
alid
ato
r
(
(
)
)
=
1
}
(
8
)
T
h
e
f
ea
tu
r
e
s
et
is
f
in
alize
d
an
d
r
elev
an
t
f
ea
tu
r
es
ar
e
ex
tr
ac
t
ed
.
T
h
e
t
r
af
f
ic
a
n
aly
s
is
o
f
ev
e
r
y
n
o
d
e
is
p
er
f
o
r
m
ed
u
s
in
g
f
r
eq
u
e
n
t
tim
e
in
ter
v
al
b
alan
cin
g
m
o
d
el
to
b
alan
ce
th
e
n
o
d
e
m
o
n
it
o
r
in
g
s
u
ch
th
at
all
n
o
d
es
tr
af
f
ic
n
ee
d
s
to
b
e
an
aly
ze
d
.
T
h
e
n
o
d
e
b
alan
cin
g
in
f
r
eq
u
e
n
t
tim
e
in
ter
v
al
is
p
er
f
o
r
m
ed
as
(
9
)
.
(
(
)
)
=
∑
(
(
)
+
,
(
)
∗
∞
=
1
min
(
)
+
ma
x
(
)
(
9
)
Her
e
is
th
e
tim
e
in
ter
v
al,
,
ar
e
th
e
s
tar
tin
g
an
d
en
d
in
g
tim
e
l
ev
els
f
o
r
tr
a
f
f
ic
an
aly
s
is
.
is
th
e
o
p
tim
u
m
T
h
r
esh
o
ld
.
(
(
(
)
)
<
ℎ
ℎ
_
_
)
{
(
(
)
)
=
∑
∑
(
(
(
)
)
)
+
N
(
i
)
)
v
a
l
i
d
a
t
o
r
(
=
=
1
(
1
0
)
(
(
)
)
=
(
(
)
)
+
∑
∑
−
1
=
=
1
(
(
)
)
+
(
(
(
)
)
+
∑
(
−
(
)
)
=
1
(
1
1
)
}
{
ℎ
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
Tr
u
s
t fa
cto
r
va
lid
a
tio
n
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RAP
H
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RS
Ma
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ti
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l
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a
n
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ra
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p
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r
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o
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le
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lar,
th
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a
re
m
o
re
th
a
n
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it
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s
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p
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).
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r
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a
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th
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re
late
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h
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v
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m
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h
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s
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in
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it
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d
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rre
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tl
y
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id
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x
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h
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sc
h
o
lars
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in
c
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a
n
d
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e
h
a
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th
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a
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d
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m
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n
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n
t
h
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a
re
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s
in
ter
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t
o
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s
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d
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ter
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h
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o
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tri
b
u
ti
n
g
to
t
h
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so
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t
h
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m
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r
with
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c
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m
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d
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m
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u
c
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ly
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c
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m
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la.rb
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n
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f
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c
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.
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