I
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l J
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urna
l o
f
E
lect
rica
l a
nd
Co
m
p
ute
r
E
ng
in
ee
ring
(
I
J
E
CE
)
Vo
l.
10
,
No
.
1
,
Feb
r
u
ar
y
2020
,
p
p
.
467
~
476
I
SS
N:
2088
-
8708
,
DOI
: 1
0
.
1
1
5
9
1
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.
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476
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I
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D
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d
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e
n
t
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h
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a
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.
W
ir
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Se
n
s
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Net
w
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k
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s
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t
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es.
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ased
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ased
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8708
I
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t J
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&
C
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p
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g
,
Vo
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10
,
No
.
1
,
Feb
r
u
ar
y
2
0
2
0
:
467
-
476
468
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h
ic
h
s
u
its
it
s
p
r
o
p
er
ties
.
Ov
er
r
ec
en
t
y
ea
r
s
s
ev
er
al
ar
tic
les a
v
ailab
le
o
n
I
DS
f
o
r
w
ir
ele
s
s
s
en
s
o
r
n
et
w
o
r
k
s
[
1
3
,
1
4
]
.
T
h
e
p
ap
er
s
r
ef
er
to
th
e
W
SN
w
h
ic
h
is
n
o
t
ex
p
o
s
ed
d
ir
ec
tl
y
to
th
e
I
n
ter
n
e
t.
W
h
er
ea
s
n
o
w
ad
a
y
s
t
h
ese
s
e
n
s
o
r
n
o
d
es a
r
e
p
ar
t
o
f
I
n
ter
n
et
o
f
T
h
i
n
g
s
.
Hen
ce
t
h
ese
n
o
d
es
ar
e
g
lo
b
all
y
av
aila
b
le
th
r
o
u
g
h
th
e
I
n
ter
n
et
v
ia
I
P
V6
B
o
r
d
e
r
R
o
u
ter
(
I
P
v
6
B
R
)
.
So
th
er
e
is
a
r
eq
u
ir
e
m
en
t o
f
m
o
r
e
ad
v
a
n
ce
d
I
DS
w
h
ic
h
ca
n
id
en
ti
f
y
m
u
ltip
le
at
tack
s
.
T
h
ese
a
ttack
s
ca
n
b
e
d
etec
ted
ef
f
ec
ti
v
el
y
u
s
i
n
g
m
ac
h
in
e
lear
n
i
n
g
al
g
o
r
ith
m
s
[
1
5
]
.
W
allg
r
en
et
a
l
[
16
]
an
d
L
e
et
a
l
[
17
]
h
av
e
p
r
o
v
en
t
h
at
R
P
L
n
e
t
w
o
r
k
is
v
u
ln
er
ab
le
t
o
m
u
ltip
le
attac
k
s
.
I
n
o
r
d
er
to
h
an
d
le
t
h
ese
attac
k
s
in
R
P
L
b
ased
n
et
wo
r
k
s
an
o
m
al
y
-
ba
s
ed
I
DS
p
r
o
p
o
s
ed
b
y
R
az
a
et
a
l
[1
8
].
T
h
is
w
o
r
k
s
i
n
t
w
o
p
h
a
s
es.
I
n
i
tiall
y
,
I
DS
d
ata
co
llec
ted
later
it
is
a
n
al
y
ze
d
.
I
n
t
h
e
in
itia
l
p
h
ase,
t
h
e
DOD
A
G
’
s
r
o
o
t
co
llect
s
i
n
f
o
r
m
atio
n
ab
o
u
t
all
it
s
m
e
m
b
e
r
s
an
d
t
h
eir
n
ei
g
h
b
o
r
s
.
T
h
at
is
p
ar
en
t
n
o
d
e,
al
l
n
eig
h
b
o
r
s
a
n
d
th
e
ir
co
r
r
esp
o
n
d
in
g
r
a
n
k
s
a
n
d
th
e
n
o
d
e’
s
r
an
k
an
d
a
ti
m
esta
m
p
.
B
ased
o
n
th
is
i
n
f
o
r
m
atio
n
m
o
n
ito
r
i
n
g
n
o
d
e
v
er
i
f
ie
s
th
e
r
an
k
o
f
ea
ch
n
o
d
e
to
id
en
ti
f
y
an
y
r
an
k
i
n
co
n
s
i
s
te
n
c
y
to
id
e
n
ti
f
y
th
e
m
al
icio
u
s
ac
tiv
it
y
i
n
t
h
e
n
et
w
o
r
k
.
Ho
w
e
v
er
,
th
e
b
eh
a
v
io
r
o
f
t
h
e
n
e
t
w
o
r
k
w
ill
c
h
an
g
e
w
it
h
a
n
i
n
cr
ea
s
e
in
t
h
e
n
u
m
b
er
o
f
m
alicio
u
s
n
o
d
es.
Hen
ce
th
er
e
is
a
n
ee
d
to
id
en
ti
f
y
m
u
ltip
le
a
ttack
s
f
o
r
v
ar
ied
n
et
w
o
r
k
s
ize.
R
P
L
b
ased
n
et
w
o
r
k
s
DO
D
AG
co
n
s
tr
u
ctio
n
is
b
ased
o
n
t
wo
o
b
j
ec
tiv
e
f
u
n
ctio
n
s
,
E
x
p
ec
ted
Nu
m
b
er
o
f
T
r
an
s
m
is
s
io
n
s
(
E
T
X
)
an
d
Ob
j
ec
tiv
e
Fu
n
ctio
n
0
(
OF0
)
w
h
ic
h
w
o
r
k
s
b
ased
o
n
h
o
p
co
u
n
t
[
1
9
]
.
Fro
m
t
h
e
l
iter
atu
r
e,
i
t
ca
n
b
e
id
en
ti
f
ied
t
h
at
p
er
f
o
r
m
an
ce
o
f
E
T
X
b
ased
6
L
o
w
P
AN
n
et
w
o
r
k
i
s
b
etter
co
m
p
ar
ed
to
OF0
[
2
0
,
2
1
]
.
Sh
r
ee
n
iv
a
s
et
a
l
[
22
]
p
r
o
p
o
s
ed
E
T
X
b
ased
I
DS.
B
u
t
th
e
n
u
m
b
er
o
f
n
o
d
es
co
n
s
id
er
ed
f
o
r
th
e
ex
p
er
i
m
e
n
tatio
n
u
s
in
g
s
i
m
u
latio
n
is
v
er
y
le
s
s
.
E
x
is
t
in
g
liter
at
u
r
e
f
o
cu
s
es
o
n
a
f
i
x
ed
n
u
m
b
er
o
f
m
alic
io
u
s
n
o
d
es
w
i
th
s
m
all
n
et
w
o
r
k
s
ize.
Mu
l
tip
l
e
attac
k
s
h
a
v
e
n
o
t
b
ee
n
ta
k
en
ca
r
e
o
f
.
T
h
is
p
ap
er
f
o
cu
s
es o
n
t
h
e
f
o
llo
w
i
n
g
:
a.
Si
m
u
late
t
h
e
R
P
L
n
et
w
o
r
k
u
s
in
g
C
o
o
j
a
s
i
m
u
lato
r
o
n
th
e
C
o
n
ti
k
i
Op
er
atin
g
S
y
s
te
m
u
s
in
g
E
T
X
as
an
o
b
j
ec
tiv
e
f
u
n
c
tio
n
.
b.
I
d
en
tif
icat
io
n
o
f
m
u
l
tip
le
att
ac
k
s
b
y
s
ca
l
in
g
u
p
t
h
e
n
e
t
wo
r
k
s
ize
w
i
th
th
e
p
er
ce
n
tag
e
in
cr
ea
s
e
in
th
e
m
alicio
u
s
n
o
d
es
c.
C
o
m
p
ar
is
o
n
o
f
n
et
w
o
r
k
b
eh
av
io
r
f
o
r
th
e
ab
o
v
e
-
m
en
t
io
n
ed
n
et
w
o
r
k
s
et
u
p
b
ased
o
n
d
if
f
er
e
n
t p
ar
a
m
eter
s
T
h
e
o
v
er
all
ar
ch
itec
tu
r
e
o
f
t
h
e
R
P
L
b
ased
n
e
t
w
o
r
k
i
s
s
h
o
wn
in
Fig
u
r
e
1
.
T
h
e
r
o
u
ter
ca
ll
ed
I
P
v
6
B
R
is
co
n
n
ec
ted
to
th
e
o
u
t
s
id
e
wo
r
ld
.
I
DS
m
o
d
u
le
u
s
ed
is
ce
n
t
r
alize
d
an
d
d
is
tr
ib
u
ted
ca
lled
h
y
b
r
id
a
n
d
is
s
to
r
ed
in
I
P
v
6
B
R
as
w
e
ll
as
i
n
i
n
d
i
v
i
d
u
al
n
o
d
es.
DO
D
A
G
r
o
o
t
co
llects
i
n
f
o
r
m
a
tio
n
ab
o
u
t
th
e
R
P
L
n
et
w
o
r
k
s
u
c
h
a
s
DOD
A
G
I
D,
R
P
L
I
n
s
tan
ce
I
D,
th
e
DOD
AG
Ver
s
io
n
N
u
m
b
er
,
th
e
p
ar
en
t
n
o
d
e
ID
,
all
n
ei
g
h
b
o
r
s
an
d
t
h
eir
co
r
r
esp
o
n
d
in
g
r
an
k
s
,
an
d
t
h
e
n
o
d
e’
s
r
an
k
an
d
a
ti
m
esta
m
p
.
I
DS
m
o
d
u
le
a
n
al
y
ze
s
t
h
is
d
ata
to
id
en
ti
f
y
th
e
in
tr
u
s
io
n
s
.
Fig
u
r
e
1
.
Ov
er
all
a
r
ch
itect
u
r
e
o
f
I
DS
in
R
P
L
b
ased
n
et
w
o
r
k
s
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
E
lec
&
C
o
m
p
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n
g
I
SS
N:
2088
-
8708
Mu
ltip
le
in
tr
u
s
io
n
d
etec
tio
n
in
R
P
L b
a
s
ed
n
etw
o
r
ks (
Ma
n
ju
la
C
B
ela
va
g
i)
469
R
est o
f
th
e
p
ap
er
is
o
r
g
an
ized
as f
o
llo
w
s
.
Sec
tio
n
2
d
escr
ib
es th
e
DO
D
A
G
co
n
s
tr
u
ct
io
n
.
I
n
Sectio
n
3
r
esear
ch
m
et
h
o
d
u
s
ed
f
o
r
in
tr
u
s
io
n
d
etec
tio
n
in
R
P
L
b
ased
n
et
w
o
r
k
s
is
d
is
cu
s
s
ed
.
Sectio
n
4
d
is
cu
s
s
es
r
es
u
lts
an
d
an
al
y
s
i
s
o
f
R
P
L
b
ased
n
et
w
o
r
k
f
o
r
d
if
f
er
e
n
t sce
n
ar
io
s
a
n
d
Sectio
n
5
co
n
cl
u
d
es th
e
p
a
p
er
.
2.
DO
DAG
CO
N
ST
R
UCT
I
O
N
I
n
th
i
s
s
ec
tio
n
R
P
L
b
ased
n
et
w
o
r
k
to
p
o
lo
g
y
b
u
ild
i
n
g
p
r
o
ce
s
s
is
d
i
s
cu
s
s
ed
.
R
P
L
i
s
a
d
is
ta
n
ce
v
ec
to
r
s
o
u
r
ce
r
o
u
tin
g
p
r
o
to
co
l
u
s
ed
b
y
lo
w
p
o
w
er
a
n
d
lo
s
s
y
n
et
w
o
r
k
s
R
P
L
n
et
w
o
r
k
s
d
o
n
o
t
h
av
e
p
r
ed
ef
in
ed
to
p
o
lo
g
y
.
Fi
g
u
r
e
3
s
h
o
w
s
tr
e
e
-
li
k
e
to
p
o
lo
g
y
w
it
h
p
ar
en
t
an
d
lea
f
n
o
d
e
s
cr
ea
ted
b
y
R
P
L
b
ased
n
et
w
o
r
k
.
Fo
r
th
e
co
n
s
tr
u
ct
io
n
tr
ee
-
l
ik
e
to
p
o
lo
g
y
,
R
P
L
u
s
e
s
I
C
MP
V6
co
n
tr
o
l
m
es
s
a
g
es
n
a
m
el
y
DOD
AG
A
d
v
er
tis
e
m
en
t
Ob
j
ec
t
(
D
A
O
)
,
I
n
f
o
r
m
a
tio
n
Ob
j
ec
t
(
DI
O)
an
d
D
OD
A
G
I
n
f
o
r
m
atio
n
So
licitatio
n
(
DI
S).
I
n
R
P
L
n
et
w
o
r
k
to
p
o
lo
g
y
(
DOD
A
G)
h
a
s
a
s
in
g
le
r
o
o
t
n
o
d
e
(
R
1
)
w
i
th
n
o
o
u
t
g
o
in
g
ed
g
es
as
s
h
o
w
n
i
n
Fig
u
r
e
2
.
I
t
is
ca
lled
as
I
P
v
6
B
R
in
6
L
OW
P
AN
n
et
w
o
r
k
s
.
I
n
a
DOD
A
G
a
r
an
k
i
s
ass
o
ciat
ed
w
it
h
ea
ch
n
o
d
e.
T
h
is
v
al
u
e
r
ep
r
esen
ts
t
h
e
p
o
s
itio
n
o
f
th
e
n
o
d
e
in
DOD
A
G
ac
co
r
d
in
g
to
r
o
o
t.
R
an
k
o
f
a
n
o
d
e
is
al
w
a
y
s
in
cr
ea
s
i
n
g
i
n
th
e
d
o
w
n
w
ar
d
d
ir
ec
tio
n
,
f
r
o
m
r
o
o
t
to
leaf
n
o
d
e
s
as
s
h
o
w
n
in
Fi
g
u
r
e
3
.
O
b
j
ec
t
iv
e
f
u
n
ctio
n
s
u
s
e
d
f
o
r
th
e
co
n
s
tr
u
ctio
n
o
f
DOD
AG
ar
e:
a.
Ob
j
ec
tiv
e
Fu
n
c
tio
n
0
(
OF0
)
b.
E
x
p
ec
ted
T
r
an
s
m
is
s
io
n
C
o
u
n
t
(
E
T
X)
c.
U
s
er
-
de
fi
n
ed
o
b
j
ec
tiv
e
f
u
n
ct
io
n
s
I
t
is
id
en
ti
fi
ed
b
y
D
OD
A
G
I
D
an
d
R
P
L
Seq
u
en
ce
I
D
.
Fi
g
u
r
e
3
s
h
o
w
s
DO
D
A
G
a
n
d
th
e
r
an
k
ass
o
ciate
d
w
ith
ea
c
h
n
o
d
e.
T
h
e
tr
an
s
m
is
s
io
n
p
ath
i
s
s
elec
ted
b
ased
o
n
th
e
co
n
s
tr
u
cted
DO
DAG.
Fig
u
r
e
2
.
Destin
at
io
n
o
r
ien
ted
d
ir
ec
ted
ac
y
clic
g
r
ap
h
(
DOD
A
G
)
–
co
n
s
tr
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Fig
u
r
e
3
.
Destin
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g
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r
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3.
3.
RE
S
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tr
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.
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h
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lated
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ze
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p
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ies
R
ate
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g
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4
s
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w
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f
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g
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ased
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eter
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in
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le
1
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Sim
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er
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e,
clien
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n
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d
es
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d
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s
p
ee
d
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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N
:
2
0
8
8
-
8708
I
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&
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p
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g
,
Vo
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,
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1
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r
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0
:
467
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476
470
li
m
it.
So
m
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alicio
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p
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m
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ize
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h
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ap
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5
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.
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n
r
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all
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th
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ased
6
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u
r
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4
.
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f
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Alg
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ruct
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r
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ce
d
u
r
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DOD
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(
No
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es N)
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n
itiall
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3:
f
o
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t N
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4:
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5
:
T
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d
es j
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s
th
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et
w
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k
6:
Selects R
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o
t a
s
t
h
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p
r
ef
er
r
ed
p
ar
en
t
7:
f
o
r
all
No
d
es N
d
o
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8708
Mu
ltip
le
in
tr
u
s
io
n
d
etec
tio
n
in
R
P
L b
a
s
ed
n
etw
o
r
ks (
Ma
n
ju
la
C
B
ela
va
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i)
471
8:
Sen
d
DI
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m
e
s
s
a
g
e
9:
No
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es
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ich
ar
e
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le
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t N
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et
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k
10:
if
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im
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ar
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ir
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11:
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m
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12:
en
d
if
13:
g
o
to
Step
6
14:
en
d
f
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r
15:
if
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y
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f
th
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t r
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16:
Sen
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17:
en
d
if
18:
Go
to
Step
3
19:
r
etu
r
n
2
0
: e
n
d
p
r
o
ce
d
u
r
e
An
al
g
o
r
ith
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n
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r
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t
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G
in
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n
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ies
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e
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Alg
o
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it
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m
2
.
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e
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th
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G
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er
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ied
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ti
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cie
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et
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.
R
a
n
k
o
f
t
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e
p
ar
en
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d
e,
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e
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D
an
d
th
e
r
a
n
k
o
f
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h
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d
o
f
it
s
n
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h
b
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s
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r
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id
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y
t
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m
ap
p
er
.
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n
o
r
d
er
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co
r
r
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t
in
f
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r
m
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h
e
r
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k
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ied
.
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f
an
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r
o
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n
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n
s
is
ten
cie
s
(
r
an
k
at
tack
)
,
th
en
it is
h
an
d
led
as
f
o
llo
w
s
:
a.
Fau
lt
y
n
o
d
es
ar
e
r
e
m
o
v
ed
f
r
o
m
th
e
w
h
ite
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l
is
t o
f
t
h
e
m
ap
p
er
b.
I
n
co
n
s
i
s
te
n
cies a
r
e
co
r
r
ec
ted
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ased
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n
th
e
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k
o
f
th
e
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ei
g
h
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o
r
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d
e
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t
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e
s
s
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t
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ly
w
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g
n
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d
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d
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a
p
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s
s
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ilit
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o
f
m
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ltip
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r
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o
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p
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f
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R
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ai
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s
w
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ite
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o
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ct
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al
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o
f
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ar
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t
h
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h
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ilter
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o
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e
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o
d
es
is
d
escr
ib
ed
in
A
l
g
o
r
ith
m
3
.
Alg
o
rit
h
m
2
I
de
ntif
ica
t
io
n
a
nd
co
rr
ec
t
io
n
o
f
RP
L
DO
DAG
I
nco
n
s
is
t
e
ncies
1
:
p
r
o
ce
d
u
r
e
ch
ec
k
-
i
n
co
n
s
i
s
te
n
c
y
(
No
d
esN)
2:
f
o
r
A
l
l N
o
d
es in
t
h
e
n
et
w
o
r
k
d
o
3
:
f
o
r
ea
ch
n
ei
g
h
b
o
r
d
o
4:
C
o
m
p
u
te
th
e
d
i
ff
er
e
n
ce
in
t
h
e
r
an
k
5
:
C
o
m
p
u
te
th
e
Av
er
ag
e
d
i
ff
er
e
n
ce
6:
if
T
h
e
co
m
p
u
ted
Di
ff
er
e
n
ce
is
>
0
.
2
th
en
7
:
I
n
cr
ea
s
e
th
e
n
u
m
b
er
o
f
f
a
u
lt
y
n
o
d
es
8
:
en
d
if
9
:
en
d
f
o
r
1
0
:
en
d
f
o
r
1
1
:
f
o
r
No
d
e
in
N
d
o
1
2
:
if
T
h
e
n
u
m
b
er
o
f
f
au
l
t
y
n
o
d
es is
>
Fau
l
t
-
T
h
r
es
h
o
ld
th
e
n
1
3
:
Up
d
ate
th
e
r
an
k
b
ased
o
n
th
e
n
eig
h
b
o
r
1
4
:
en
d
if
1
5
:
en
d
f
o
r
1
6
:
r
etu
r
n
1
7
: e
n
d
p
r
o
ce
d
u
r
e
Alg
o
rit
h
m
3
F
ilte
re
d No
des
1
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r
o
ce
d
u
r
e
Fil
ter
-
n
o
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es (
No
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es
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h
ite
-
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2:
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o
r
No
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e
in
w
h
ite
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li
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t d
o
3:
if
No
d
e
k
n
o
w
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to
R
P
L
D
OD
AG
th
e
n
4:
A
d
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th
e
n
o
d
e
to
Fil
ter
ed
s
et.
5:
en
d
if
6:
en
d
f
o
r
7
:
r
etu
r
n
8
: e
n
d
p
r
o
ce
d
u
r
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
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lec
&
C
o
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p
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n
g
,
Vo
l.
10
,
No
.
1
,
Feb
r
u
ar
y
2
0
2
0
:
467
-
476
472
I
n
th
e
ca
s
e
o
f
R
P
L
n
et
w
o
r
k
s
,
th
e
r
an
k
o
f
t
h
e
n
o
d
es
i
s
in
cr
ea
s
in
g
to
w
ar
d
s
th
e
lea
f
as
s
h
o
w
n
i
n
Fig
u
r
e
3
.
So
m
e
ti
m
es
e
v
en
to
p
o
lo
g
y
f
o
llo
w
s
t
h
e
R
P
L
p
ar
en
t.
I
n
c
h
ild
r
an
k
r
elat
io
n
s
h
ip
t
h
er
e
is
a
p
o
s
s
ib
ilit
y
o
f
in
co
n
s
i
s
te
n
c
y
.
I
n
co
n
s
is
te
n
c
y
w
it
h
r
esp
ec
t
to
p
ar
en
t
-
c
h
ild
r
elatio
n
s
h
ip
ca
n
a
ls
o
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e
id
en
ti
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ied
b
y
co
m
p
ar
i
n
g
th
e
r
an
k
o
f
th
e
h
o
s
t
n
o
d
e
an
d
its
p
ar
en
t.
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h
is
n
ee
d
to
b
e
w
it
h
in
t
h
e
r
an
g
e
Min
-
Ho
p
-
R
an
k
-
I
n
cr
ea
s
e
(
v
alu
e
s
e
t
f
o
r
th
e
s
i
m
u
latio
n
)
[
2
6
]
as d
es
cr
ib
ed
in
A
l
g
o
r
ith
m
4
.
Alg
o
rit
h
m
4
Ra
n
k
I
nco
ns
i
s
t
ency
I
dentif
ica
t
io
n
1
: p
r
o
ce
d
u
r
e
Fin
d
-
R
an
k
-
i
n
co
n
s
is
te
n
c
y
(
No
d
es
AL
L
N)
2:
f
o
r
E
v
er
y
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e
in
AL
L
N
d
o
3:
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e
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p
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cr
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t N
o
d
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s
r
an
k
t
h
en
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p
d
ate
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f
a
u
lt
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n
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d
e
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u
n
t
5:
en
d
if
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d
f
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r
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o
r
E
v
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e
in
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L
N
d
o
8:
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s
f
a
u
lt c
o
u
n
t is >
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u
l
t
-
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h
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esh
o
ld
th
e
n
9:
R
aise a
n
alar
m
10:
en
d
if
11:
en
d
f
o
r
12:
r
etu
r
n
1
3
: e
n
d
p
r
o
ce
d
u
r
e
Si
m
u
lated
d
ata
co
n
s
is
t
s
o
f
tr
af
f
ic
d
ata
w
h
ich
is
i
n
“
p
ca
p
”
f
o
r
m
at.
I
t
h
as
b
o
th
n
o
r
m
al
an
d
m
a
licio
u
s
p
ac
k
ets.
T
h
is
d
ata
is
clu
s
ter
ed
u
s
i
n
g
K
-
m
ea
n
s
to
g
et
clu
s
ter
s
w
h
ic
h
ar
e
n
o
r
m
al
an
d
clu
s
te
r
s
o
f
o
th
er
attac
k
s
.
Af
ter
g
e
tti
n
g
cl
u
s
ter
s
,
th
e
s
e
ar
e
lab
eled
ac
co
r
d
in
g
l
y
as
t
h
e
n
o
r
m
al
,
r
a
n
k
attac
k
,
s
elec
t
iv
e
f
o
r
w
ar
d
in
g
attac
k
s
,
an
d
DOS.
T
h
is
g
i
v
es
s
u
p
er
v
i
s
ed
d
ataset
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h
ich
ca
n
b
e
u
s
e
d
f
o
r
b
u
ild
in
g
p
r
ed
ictio
n
m
o
d
els
f
o
r
id
en
tify
i
n
g
th
ese
attac
k
s
.
Ma
c
h
i
n
e
lear
n
i
n
g
al
g
o
r
ith
m
R
an
d
o
m
Fo
r
est
C
las
s
i
f
ier
is
u
s
ed
to
b
u
i
ld
a
p
r
ed
ictiv
e
m
o
d
el
an
d
f
u
r
t
h
er
clas
s
i
f
icatio
n
.
Di
f
f
er
e
n
t
m
o
d
el
s
ar
e
b
u
il
t
w
it
h
d
i
f
f
e
r
en
t
n
e
t
w
o
r
k
s
ize
an
d
m
alicio
u
s
n
o
d
es.
Fo
r
ea
c
h
ac
cu
r
ac
y
a
n
d
Fals
e
P
o
s
itiv
e
R
ate
ar
e
r
ec
o
r
d
ed
.
4.
RE
SU
L
T
S
A
ND
AN
AL
Y
SI
S
W
ir
eless
s
e
n
s
o
r
n
et
w
o
r
k
h
a
v
in
g
n
o
r
m
al
a
n
d
m
a
licio
u
s
n
o
d
es
is
s
i
m
u
lated
o
n
C
o
n
ti
k
i
o
p
er
atin
g
s
y
s
te
m
u
s
i
n
g
C
o
o
j
a
s
im
u
lat
o
r
.
Ser
ies
o
f
s
i
m
u
la
tio
n
s
ar
e
ca
r
r
ied
o
u
t
b
y
v
ar
y
in
g
t
h
e
n
u
m
b
er
o
f
n
o
d
es.
T
h
e
b
eh
av
io
r
o
f
th
e
n
e
t
w
o
r
k
i
s
o
b
s
er
v
ed
in
th
r
ee
d
i
f
f
er
e
n
t c
ases
:
a.
1
0
% in
cr
ea
s
e
in
m
alicio
u
s
n
o
d
e
s
o
u
t o
f
1
0
,
4
0
an
d
1
0
0
n
o
r
m
al
n
o
d
es
b.
2
0
%
in
cr
ea
s
e
in
m
alicio
u
s
n
o
d
es
o
u
t o
f
1
0
,
4
0
an
d
1
0
0
n
o
r
m
al
n
o
d
es
c.
3
0
%
in
cr
ea
s
e
in
m
alicio
u
s
n
o
d
es
w
it
h
1
0
,
4
0
an
d
1
0
0
n
o
r
m
al
n
o
d
es
T
h
e
s
er
v
er
n
o
d
e
ac
ts
as
th
e
r
o
o
t
o
f
t
h
e
D
OD
A
G.
Si
m
u
lati
o
n
ti
m
i
n
g
s
ar
e
co
n
tr
o
lled
b
y
th
e
C
o
o
j
a
p
lu
g
-
i
n
n
a
m
e
l
y
C
o
n
tik
i
tes
t
ed
ito
r
.
T
h
e
n
et
w
o
r
k
s
ce
n
ar
io
is
s
h
o
w
n
i
n
Fi
g
u
r
e
5
.
T
h
e
g
r
ee
n
ar
ea
r
ese
n
t
s
th
e
tr
an
s
m
is
s
io
n
r
ag
e
o
f
n
o
d
e
7
an
d
th
e
g
r
a
y
ar
ea
in
d
icat
es
th
e
i
n
ter
f
er
e
n
ce
r
an
g
e.
No
d
e
1
is
th
e
s
er
v
er
,
n
o
d
e
1
0
is
th
e
m
a
licio
u
s
n
o
d
e
an
d
th
e
r
e
m
ai
n
in
g
ar
e
clie
n
t
n
o
d
es.
T
o
av
o
id
th
e
lo
s
s
es
at
th
e
tr
an
s
m
itter
,
T
r
an
s
m
is
s
io
n
r
atio
(
T
X)
is
s
et
as
100%
.
Hen
ce
lo
s
s
es a
r
e
all
o
w
ed
o
n
l
y
at
t
h
e
r
ec
ei
v
in
g
e
n
d
.
So
th
e
s
i
m
u
latio
n
p
ar
am
eter
R
P
L
-
MO
P
is
s
et
as
NO_
DOW
NW
AR
D_
R
OU
T
E
in
d
icate
s
th
at
m
u
l
tip
o
in
t
to
p
o
in
t
tr
af
f
ic
i
s
co
n
s
id
er
ed
f
o
r
th
e
ev
al
u
atio
n
.
Fig
u
r
e
5
.
Si
m
u
lated
n
et
w
o
r
k
w
it
h
m
alicio
u
s
a
n
d
n
o
n
-
m
alici
o
u
s
m
o
te
s
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
E
lec
&
C
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m
p
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n
g
I
SS
N:
2088
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8708
Mu
ltip
le
in
tr
u
s
io
n
d
etec
tio
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in
R
P
L b
a
s
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n
etw
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r
ks (
Ma
n
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B
ela
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473
Fig
u
r
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6
s
h
o
w
s
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i
m
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et
w
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k
tr
af
f
ic
“
p
ca
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”
w
ith
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al
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s
ac
tiv
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t
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n
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e
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r
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m
F
ig
u
r
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th
at
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it
h
m
alic
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s
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itie
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[
m
al
f
o
r
m
ed
p
ac
k
et
s
]
.
Fig
u
r
e
6
.
Sa
m
p
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tr
af
f
ic
w
h
ic
h
s
h
o
w
s
m
alicio
u
s
p
ac
k
et
4
.
1
.
P
er
f
o
r
m
a
nce
m
et
rics
P
er
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n
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o
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tio
n
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b
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tiv
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ased
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a
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e
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e
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w
:
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ate
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et
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ased
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I
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N
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2
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8
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I
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
E
lec
&
C
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m
p
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n
g
I
SS
N:
2088
-
8708
Mu
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k
r
ed
u
ce
s
d
r
asti
ca
ll
y
.
B
ec
au
s
e
it
i
s
d
i
f
f
ic
u
lt
to
d
if
f
er
e
n
tiate
b
et
w
ee
n
t
h
e
n
o
r
m
al
n
o
d
e
an
d
m
alicio
u
s
n
o
d
es
ef
f
ec
tiv
e
l
y
.
He
n
ce
it
ca
n
b
e
co
n
clu
d
ed
th
at
m
u
ltip
le
attac
k
s
ca
n
b
e
id
en
t
if
ied
ef
f
ec
ti
v
el
y
i
n
R
P
L
b
ased
n
et
w
o
r
k
s
u
s
i
n
g
m
ac
h
in
e
le
ar
n
i
n
g
tech
n
iq
u
e
s
.
RE
F
E
R
E
NC
E
S
[1
]
N
.
Ku
sh
a
ln
a
g
a
r,
e
t
a
l
.,
“
IP
v
6
o
v
e
r
lo
w
-
p
o
we
r
w
irele
ss
p
e
r
so
n
a
l
a
re
a
n
e
t
w
o
rk
s
(6
L
o
W
P
A
Ns
):
o
v
e
rv
ie
w
,
a
ss
u
m
p
ti
o
n
s,
p
r
o
b
lem
sta
te
m
e
n
t,
a
n
d
g
o
a
ls
,
”
RF
C
4
9
1
9
,
2
0
0
7
.
[2
]
T
.
T
sa
o
,
e
t
a
l
.
,
“
A
S
e
c
u
rit
y
T
h
re
a
t
A
n
a
l
y
sis
f
o
r
th
e
Ro
u
ti
n
g
P
ro
t
o
c
o
l
f
o
r
L
o
w
-
P
o
w
e
r
a
n
d
L
o
ss
y
Ne
t
w
o
rk
s
(RP
L
s)
,
”
RF
C
7
4
1
6
,
2
0
1
5
.
[3
]
T
.
W
in
ter
,
e
t
a
l
.,
“
R
P
L
:
I
P
v
6
Ro
u
ti
n
g
P
ro
to
c
o
l
f
o
r
L
o
w
-
P
o
w
e
r
a
n
d
L
o
ss
y
Ne
t
w
o
rk
s
,
”
RF
C
6
5
5
0
,
2
0
1
2
.
[4
]
Jo
Y
.,
e
t
a
l
.
,
“
De
sig
n
a
n
d
im
p
lem
e
n
tatio
n
o
f
h
e
tero
g
e
n
e
o
u
s
s
u
rf
a
c
e
g
a
te
w
a
y
f
o
r
u
n
d
e
rw
a
ter
a
c
o
u
stic
se
n
so
r
n
e
tw
o
rk
,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
El
e
c
trica
l
a
n
d
Co
mp
u
ter
E
n
g
i
n
e
e
rin
g
,
v
o
l
.
9
,
p
p
.
1
2
2
6
-
1
2
3
1
,
2
0
1
9
.
[5
]
A
li
,
e
t
a
l
.,
“
Re
a
l
-
ti
m
e
h
e
a
rt
p
u
lse
m
o
n
it
o
ri
n
g
tec
h
n
iq
u
e
u
sin
g
w
i
re
les
s
s
e
n
so
r
n
e
tw
o
rk
a
n
d
m
o
b
il
e
a
p
p
li
c
a
ti
o
n
,
”
In
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
E
lec
trica
l
a
n
d
C
o
mp
u
ter
En
g
in
e
e
rin
g
,
v
ol
.
8
,
pp
.
5
1
1
8
-
5
1
2
6
,
2
0
1
8
.
[6
]
S
.
Zan
d
e
r
,
e
t
a
l
.,
“
A
u
to
m
a
ted
traff
ic
c
las
si
f
ica
ti
o
n
a
n
d
a
p
p
li
c
a
ti
o
n
id
e
n
ti
f
ica
ti
o
n
u
si
n
g
m
a
c
h
in
e
lea
rn
in
g
,
”
Pro
c
e
e
d
in
g
s
o
f
t
h
e
IEE
E
Co
n
fer
e
n
c
e
o
n
L
o
c
a
l
Co
m
p
u
ter
Ne
two
rk
s
3
0
t
h
A
n
n
ive
rs
a
ry
,
p
p
.
2
5
0
-
2
5
7
,
2
0
0
5
.
[7
]
C.
Ka
rlo
f
a
n
d
D.
W
a
g
n
e
r,
“
S
e
c
u
re
ro
u
ti
n
g
in
w
irele
ss
s
e
n
so
r
n
e
tw
o
rk
s:
A
tt
a
c
k
s
a
n
d
c
o
u
n
term
e
a
su
re
s
,
”
Ad
h
o
c
n
e
two
rk
s
,
v
ol
.
1
,
p
p
.
2
9
3
-
3
1
5
,
2
0
0
3
.
[8
]
Y.
X
u
,
e
t
a
l
.,
“
De
tec
ti
n
g
w
o
r
m
h
o
le
a
tt
a
c
k
s
in
w
ir
e
les
s
s
e
n
so
r
n
e
two
rk
s,
”
Criti
c
a
l
In
fra
stru
c
t
u
re
Pro
tec
ti
o
n
,
a
n
d
S
.
S
h
e
n
o
i,
Ed
s.
Bo
sto
n
,
M
A:
S
p
ri
n
g
e
r US
,
p
p
.
2
6
7
-
2
7
9
,
2
0
0
8
.
[9
]
N
.
Jia
n
g
,
e
t
a
l
.
,
“
Ro
u
t
in
g
a
tt
a
c
k
s
p
re
v
e
n
ti
o
n
m
e
c
h
a
n
is
m
f
o
r
R
P
L
b
a
se
d
o
n
m
icro
p
a
y
m
e
n
t
sc
h
e
m
e
,
”
P
ro
c
e
d
in
g
s
o
f
In
ter
n
a
t
io
n
a
l
C
o
n
fer
e
n
c
e
o
n
W
ire
les
s
Co
mm
u
n
ica
ti
o
n
s
S
ig
n
a
l
Pro
c
e
ss
in
g
a
n
d
Ne
two
rk
in
g
(
W
iS
PNE
T
)
,
p
p
.
8
3
5
-
8
4
1
,
2
0
1
6
.
[1
0
]
A
.
M
a
y
z
a
u
d
,
e
t
a
l
.
,
“
M
it
ig
a
ti
o
n
o
f
to
p
o
lo
g
ica
l
i
n
c
o
n
siste
n
c
y
a
tt
a
c
k
s
in
RP
L
-
b
a
se
d
l
ow
-
p
o
w
e
r
l
o
ss
y
n
e
t
w
o
rk
s
,
”
In
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
Ne
tw
o
rk
M
a
n
a
g
e
me
n
t
,
v
o
l.
2
5
,
p
p
.
3
2
0
-
3
3
9
,
2
0
1
5
.
[1
1
]
M
.
Nik
ra
v
a
n
,
e
t
a
l
.
,
“
A
L
ig
h
t
w
e
ig
h
t
De
f
e
n
se
A
p
p
ro
a
c
h
to
M
it
ig
a
te
V
e
rsio
n
Nu
m
b
e
r
a
n
d
Ra
n
k
A
tt
a
c
k
s
in
L
o
w
-
P
o
w
e
r
a
n
d
L
o
ss
y
Ne
t
w
o
rk
s
,
”
W
ir
e
les
s P
e
rs
o
n
a
l
Co
mm
u
n
ica
ti
o
n
s
,
v
o
l.
9
9
,
pp.
1
0
3
5
-
1
0
5
9
,
2
0
1
8
.
[1
2
]
F
a
d
e
le
,
e
t
a
l
.,
“
In
ter
n
e
t
o
f
T
h
in
g
s
se
c
u
rit
y
:
A
su
rv
e
y
,
”
J
o
u
rn
a
l
o
f
Ne
two
rk
a
n
d
C
o
mp
u
ter
Ap
p
l
ica
ti
o
n
s
,
v
o
l.
8
8
,
2
0
1
7
.
[1
3
]
H
.
S
e
d
jelm
a
c
i
,
e
t
a
l
.
,
“
No
v
e
l
H
y
b
rid
In
tru
si
o
n
De
tec
ti
o
n
S
y
s
tem
f
o
r
Clu
ste
re
d
W
irele
ss
S
e
n
so
r
Ne
tw
o
r
k
,
”
In
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
Ne
tw
o
rk
S
e
c
u
rity
&
Its
Ap
p
li
c
a
ti
o
n
s
,
v
o
l.
3
,
2
0
1
1
.
[1
4
]
Y.
M
a
leh
,
e
t
a
l
.
,
“
A
g
lo
b
a
l
h
y
b
rid
in
tru
si
o
n
d
e
tec
ti
o
n
sy
ste
m
f
o
r
w
irele
ss
se
n
so
r
n
e
tw
o
rk
s,
”
Pro
c
e
d
ia
Co
m
p
u
ter
S
c
ien
c
e
,
6
th
In
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
Amb
ien
t
S
y
ste
ms
,
Ne
two
rk
s
a
n
d
T
e
c
h
n
o
lo
g
ies
(ANT
)
,
v
o
l.
5
2
,
p
p
.
1
0
4
7
-
1
0
5
2
,
2
0
1
5
.
[1
5
]
Be
lav
a
g
i
M.
C.
a
n
d
M
u
n
iy
a
l
B.
,
“
M
u
lt
i
Clas
s
M
a
c
h
in
e
L
e
a
rn
in
g
A
l
g
o
rit
h
m
s
f
o
r
In
tru
si
o
n
De
tec
ti
o
n
-
A
P
e
rf
o
r
m
a
n
c
e
S
tu
d
y
,
”
S
e
c
u
rity
in
Co
m
p
u
t
in
g
a
n
d
Co
mm
u
n
ica
ti
o
n
s.
S
S
CC
2
0
1
7
.
C
o
mm
u
n
ic
a
ti
o
n
s
in
Co
mp
u
te
r
a
n
d
In
f
o
rm
a
ti
o
n
S
c
ien
c
e
,
S
p
ri
n
g
e
r
,
v
o
l
.
7
4
6
,
2
0
1
7
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
10
,
No
.
1
,
Feb
r
u
ar
y
2
0
2
0
:
467
-
476
476
[1
6
]
L
.
W
a
ll
g
re
n
,
e
t
a
l
.,
“
Ro
u
ti
n
g
A
t
tac
k
s
a
n
d
Co
u
n
term
e
a
su
re
s
in
t
h
e
RP
L
-
b
a
se
d
In
ter
n
e
t
o
f
T
h
in
g
s
,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
Distrib
u
ted
S
e
n
so
r Ne
t
wo
rk
s
,
v
o
l
.
9
,
2
0
1
3
.
[1
7
]
Le
,
e
t
a
l
.,
“
T
h
e
im
p
a
c
t
o
f
ra
n
k
a
tt
a
c
k
o
n
n
e
tw
o
rk
to
p
o
l
o
g
y
o
f
ro
u
ti
n
g
p
r
o
to
c
o
l
f
o
r
l
o
w
-
p
o
w
e
r
a
n
d
l
o
ss
y
n
e
t
w
o
rk
s
,
”
IEE
E
S
e
n
so
rs
J
o
u
r
n
a
l
,
v
ol
.
1
3
,
p
p
.
3
6
8
5
-
3
6
9
2
,
2
0
1
3
.
[1
8
]
Ra
z
a
S.
,
e
t
a
l
.,
“
S
V
EL
T
E:
Re
a
l
-
ti
m
e
in
tru
sio
n
d
e
tec
ti
o
n
in
t
h
e
In
tern
e
t
o
f
T
h
in
g
s
,
”
Ad
Ho
c
Ne
two
rk
s
,
v
ol
.
1,
p
p
.
2
6
6
1
-
2
6
7
4
,
2
0
1
3
.
[1
9
]
H.
L
a
m
sa
a
z
i
,
e
t
a
l
.
,
“
S
tu
d
y
o
f
th
e
I
m
p
a
c
t
o
f
De
si
g
n
e
d
Ob
jec
ti
v
e
F
u
n
c
ti
o
n
o
n
th
e
R
P
L
-
Ba
se
d
Ro
u
ti
n
g
P
r
o
t
o
c
o
l
,
”
Ch
a
p
ter
Ad
v
a
n
c
e
s in
Ub
iq
u
it
o
u
s
Ne
two
rk
in
g
,
L
e
c
tu
re
No
tes
i
n
El
e
c
trica
l
En
g
in
e
e
rin
g
,
v
o
l
.
2
,
p
p
.
6
7
-
8
0
,
2
0
1
6
.
[2
0
]
W
.
M
a
rd
in
i
,
e
t
a
l
.
,
“
Co
m
p
re
h
e
n
siv
e
P
e
rf
o
rm
a
n
c
e
A
n
a
l
y
sis
o
f
RP
L
Ob
je
c
ti
v
e
F
u
n
c
ti
o
n
s
in
I
o
T
Ne
t
w
o
rk
s
,”
In
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
C
o
mm
u
n
ica
ti
o
n
Ne
two
rk
s a
n
d
In
f
o
rm
a
ti
o
n
S
e
c
u
rity (
IJ
CNIS
),
v
o
l.
9
,
pp
.
323
-
3
3
2
,
2
0
1
7
.
[2
1
]
W
.
Tan
g
,
e
t
a
l
.
,
“
A
n
a
l
y
sis
a
n
d
o
p
ti
m
iza
ti
o
n
stra
teg
y
o
f
m
u
lt
ip
a
th
RP
L
-
Ba
se
d
o
n
th
e
COO
JA
si
m
u
lato
r
,
”
In
t.
J
.
Co
mp
u
t
.
S
c
i
.
,
v
ol
.
1
1
,
p
p
.
27
-
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l,
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c
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rried
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h
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p
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p
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to
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tern
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la
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in
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s
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s
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sti
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.
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h
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s
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ter
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c
e
s/jo
u
r
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a
ls.
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e
a
rc
h
in
tere
st i
n
c
lu
d
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s n
e
tw
o
rk
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c
u
rit
y
.
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