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R
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ec
tio
n
,
we
d
is
cu
s
s
th
e
ap
p
licatio
n
o
f
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
s
f
o
r
s
ec
u
r
ity
.
T
h
e
f
o
u
r
t
h
s
ec
tio
n
p
r
esen
ts
o
u
r
p
r
o
p
o
s
ed
I
DS
ap
p
r
o
ac
h
.
T
h
e
f
if
t
h
s
ec
tio
n
d
etai
ls
o
u
r
cu
s
to
m
d
ataset.
T
h
e
s
ix
th
s
ec
tio
n
d
is
cu
s
s
es
th
e
r
esu
lts
an
d
p
r
o
v
id
es
an
in
-
d
ep
th
an
al
y
s
is
.
Fin
ally
,
th
e
p
ap
er
co
n
clu
d
es
with
a
s
u
m
m
a
r
y
o
f
o
u
r
f
i
n
d
in
g
s
an
d
s
u
g
g
esti
o
n
s
f
o
r
f
u
tu
r
e
wo
r
k
.
2.
L
I
T
E
R
AT
U
RE
R
E
VI
E
W
2
.
1
.
Rela
t
ed
wo
rk
s
Var
io
u
s
s
tu
d
ies
[
8
]
–
[
1
2
]
h
av
e
b
ee
n
co
n
d
u
cte
d
to
e
n
h
an
ce
th
e
s
ec
u
r
ity
o
f
MA
NE
T
s
,
f
o
cu
s
in
g
o
n
d
if
f
er
en
t
ap
p
r
o
ac
h
es
a
n
d
m
eth
o
d
o
lo
g
ies.
T
h
is
s
ec
tio
n
d
is
cu
s
s
es
th
e
r
elate
d
wo
r
k
in
th
is
d
o
m
ain
b
y
s
u
m
m
ar
izin
g
k
ey
f
in
d
in
g
s
f
r
o
m
p
r
ev
io
u
s
r
esear
ch
ef
f
o
r
ts
.
T
h
e
r
ev
iew
h
ig
h
lig
h
ts
th
e
p
r
o
g
r
ess
m
ad
e
in
s
ec
u
r
in
g
MA
NE
T
s
.
I
n
o
n
e
s
t
u
d
y
b
y
A
l
m
o
m
a
n
i
e
t
a
l
.
[
1
3
]
,
a
c
o
m
p
a
r
i
s
o
n
o
f
v
a
r
i
o
u
s
m
a
c
h
i
n
e
l
ea
r
n
i
n
g
c
l
a
s
s
i
f
i
e
r
s
wa
s
c
o
n
d
u
c
t
e
d
t
o
i
d
e
n
t
i
f
y
t
h
e
m
o
s
t
e
f
f
ec
t
i
v
e
o
n
e
f
o
r
i
n
t
r
u
s
i
o
n
d
e
t
e
c
t
i
o
n
i
n
M
AN
E
T
s
.
T
h
e
s
tu
d
y
f
o
u
n
d
t
h
a
t
t
h
e
r
a
n
d
o
m
f
o
r
e
s
t
(
RF
)
c
l
ass
i
f
i
e
r
o
u
t
p
e
r
f
o
r
m
e
d
t
h
e
o
t
h
e
r
s
,
a
c
h
ie
v
i
n
g
a
n
a
c
c
u
r
a
c
y
r
a
t
e
o
f
8
7
%
.
A
d
d
i
t
i
o
n
a
l
l
y
,
t
h
e
R
F
c
l
a
s
s
i
f
ie
r
d
e
m
o
n
s
t
r
a
te
d
h
i
g
h
F
-
m
e
a
s
u
r
e
a
n
d
p
r
e
c
is
i
o
n
s
c
o
r
es
,
in
d
i
c
a
t
i
n
g
i
ts
r
o
b
u
s
t
n
es
s
i
n
d
e
t
ec
t
i
n
g
i
n
t
r
u
s
i
o
n
s
.
Ko
ch
er
an
d
Ku
m
a
r
[
1
4
]
u
tili
ze
d
m
ac
h
in
e
lear
n
in
g
m
o
d
els
tr
ain
ed
o
n
th
e
UNSW
-
NB
1
5
d
ataset
[
1
5
]
to
class
if
y
n
etwo
r
k
tr
af
f
ic.
T
h
e
ev
alu
atio
n
in
clu
d
ed
k
-
n
ea
r
e
s
t
n
eig
h
b
o
r
s
(
k
-
NN
)
,
s
to
ch
asti
c
g
r
ad
ien
t
d
escen
t
(
SGD)
,
d
ec
is
io
n
tr
ee
(
DT
)
,
r
a
n
d
o
m
f
o
r
est
(
R
F)
,
lo
g
is
tic
r
eg
r
ess
io
n
(
LR
)
,
an
d
n
aïv
e
B
ay
es
(
NB
)
clas
s
if
ier
s
.
Am
o
n
g
th
ese,
th
e
RF
class
if
ie
r
ac
h
iev
ed
t
h
e
h
ig
h
est p
er
f
o
r
m
an
ce
,
with
an
ac
c
u
r
ac
y
r
ate
o
f
9
5
.
4
3
%.
I
n
an
o
th
er
s
tu
d
y
,
Sar
a
n
y
a
et
a
l.
[
1
6
]
co
n
d
u
cte
d
a
c
o
m
p
r
e
h
en
s
iv
e
s
u
r
v
ey
o
n
i
n
tr
u
s
io
n
d
etec
tio
n
u
s
in
g
m
ac
h
in
e
lear
n
in
g
al
g
o
r
ith
m
s
.
T
h
ey
ev
alu
ated
th
e
p
er
f
o
r
m
an
ce
o
f
lin
ea
r
d
is
cr
im
in
an
t
an
aly
s
is
(
L
DA)
,
class
if
icatio
n
an
d
r
eg
r
ess
io
n
t
r
ee
(
C
AR
T
)
,
an
d
R
F
tech
n
iq
u
es
o
n
th
e
KDD'
9
9
C
u
p
d
atase
t
[
1
7
]
.
T
h
e
r
esu
lts
s
h
o
wed
th
at
th
e
R
F
alg
o
r
ith
m
o
u
tp
er
f
o
r
m
ed
t
h
e
o
th
er
te
ch
n
iq
u
es,
ac
h
iev
i
n
g
an
im
p
r
ess
iv
e
ac
cu
r
ac
y
o
f
9
9
.
8
1
%,
co
m
p
ar
ed
to
L
DA'
s
9
8
.
1
%
an
d
C
AR
T
's
9
8
%.
T
h
is
s
tu
d
y
h
ig
h
lig
h
ts
th
e
ef
f
e
ctiv
en
ess
o
f
R
F
in
in
tr
u
s
io
n
d
etec
tio
n
task
s
an
d
it
s
p
o
ten
tial su
p
er
io
r
ity
o
v
er
o
th
er
co
m
m
o
n
ly
u
s
ed
alg
o
r
ith
m
s
.
L
aq
tib
et
a
l
[
1
8
]
c
o
n
d
u
cted
a
s
y
s
tem
atic
co
m
p
ar
is
o
n
o
f
th
r
ee
in
tr
u
s
io
n
d
etec
tio
n
s
y
s
tem
s
b
ased
o
n
th
e
I
n
ce
p
tio
n
ar
ch
itectu
r
e
i
n
ce
p
tio
n
c
o
n
v
o
lu
tio
n
al
n
eu
r
al
n
etwo
r
k
(
C
NN)
,
b
id
ir
ec
tio
n
al
lo
n
g
s
h
o
r
t
-
ter
m
m
em
o
r
y
(
B
L
STM
)
,
an
d
d
ee
p
b
elief
n
etwo
r
k
(
DB
N)
.
T
h
e
y
u
s
ed
th
e
NSL
-
KDD
d
ataset
[
1
9
]
,
w
h
ich
in
clu
d
es
d
ata
o
n
b
o
th
in
tr
u
s
io
n
s
an
d
n
o
r
m
al
n
etwo
r
k
co
n
n
ec
tio
n
s
.
T
h
eir
s
tu
d
y
aim
ed
to
o
f
f
er
f
o
u
n
d
atio
n
al
g
u
id
an
ce
o
n
ch
o
o
s
in
g
a
p
p
r
o
p
r
iate
d
ee
p
lea
r
n
in
g
m
o
d
els f
o
r
MA
NE
T
en
v
ir
o
n
m
en
ts
.
Acc
o
r
d
in
g
to
Ab
r
ar
et
a
l
.
i
n
[
2
0
]
,
v
ar
io
u
s
m
ac
h
in
e
lear
n
in
g
class
if
ier
s
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
in
e
(
SVM)
,
k
-
NN,
L
R
,
NB
,
m
u
lti
-
lay
er
p
er
ce
p
tr
o
n
(
ML
P),
R
F,
e
x
tr
a
-
tr
ee
class
if
ier
(
E
T
C
)
,
an
d
DT
wer
e
u
tili
ze
d
to
class
if
y
d
ata
as
n
o
r
m
al
o
r
in
tr
u
s
iv
e.
T
h
eir
m
o
d
el
p
er
f
o
r
m
an
ce
was
ev
alu
ated
u
s
in
g
f
o
u
r
f
ea
tu
r
e
s
u
b
s
ets
ex
tr
ac
ted
f
r
o
m
th
e
NSL
-
KDD
d
ataset.
E
m
p
ir
ical
r
esu
lts
s
h
o
wed
th
at
R
F,
E
T
C
,
an
d
DT
ac
h
iev
ed
p
er
f
o
r
m
an
ce
ab
o
v
e
9
9
% f
o
r
all
attac
k
class
es a
cr
o
s
s
d
if
f
er
en
t f
ea
tu
r
e
s
u
b
s
ets.
Alan
g
ar
i
[
2
1
]
p
r
o
p
o
s
ed
an
ad
v
an
ce
d
h
y
b
r
id
ize
d
o
p
tim
iz
atio
n
tech
n
iq
u
e
(
AHGFFA)
th
at
u
s
es
u
n
s
u
p
er
v
is
ed
m
ac
h
in
e
lear
n
in
g
to
en
h
an
ce
s
ec
u
r
ity
in
MA
NE
T
-
in
ter
n
et
o
f
th
in
g
s
(
I
o
T
)
s
en
s
o
r
s
y
s
tem
s
.
T
h
is
m
eth
o
d
in
co
r
p
o
r
ates
s
ec
u
r
e
ce
r
tific
ate
-
b
ased
g
r
o
u
p
f
o
r
m
at
io
n
(
SC
GF)
an
d
r
ec
o
m
m
en
d
ed
ac
tio
n
K
-
m
ea
n
s
(K
-
R
F
m
ea
n
s
)
f
ilter
in
g
to
o
r
g
an
ize
th
e
n
etwo
r
k
i
n
to
g
r
o
u
p
s
an
d
o
p
tim
ize
r
o
u
tin
g
b
ased
o
n
tr
u
s
t
ca
lcu
latio
n
s
.
T
h
e
co
m
b
i
n
atio
n
o
f
g
e
n
etic
an
d
Fire
f
ly
alg
o
r
ith
m
s
in
AHGFFA
en
s
u
r
es saf
e
an
d
ef
f
icien
t r
o
u
tin
g
.
T
h
e
s
tu
d
y
in
[
2
2
]
in
tr
o
d
u
ce
s
a
m
o
d
el
th
at
lev
er
ag
es th
e
p
ar
ti
cle
b
ee
co
lo
n
y
s
war
m
(
PB
C
S)
alg
o
r
ith
m
f
o
r
e
f
f
icien
t
r
o
u
tin
g
a
n
d
in
t
eg
r
ates
th
e
h
y
b
r
id
A
d
aBo
o
s
t
-
RF
alg
o
r
ith
m
to
r
e
d
u
ce
tr
ain
in
g
tim
e
wh
ile
en
h
an
cin
g
ac
cu
r
ac
y
.
T
o
p
r
ev
e
n
t
attac
k
s
,
th
e
m
o
d
el
e
m
p
lo
y
s
th
e
ad
h
o
c
o
n
-
d
em
an
d
d
is
tan
ce
v
ec
to
r
(
AODV
)
p
r
o
to
co
l,
w
h
ich
is
a
wid
ely
u
s
ed
ap
p
r
o
ac
h
i
n
MA
NE
T
s
.
T
h
e
m
o
d
el'
s
p
er
f
o
r
m
an
ce
is
th
o
r
o
u
g
h
ly
ev
al
u
ated
u
s
in
g
v
ar
io
u
s
m
etr
ics,
in
clu
d
in
g
ac
cu
r
ac
y
,
r
ec
all,
p
r
ec
is
io
n
,
en
er
g
y
co
n
s
u
m
p
tio
n
,
an
d
n
etwo
r
k
life
tim
e,
d
em
o
n
s
tr
atin
g
its
ef
f
ec
tiv
e
n
ess
in
b
o
th
s
ec
u
r
ity
a
n
d
r
eso
u
r
ce
m
an
ag
em
e
n
t.
T
h
e
s
e
s
t
u
d
i
es
p
r
o
v
i
d
e
v
a
l
u
a
b
l
e
i
n
s
i
g
h
ts
i
n
t
o
t
h
e
a
p
p
li
c
a
ti
o
n
o
f
m
a
c
h
i
n
e
l
ea
r
n
i
n
g
t
e
c
h
n
i
q
u
es
to
e
n
h
a
n
c
e
t
h
e
s
e
c
u
r
i
t
y
o
f
M
AN
E
T
s
.
T
h
e
y
d
e
m
o
n
s
t
r
a
te
t
h
e
e
f
f
e
c
ti
v
e
n
e
s
s
o
f
v
a
r
i
o
u
s
c
la
s
s
i
f
ie
r
s
a
n
d
d
e
t
e
c
ti
o
n
m
e
t
h
o
d
s
,
s
h
o
w
c
as
i
n
g
t
h
e
p
o
t
e
n
ti
a
l
o
f
th
e
s
e
a
p
p
r
o
a
c
h
e
s
i
n
a
d
d
r
e
s
s
i
n
g
s
e
c
u
r
i
t
y
c
h
a
ll
e
n
g
e
s
.
B
u
il
d
i
n
g
u
p
o
n
t
h
i
s
e
x
i
s
t
i
n
g
r
e
s
e
a
r
c
h
,
o
u
r
s
t
u
d
y
m
a
k
es
a
s
ig
n
i
f
i
c
a
n
t
c
o
n
t
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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N
:
2
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8
8
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I
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p
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g
,
Vo
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15
,
No
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2
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20
25
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2
1
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2140
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t
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r
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a
n
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i
m
p
l
e
m
e
n
t
a
ti
o
n
.
2
.
2
.
B
la
ck
ho
le
a
t
t
a
ck
in M
ANE
T
I
n
a
b
lac
k
h
o
le
attac
k
,
a
m
a
licio
u
s
n
o
d
e
m
a
n
ip
u
lates
th
e
r
o
u
tin
g
p
r
o
to
co
l
to
attr
ac
t
a
ll
n
etwo
r
k
tr
af
f
ic
to
war
d
s
its
elf
,
u
ltima
t
ely
d
r
o
p
p
in
g
th
e
p
ac
k
ets.
T
h
is
ac
tio
n
cr
ea
tes
a
“
b
lack
h
o
le
”
in
th
e
n
etwo
r
k
,
wh
er
e
d
ata
is
ab
s
o
r
b
ed
an
d
lo
s
t,
d
is
r
u
p
tin
g
co
m
m
u
n
icatio
n
[
4
]
,
[
2
3
]
.
T
h
e
attac
k
ex
p
l
o
its
th
e
tr
u
s
t
-
b
ased
n
atu
r
e
o
f
r
o
u
tin
g
p
r
o
to
c
o
ls
,
p
a
r
ticu
lar
ly
in
MA
NE
T
s
.
Sp
ec
if
ically
,
th
e
m
alicio
u
s
n
o
d
e
s
en
d
s
a
f
alse
r
o
u
te
r
ep
ly
(
R
R
E
P)
p
ac
k
et
to
th
e
s
o
u
r
ce
n
o
d
e,
f
alsely
claim
in
g
th
at
it
h
as
th
e
s
h
o
r
test
p
ath
to
th
e
d
esti
n
atio
n
.
T
h
is
d
ec
eitf
u
l
ac
tio
n
tak
es
ad
v
a
n
ta
g
e
o
f
p
r
o
to
co
ls
lik
e
AODV
[
2
4
]
,
wh
er
e
n
o
d
es
d
e
p
en
d
o
n
r
ec
eiv
e
d
r
o
u
te
r
ep
li
es
to
estab
lis
h
p
ath
s
f
o
r
d
ata
tr
an
s
m
is
s
io
n
.
T
h
e
s
o
u
r
ce
n
o
d
e,
u
n
awa
r
e
o
f
th
e
m
alicio
u
s
in
ten
t,
th
e
n
f
o
r
war
d
s
all
its
d
ata
p
ac
k
ets
to
th
e
att
ac
k
er
.
T
h
e
attac
k
er
,
in
s
tead
o
f
f
o
r
war
d
in
g
th
ese
p
ac
k
ets,
d
r
o
p
s
th
em
o
r
k
ee
p
s
th
em
,
ef
f
ec
tiv
ely
is
o
latin
g
th
e
s
o
u
r
ce
n
o
d
e
f
r
o
m
th
e
r
est
o
f
th
e
n
etwo
r
k
an
d
ca
u
s
i
n
g
a
b
r
ea
k
d
o
wn
in
c
o
m
m
u
n
icatio
n
.
T
h
e
m
ec
h
a
n
is
m
o
f
th
e
b
lack
h
o
le
attac
k
is
illu
s
tr
ated
in
Fig
u
r
e
1
.
Fig
u
r
e
1
.
T
h
e
b
lack
h
o
le
attac
k
3.
M
ACH
I
N
E
L
E
AR
NING
A
L
G
O
RIT
H
M
S F
O
R
SE
C
URI
T
Y
L
R
[
2
5
]
is
a
s
tatis
tical
m
eth
o
d
u
s
ed
f
o
r
p
r
e
d
ictiv
e
m
o
d
elin
g
.
I
t
is
a
g
en
e
r
alize
d
lin
ea
r
m
o
d
el
th
at
is
u
s
ed
to
m
o
d
el
th
e
r
elatio
n
s
h
i
p
b
etwe
en
a
b
i
n
ar
y
o
u
tco
m
e
v
ar
iab
le
an
d
o
n
e
o
r
m
o
r
e
in
d
ep
en
d
en
t
v
ar
iab
les.
T
h
e
o
u
tco
m
e
v
ar
iab
le
is
a
b
i
n
ar
y
v
ar
iab
le
(
also
k
n
o
wn
as
a
d
ep
en
d
e
n
t
v
ar
ia
b
le)
th
at
ca
n
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e
o
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o
n
e
o
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two
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o
s
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ib
le
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alu
es,
s
u
ch
as
“
y
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”
o
r
“
no
”
,
“
tr
u
e
”
o
r
“
f
alse
”
,
o
r
“
1
”
o
r
“
0
”
as sh
o
wn
in
Fig
u
r
e
2
.
Fig
u
r
e
2
.
L
o
g
is
tic
r
eg
r
ess
io
n
(
L
R
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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&
C
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p
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n
g
I
SS
N:
2088
-
8
7
0
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C
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s
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tio
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…
(
Ho
u
d
a
Mo
u
d
n
i
)
2141
DT
class
if
ier
[
2
6
]
is
a
s
u
p
e
r
v
is
ed
lear
n
in
g
alg
o
r
ith
m
u
s
e
d
in
class
if
icatio
n
p
r
o
b
lem
s
.
I
t
cr
ea
tes
a
tr
ee
-
lik
e
m
o
d
el
o
f
d
ec
is
io
n
s
a
n
d
th
eir
p
o
s
s
ib
le
o
u
tc
o
m
es,
in
clu
d
in
g
p
r
ed
ictio
n
s
o
f
th
e
o
u
t
p
u
t
v
alu
e
.
T
h
e
tr
ee
is
co
n
s
tr
u
cted
b
y
r
ec
u
r
s
iv
ely
s
p
litt
in
g
th
e
d
ata
in
t
o
s
u
b
s
ets
b
ased
o
n
th
e
v
alu
es
o
f
th
e
i
n
p
u
t
f
ea
t
u
r
es,
with
ea
ch
s
p
lit
lead
in
g
to
a
s
p
ec
if
i
c
d
ec
is
io
n
o
r
p
r
ed
ictio
n
.
T
h
e
f
in
al
p
r
ed
ictio
n
s
ar
e
m
ad
e
b
y
tr
av
er
s
in
g
th
e
tr
ee
f
r
o
m
t
h
e
r
o
o
t
to
a
leaf
n
o
d
e
.
T
h
e
g
o
al
is
to
cr
ea
te
a
m
o
d
el
th
at
co
r
r
ec
tly
class
if
ies
th
e
m
ajo
r
ity
o
f
th
e
tr
ain
i
n
g
ex
am
p
les an
d
g
e
n
er
alize
s
well
to
n
ew,
u
n
s
ee
n
e
x
am
p
les.
I
t is
a
tr
ee
-
s
tr
u
ctu
r
ed
class
if
ier
,
w
h
er
e
in
ter
n
al
n
o
d
es
r
ep
r
esen
t
th
e
f
ea
tu
r
es
o
f
a
d
ataset,
b
r
an
ch
es
r
ep
r
esen
t
th
e
d
ec
is
io
n
r
u
les,
an
d
ea
ch
leaf
n
o
d
e
r
ep
r
esen
ts
th
e
o
u
tco
m
e
as sh
o
wn
i
n
Fig
u
r
e
3
.
R
F
[
2
7
]
is
an
en
s
em
b
le
lear
n
in
g
m
eth
o
d
u
s
ed
f
o
r
class
if
icatio
n
,
r
eg
r
ess
io
n
,
an
d
o
th
e
r
task
s
.
I
t
co
n
s
tr
u
cts
m
u
ltip
le
DT
d
u
r
i
n
g
tr
ain
in
g
an
d
o
u
t
p
u
ts
th
e
class
th
at
is
th
e
m
o
d
e
o
f
t
h
e
class
es
(
f
o
r
class
if
icatio
n
)
o
r
th
e
m
ea
n
p
r
ed
ictio
n
(
f
o
r
r
eg
r
ess
io
n
)
o
f
th
e
in
d
iv
id
u
al
t
r
ee
s
.
As
a
r
esu
lt
o
f
th
is
co
m
b
in
atio
n
,
th
e
f
o
r
est
u
s
u
ally
ac
h
iev
es a
b
etter
p
r
ed
i
ctiv
e
ac
cu
r
ac
y
th
an
an
y
in
d
iv
i
d
u
al
tr
ee
.
T
h
e
k
ey
d
i
f
f
er
en
ce
b
etwe
en
a
DT
an
d
a
R
F
is
th
at
in
a
R
F,
a
r
a
n
d
o
m
s
u
b
s
et
o
f
th
e
f
ea
t
u
r
es
is
ch
o
s
en
f
o
r
ea
ch
s
p
lit,
in
ad
d
itio
n
t
o
a
r
an
d
o
m
s
am
p
le
o
f
th
e
d
ata.
T
h
is
in
tr
o
d
u
ce
s
r
an
d
o
m
n
ess
in
to
th
e
m
o
d
el,
wh
i
ch
h
elp
s
to
r
ed
u
ce
o
v
e
r
f
itti
n
g
an
d
im
p
r
o
v
e
th
e
o
v
er
all
ac
c
u
r
ac
y
o
f
th
e
m
o
d
el
.
R
F
is
b
ased
o
n
th
e
c
o
n
ce
p
t
o
f
e
n
s
em
b
le
lear
n
in
g
,
wh
ich
i
n
v
o
lv
es
c
o
m
b
in
i
n
g
m
u
ltip
le
class
if
ier
s
to
s
o
lv
e
a
co
m
p
lex
p
r
o
b
lem
an
d
im
p
r
o
v
e
th
e
p
er
f
o
r
m
an
ce
o
f
t
h
e
m
o
d
el
as
s
h
o
wn
i
n
Fig
u
r
e
4
.
Fig
u
r
e
3
.
Dec
is
io
n
tr
ee
Fig
u
r
e
4
.
R
an
d
o
m
f
o
r
est (
R
F)
k
-
NN
[
2
8
]
is
an
in
s
tan
ce
-
b
ase
d
,
o
r
m
em
o
r
y
-
b
ased
,
s
u
p
e
r
v
is
ed
lear
n
in
g
alg
o
r
ith
m
.
T
h
e
b
a
s
ic
id
ea
is
to
class
if
y
a
n
ew,
u
n
s
ee
n
o
b
s
er
v
atio
n
b
y
d
eter
m
in
i
n
g
t
h
e
m
ajo
r
ity
class
o
f
its
“
k
”
cl
o
s
est
“
n
eig
h
b
o
r
s
”
in
t
h
e
f
ea
tu
r
e
s
p
ac
e.
T
h
e
n
eig
h
b
o
r
s
ar
e
d
ef
in
ed
b
y
a
d
is
tan
ce
m
etr
ic,
s
u
ch
as
E
u
clid
ea
n
d
is
tan
ce
,
wh
ich
m
ea
s
u
r
es
th
e
s
im
ilar
ity
b
etwe
en
two
o
b
s
er
v
atio
n
s
.
T
h
e
v
alu
e
o
f
“
k
”
i
s
ch
o
s
en
b
y
th
e
u
s
er
,
a
n
d
a
c
o
m
m
o
n
ch
o
ice
is
to
u
s
e
k
=5
o
r
k
=1
0
.
T
h
e
alg
o
r
ith
m
is
s
im
p
le
to
im
p
lem
en
t
b
u
t
ca
n
b
e
co
m
p
u
tatio
n
ally
ex
p
en
s
iv
e
wh
en
wo
r
k
in
g
with
lar
g
e
d
atasets
.
Ad
d
itio
n
al
ly
,
it
is
s
en
s
itiv
e
to
th
e
ch
o
ice
o
f
d
is
tan
ce
m
etr
ic
an
d
t
h
e
v
al
u
e
o
f
“
k
”
as
s
h
o
wn
in
Fig
u
r
e
5
.
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
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n
g
,
Vo
l.
15
,
No
.
2
,
Ap
r
il
20
25
:
2
1
3
8
-
2
1
4
9
2142
Fig
u
r
e
5
.
K
-
n
ea
r
est n
eig
h
b
o
r
s
(k
-
NN)
4.
P
RO
P
O
SE
D
AP
P
RO
ACH
Ou
r
p
r
o
p
o
s
ed
s
y
s
tem
p
r
esen
ts
a
n
o
v
el
a
p
p
r
o
ac
h
f
o
r
an
i
n
tr
u
s
io
n
d
etec
tio
n
s
y
s
tem
u
s
in
g
L
R
,
DT
,
R
F,
an
d
k
-
NN
alg
o
r
ith
m
s
,
im
p
lem
en
ted
in
Py
th
o
n
(
v
er
s
io
n
3
.
1
2
.
3
)
[
2
9
]
.
T
h
e
s
y
s
tem
ar
ch
it
ec
tu
r
e
co
n
s
is
ts
o
f
s
ix
d
is
tin
ct
s
tep
s
:
Step
1
.
E
s
tab
lis
h
in
g
m
o
b
ilit
y
with
in
th
e
NS
-
2
s
o
f
twar
e
[
3
0
]
to
g
e
n
er
ate
r
ea
lis
tic
s
ce
n
ar
io
s
.
T
h
is
in
v
o
lv
es
co
n
f
ig
u
r
in
g
t
h
e
m
o
v
em
en
t
p
atter
n
s
o
f
n
o
d
es
with
in
th
e
n
et
wo
r
k
to
m
im
ic
r
ea
l
-
wo
r
l
d
co
n
d
itio
n
s
,
s
u
ch
as
v
ar
y
in
g
s
p
ee
d
s
,
p
au
s
es,
an
d
tr
ajec
to
r
ies.
B
y
ac
cu
r
ately
s
im
u
latin
g
n
o
d
e
m
o
b
ilit
y
,
w
e
ca
n
b
etter
u
n
d
er
s
tan
d
h
o
w
th
e
n
etwo
r
k
b
eh
av
es
u
n
d
er
d
if
f
er
e
n
t
cir
cu
m
s
tan
ce
s
an
d
h
o
w
it
is
af
f
ec
te
d
b
y
p
o
ten
tial
in
tr
u
s
io
n
s
.
Step
2
.
Gen
e
r
atin
g
b
o
th
n
o
r
m
al
an
d
m
alicio
u
s
n
o
d
es
in
th
e
MA
NE
T
u
s
in
g
th
e
NS
-
2
s
o
f
twar
e,
r
esu
ltin
g
i
n
tr
ac
e
f
iles
an
d
n
etwo
r
k
an
im
a
to
r
(
NAM
)
f
iles
.
T
h
e
tr
ac
e
f
ile
ca
p
tu
r
es
th
e
r
e
q
u
ir
ed
d
ataset,
wh
ile
th
e
NAM
f
ile
d
escr
ib
es n
o
d
e
co
m
m
u
n
icatio
n
.
Step
3
.
E
x
tr
ac
tin
g
o
b
s
er
v
atio
n
s
f
r
o
m
th
e
NS
-
2
tr
ac
e
f
ile
t
o
s
er
v
e
as
i
n
p
u
ts
f
o
r
t
h
e
s
y
s
tem
u
s
in
g
an
AW
K
s
cr
ip
t.
T
h
is
ex
tr
ac
tio
n
p
r
o
ce
s
s
en
s
u
r
es
th
at
all
r
elev
an
t
m
e
tr
ics
ar
e
ca
p
tu
r
ed
,
p
r
o
v
id
in
g
a
d
ataset
f
o
r
s
u
b
s
eq
u
en
t a
n
aly
s
is
an
d
m
o
d
e
lin
g
with
in
th
e
in
tr
u
s
io
n
d
etec
t
io
n
s
y
s
tem
.
Step
4
.
Pre
p
r
o
ce
s
s
in
g
th
e
d
ata
in
v
o
lv
es
co
n
v
er
tin
g
ca
teg
o
r
ic
al
v
alu
es
to
n
u
m
er
ical
o
n
es,
clea
n
in
g
th
e
d
ata
b
y
r
em
o
v
in
g
r
o
ws
with
m
is
s
in
g
o
r
in
f
in
ite
v
alu
es,
a
n
d
elim
in
ati
n
g
d
u
p
licate
r
o
ws.
W
e
b
alan
c
e
th
e
d
ataset
u
s
in
g
r
an
d
o
m
u
n
d
er
s
am
p
lin
g
.
Ou
tlier
s
ar
e
th
en
r
em
o
v
ed
f
r
o
m
all
th
e
f
ea
tu
r
es,
an
d
th
e
f
ea
tu
r
es
ar
e
s
tan
d
ar
d
ized
.
Fin
ally
,
o
u
r
d
ata
s
et
is
d
iv
id
ed
in
to
tr
ain
in
g
an
d
test
s
ets.
Step
5
.
T
r
ain
in
g
th
e
I
DS sy
s
tem
u
s
in
g
L
R
,
DT
,
R
F,
an
d
K
-
N
N
alg
o
r
ith
m
s
with
th
e
tr
ain
in
g
s
et
o
f
o
u
r
d
ataset.
Step
6
.
T
esti
n
g
th
e
s
y
s
tem
'
s
p
er
f
o
r
m
a
n
ce
b
y
ev
alu
atin
g
d
ete
ctio
n
ac
cu
r
ac
y
u
s
in
g
d
if
f
er
en
t c
o
n
f
u
s
io
n
m
atr
ices
an
d
ass
ess
in
g
p
r
ec
is
io
n
,
r
ec
all
,
an
d
F1
-
s
co
r
e.
T
o
p
r
o
v
i
d
e
a
c
l
e
a
r
e
r
u
n
d
e
r
s
ta
n
d
i
n
g
o
f
o
u
r
p
r
o
p
o
s
e
d
m
e
t
h
o
d
,
t
h
e
f
o
l
l
o
w
i
n
g
f
l
o
w
c
h
a
r
t
i
n
Fi
g
u
r
e
6
s
h
o
w
s
t
h
e
s
t
e
p
-
by
-
s
t
e
p
p
r
o
c
e
s
s
o
f
o
u
r
i
n
t
r
u
s
i
o
n
d
e
t
e
c
t
i
o
n
s
y
s
t
e
m
,
f
r
o
m
s
c
e
n
ar
i
o
g
e
n
e
r
a
t
i
o
n
t
o
m
o
d
e
l
t
r
a
i
n
i
n
g
a
n
d
e
v
a
l
u
a
t
i
o
n
.
Fig
u
r
e
6
.
Flo
wch
ar
t
o
f
th
e
p
r
o
p
o
s
ed
in
tr
u
s
io
n
d
etec
tio
n
s
y
s
t
em
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
C
u
s
to
miz
ed
d
a
ta
s
et
-
b
a
s
ed
ma
ch
in
e
lea
r
n
in
g
a
p
p
r
o
a
ch
fo
r
b
la
ck
h
o
le
a
tta
ck
d
etec
tio
n
in
…
(
Ho
u
d
a
Mo
u
d
n
i
)
2143
5.
O
UR
DATA
SE
T
5
.
1
.
Da
t
a
co
llect
io
n
a
nd
prepa
ra
t
io
n
I
n
th
e
d
ata
co
llectio
n
an
d
p
r
e
p
ar
atio
n
s
tep
,
we
d
is
cu
s
s
ed
t
h
e
to
o
ls
to
b
e
u
s
ed
an
d
th
e
cr
ea
tio
n
o
f
s
ce
n
ar
io
s
.
T
h
is
in
v
o
l
v
ed
u
s
in
g
s
p
ec
if
ic
to
o
ls
an
d
tech
n
i
q
u
es
to
co
llect
d
ata
a
n
d
p
r
ep
a
r
e
it
f
o
r
f
u
r
th
er
an
aly
s
is
an
d
m
o
d
elin
g
.
Scen
ar
io
s
wer
e
cr
ea
ted
to
s
im
u
late
r
ea
l
-
wo
r
ld
co
n
d
itio
n
s
an
d
ca
p
t
u
r
e
d
if
f
e
r
en
t
b
eh
av
io
r
s
an
d
in
ter
ac
tio
n
s
in
a
MA
NE
T
.
T
h
r
o
u
g
h
o
u
t
th
e
d
ata
co
llectio
n
p
r
o
ce
s
s
,
a
r
an
g
e
o
f
to
o
l
s
wer
e
em
p
lo
y
ed
to
g
ath
er
p
er
tin
en
t
in
f
o
r
m
atio
n
,
i
n
clu
d
in
g
p
ac
k
e
t
tr
an
s
m
is
s
io
n
d
ata,
n
etwo
r
k
to
p
o
l
o
g
y
,
an
d
n
etwo
r
k
p
e
r
f
o
r
m
an
ce
m
etr
ics.
B
y
u
tili
zin
g
th
ese
to
o
ls
,
th
e
n
ec
ess
ar
y
d
ata
p
o
i
n
ts
f
o
r
a
n
aly
s
is
wer
e
ef
f
ec
tiv
ely
ca
p
t
u
r
ed
.
I
n
ad
d
itio
n
,
s
ce
n
ar
io
s
wer
e
d
esig
n
ed
an
d
s
im
u
lated
t
o
m
im
ic
r
ea
l
-
wo
r
ld
s
itu
atio
n
s
,
i
n
clu
d
in
g
d
if
f
er
en
t
n
etwo
r
k
co
n
d
itio
n
s
,
n
o
d
e
b
eh
a
v
io
r
s
,
an
d
b
lac
k
h
o
le
attac
k
s
.
T
h
is
in
v
o
lv
e
d
s
ettin
g
u
p
th
e
n
et
wo
r
k
en
v
ir
o
n
m
en
t,
co
n
f
ig
u
r
in
g
n
o
d
e
b
eh
a
v
io
r
s
,
a
n
d
g
en
er
atin
g
d
ata
th
r
o
u
g
h
s
im
u
latio
n
s
.
T
h
e
s
im
u
latio
n
s
wer
e
co
n
d
u
ct
ed
u
s
in
g
NS
-
2
(
v
-
2
.
3
5
)
n
etwo
r
k
s
im
u
lato
r
to
e
v
alu
ate
th
e
p
e
r
f
o
r
m
a
n
ce
o
f
o
u
r
p
r
o
p
o
s
ed
s
o
lu
tio
n
ag
ain
s
t
b
lack
h
o
le
n
o
d
es.
I
n
a
5
2
0
x
5
2
0
m
a
r
ea
,
2
5
n
o
d
es
wer
e
r
a
n
d
o
m
ly
d
is
tr
ib
u
ted
.
T
h
ey
ex
ec
u
ted
th
e
AODV
r
o
u
tin
g
p
r
o
t
o
co
l
b
o
th
with
o
u
t
attac
k
an
d
u
n
d
er
th
e
b
lack
h
o
le
attac
k
s
ce
n
ar
io
.
Ma
licio
u
s
n
o
d
es
wer
e
also
r
an
d
o
m
ly
d
is
tr
ib
u
ted
.
T
en
p
air
s
wer
e
s
elec
ted
r
an
d
o
m
ly
f
o
r
d
ata
co
m
m
u
n
icatio
n
,
ea
ch
tr
an
s
m
itti
n
g
at
5
1
2
b
y
te
s
p
er
s
ec
o
n
d
.
All
n
o
d
es
m
o
v
ed
ac
co
r
d
in
g
t
o
a
r
a
n
d
o
m
wa
y
p
o
in
t
m
o
d
el
with
s
p
ee
d
s
r
an
g
in
g
r
an
d
o
m
ly
f
r
o
m
0
to
3
0
m
/s
.
Ad
d
itio
n
ally
,
n
o
d
es
h
a
d
a
p
a
u
s
e
tim
e
o
f
1
0
s
ec
o
n
d
s
.
T
a
b
le
1
p
r
o
v
id
es a
s
u
m
m
ar
y
o
f
th
e
s
i
m
u
latio
n
p
ar
a
m
eter
s
.
On
ce
th
e
d
ata
was
co
llected
,
it
u
n
d
er
wen
t
a
s
er
ies
o
f
p
r
e
p
ar
atio
n
s
tep
s
to
en
s
u
r
e
its
q
u
ality
an
d
s
u
itab
ilit
y
f
o
r
a
n
aly
s
is
.
T
h
ese
s
tep
s
in
clu
d
ed
clea
n
in
g
th
e
d
ata
b
y
r
e
m
o
v
in
g
an
y
ir
r
e
lev
an
t
o
r
er
r
o
n
eo
u
s
en
tr
ies,
m
an
ag
in
g
m
is
s
in
g
v
alu
es,
an
d
co
n
v
er
tin
g
th
e
d
a
ta
in
to
an
ap
p
r
o
p
r
iate
f
o
r
m
at
f
o
r
an
aly
s
is
.
T
h
is
th
o
r
o
u
g
h
p
r
ep
ar
atio
n
was e
s
s
en
tial to
en
s
u
r
e
th
e
ac
c
u
r
ac
y
an
d
r
eliab
ilit
y
o
f
t
h
e
s
u
b
s
eq
u
e
n
t
an
aly
s
is
.
O
v
e
r
al
l,
t
h
e
d
ata
c
o
ll
ec
t
io
n
a
n
d
p
r
ep
a
r
at
io
n
p
r
o
ce
s
s
i
n
v
o
lv
ed
t
h
e
u
s
e
o
f
a
p
p
r
o
p
r
iat
e
to
o
l
s
to
c
r
e
ate
r
e
alis
t
ic
s
ce
n
ar
io
s
a
n
d
ac
cu
r
at
ely
c
a
p
t
u
r
e
th
e
d
ata
.
B
y
c
ar
ef
u
ll
y
p
r
o
c
ess
i
n
g
t
h
e
c
o
l
le
cte
d
d
ata
,
we
e
n
s
u
r
ed
t
h
a
t
it
w
as
r
ea
d
y
f
o
r
c
o
m
p
r
e
h
en
s
i
v
e
a
n
a
ly
s
is
.
T
h
is
m
et
ic
u
l
o
u
s
ap
p
r
o
ac
h
la
id
a
s
t
r
o
n
g
f
o
u
n
d
a
tio
n
f
o
r
th
e
s
tu
d
y
'
s
f
i
n
d
in
g
s
.
T
ab
le
1
.
Simu
latio
n
p
ar
am
eter
s
P
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F
ea
t
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s
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t
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n a
nd
f
ea
t
ure
ex
t
ra
ct
i
o
n
Du
r
in
g
th
e
f
ea
t
u
r
e
s
elec
tio
n
an
d
ex
tr
ac
tio
n
s
tep
,
we
a
n
aly
ze
d
th
e
b
lack
h
o
le
attac
k
i
n
d
etail
to
id
en
tify
k
ey
in
d
icato
r
s
f
o
r
ef
f
ec
tiv
e
d
etec
tio
n
.
Usi
n
g
an
awk
s
cr
ip
t,
we
p
ar
s
ed
an
d
p
r
o
ce
s
s
ed
th
e
d
ata
to
ex
tr
ac
t
r
elev
an
t
f
ea
tu
r
es.
T
h
i
s
p
r
o
ce
s
s
tr
an
s
f
o
r
m
ed
r
aw
d
ata
in
to
a
r
ef
in
ed
s
et
o
f
f
ea
tu
r
es
th
at
ca
p
tu
r
e
ess
en
tial
in
f
o
r
m
atio
n
ab
o
u
t
th
e
attac
k
.
T
h
ese
f
ea
tu
r
es
ar
e
cr
u
cial
f
o
r
b
u
ild
in
g
an
ef
f
ec
tiv
e
d
etec
tio
n
m
o
d
el,
as
th
ey
p
r
o
v
id
e
v
alu
ab
le
in
s
ig
h
ts
f
o
r
ac
cu
r
ate
class
if
icatio
n
an
d
d
etec
tio
n
o
f
t
h
e
attac
k
.
W
e
u
s
ed
th
e
f
o
llo
win
g
f
ea
tu
r
es
to
d
etec
t
th
e
b
lac
k
h
o
le
attac
k
in
MA
NE
T
:
p
ac
k
ets
s
en
t,
p
ac
k
ets
r
ec
eiv
ed
,
p
ac
k
ets
f
o
r
war
d
ed
,
p
ac
k
ets
d
r
o
p
p
e
d
,
s
en
t
r
o
u
te
r
e
q
u
est
(
R
R
E
Q
)
,
r
ec
eiv
ed
R
R
E
Q,
s
en
t
r
o
u
te
r
ep
ly
(
R
R
E
P
)
,
an
d
en
er
g
y
le
f
t.
E
ac
h
o
f
t
h
ese
f
ea
tu
r
es
was
ca
r
e
f
u
ll
y
ch
o
s
en
b
ased
o
n
its
r
elev
an
c
e
to
id
e
n
tify
in
g
th
e
b
lack
h
o
le
attac
k
.
B
elo
w
is
a
d
etailed
ex
p
lan
atio
n
o
f
ea
c
h
f
ea
tu
r
e'
s
im
p
o
r
tan
ce
.
a.
Pack
ets
s
en
t:
m
o
n
ito
r
in
g
th
e
p
ac
k
ets
s
en
t
b
y
a
n
o
d
e
h
elp
s
u
s
g
au
g
e
its
ac
tiv
ity
lev
el.
I
n
th
e
co
n
tex
t
o
f
a
b
lack
h
o
le
attac
k
,
th
e
m
alicio
u
s
n
o
d
e
m
ay
f
ail
to
f
o
r
war
d
p
ac
k
ets
it
r
ec
eiv
es,
lead
in
g
to
a
n
o
ticea
b
le
d
ec
r
ea
s
e
in
p
ac
k
et
tr
an
s
m
is
s
i
o
n
co
m
p
a
r
ed
to
th
e
o
v
er
all
n
etwo
r
k
tr
af
f
ic.
T
h
is
h
elp
s
in
id
en
tify
in
g
n
o
d
es
th
at
ar
e
b
eh
a
v
in
g
a
b
n
o
r
m
ally
,
as leg
itima
te
n
o
d
es sh
o
u
ld
h
av
e
a
co
n
s
is
ten
t r
ate
o
f
s
en
t p
ac
k
ets.
b.
Pack
ets
r
ec
eiv
ed
:
a
n
aly
zin
g
th
e
n
u
m
b
e
r
o
f
p
ac
k
ets
r
ec
eiv
ed
allo
ws
u
s
to
id
en
tify
n
o
d
es
th
at
ex
h
ib
it
u
n
u
s
u
al
b
e
h
av
io
r
.
A
s
elec
tiv
e
b
lack
h
o
le
n
o
d
e
d
r
o
p
s
o
r
ig
n
o
r
es
p
ac
k
ets,
lead
in
g
to
a
r
ed
u
ce
d
n
u
m
b
er
o
f
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
:
2
1
3
8
-
2
1
4
9
2144
r
ec
eiv
ed
p
ac
k
ets.
T
h
is
f
ea
tu
r
e
is
cr
u
cial
f
o
r
d
etec
tin
g
d
is
cr
e
p
an
cies
in
p
ac
k
et
r
ec
ep
tio
n
,
w
h
ich
ca
n
s
ig
n
al
m
alicio
u
s
ac
tiv
ity
.
c.
Pack
ets
f
o
r
war
d
ed
:
t
h
is
f
ea
tu
r
e
en
ab
les
u
s
to
o
b
s
er
v
e
th
e
p
ac
k
et
f
o
r
war
d
in
g
b
e
h
av
io
r
o
f
n
o
d
es
in
th
e
n
etwo
r
k
.
A
b
lack
h
o
le
n
o
d
e
t
y
p
ically
av
o
id
s
f
o
r
war
d
in
g
p
a
ck
ets
to
leg
itima
te
d
esti
n
atio
n
s
,
r
esu
ltin
g
in
a
lo
wer
n
u
m
b
e
r
o
f
p
ac
k
ets
f
o
r
war
d
ed
co
m
p
ar
ed
to
o
th
er
n
o
d
es.
T
h
is
f
ea
tu
r
e
is
d
ir
ec
tly
r
e
lated
to
th
e
co
r
e
b
eh
av
io
r
o
f
a
b
lack
h
o
le
attac
k
,
wh
er
e
th
e
m
alicio
u
s
n
o
d
e
d
i
s
r
u
p
ts
n
o
r
m
al
d
ata
f
lo
w.
d.
Pack
et
d
r
o
p
p
e
d
:
t
r
ac
k
in
g
th
e
n
u
m
b
er
o
f
d
r
o
p
p
e
d
p
ac
k
ets
is
ess
en
tial
f
o
r
id
en
tify
in
g
b
lac
k
h
o
le
attac
k
s
.
Ma
licio
u
s
n
o
d
es
ten
d
to
in
ten
tio
n
ally
d
r
o
p
p
ac
k
ets,
d
is
r
u
p
ti
n
g
th
e
n
o
r
m
al
f
lo
w
o
f
co
m
m
u
n
icatio
n
s
.
An
in
cr
ea
s
ed
n
u
m
b
er
o
f
d
r
o
p
p
ed
p
ac
k
ets
ca
n
in
d
icate
th
e
p
r
esen
ce
o
f
a
b
lac
k
h
o
le
n
o
d
e,
as
leg
itima
te
n
o
d
es
s
h
o
u
ld
m
ain
tain
a
lo
w
d
r
o
p
r
at
e.
e.
Fo
r
war
d
p
ac
k
et
r
atio
:
t
h
is
f
ea
tu
r
e
m
ea
s
u
r
es
th
e
p
r
o
p
o
r
tio
n
o
f
p
ac
k
ets
th
at
a
n
o
d
e
f
o
r
war
d
s
co
m
p
ar
ed
t
o
th
e
p
ac
k
ets
it
r
ec
eiv
es.
I
t
in
d
i
ca
tes
th
e
ef
f
icien
cy
an
d
r
eliab
ilit
y
o
f
a
n
o
d
e
in
f
o
r
war
d
in
g
d
ata
with
in
th
e
n
etwo
r
k
.
A
h
ig
h
f
o
r
war
d
p
ac
k
et
r
atio
s
u
g
g
ests
ef
f
ec
tiv
e
p
ac
k
et
f
o
r
war
d
in
g
,
wh
ile
a
lo
w
r
atio
m
ay
in
d
icate
is
s
u
es su
ch
as p
ac
k
et
lo
s
s
o
r
m
alicio
u
s
ac
tiv
ity
lik
e
a
b
lack
h
o
le
attac
k
.
f.
Sen
t
R
R
E
Q:
a
n
aly
zin
g
th
e
n
u
m
b
er
o
f
R
R
E
Q
p
ac
k
ets
s
en
t
b
y
a
n
o
d
e
h
el
p
s
u
s
d
eter
m
in
e
if
a
n
o
d
e
is
ac
tiv
ely
in
v
o
lv
ed
in
r
o
u
te
d
is
co
v
er
y
.
T
h
is
f
ea
tu
r
e
h
elp
s
id
en
tify
n
o
d
es
th
at
m
ay
b
e
f
ab
r
icatin
g
r
o
u
tes,
wh
ich
is
a
tactic
u
s
ed
in
b
lack
h
o
le
attac
k
s
.
g.
R
ec
eiv
ed
R
R
E
Q:
m
o
n
ito
r
in
g
th
e
r
ec
ep
tio
n
o
f
R
R
E
Q
p
ac
k
ets
allo
ws
u
s
to
ev
alu
ate
th
e
p
ar
ticip
atio
n
o
f
n
o
d
es in
th
e
r
o
u
te
d
is
co
v
er
y
p
r
o
ce
s
s
.
h.
Sen
t
R
R
E
P
:
t
r
ac
k
in
g
th
e
n
u
m
b
er
o
f
R
R
E
P
p
ac
k
ets
g
en
er
ate
d
b
y
n
o
d
es
h
elp
s
id
en
tify
n
o
d
es
th
at
m
ay
b
e
f
ab
r
icatin
g
o
r
m
o
d
if
y
in
g
R
R
E
P
p
ac
k
ets
to
m
is
lead
th
e
n
etwo
r
k
.
A
b
lack
h
o
le
n
o
d
e
ca
n
f
ab
r
icate
o
r
m
o
d
if
y
R
R
E
P p
ac
k
ets to
attr
a
ct
tr
af
f
ic
to
war
d
s
its
elf
,
ca
u
s
in
g
ab
n
o
r
m
al
p
atter
n
s
in
s
en
t RR
E
P p
ac
k
ets.
i.
E
n
er
g
y
lef
t:
m
o
n
ito
r
in
g
th
e
e
n
er
g
y
le
v
el
o
f
n
o
d
es
is
cr
u
cia
l
f
o
r
d
etec
tin
g
a
b
lac
k
h
o
le
at
tack
.
Ma
licio
u
s
n
o
d
es
m
ay
co
n
s
u
m
e
e
n
er
g
y
m
o
r
e
r
ap
id
ly
d
u
e
to
th
eir
m
alicio
u
s
ac
tiv
ities
,
s
u
ch
as
g
en
er
ati
n
g
u
n
n
ec
ess
ar
y
tr
af
f
ic
o
r
p
er
f
o
r
m
in
g
ad
d
itio
n
a
l o
p
er
atio
n
s
.
A
r
a
p
id
d
ec
lin
e
in
en
er
g
y
ca
n
i
n
d
icate
m
alicio
u
s
b
eh
av
io
r
.
6.
RE
SU
L
T
AND
DI
SCUS
SI
O
N
6
.
1
.
Da
t
a
s
et
s
t
a
t
is
t
ics o
v
er
v
iew
Fig
u
r
e
7
d
e
p
ic
ti
n
g
t
h
e
s
ta
tis
ti
cs
o
f
o
u
r
d
atas
et
,
i
n
c
lu
d
i
n
g
th
e
d
is
t
r
i
b
u
ti
o
n
o
f
a
ll
th
e
s
tu
d
i
e
d
f
e
at
u
r
es.
T
h
is
v
is
u
al
iza
ti
o
n
p
r
o
v
i
d
es
a
n
o
v
e
r
v
iew
o
f
k
ey
m
ea
s
u
r
es
s
u
c
h
as
m
ea
n
,
s
t
an
d
ar
d
d
ev
iat
io
n
,
m
e
d
ia
n
,
m
i
n
i
m
u
m
,
an
d
m
a
x
i
m
u
m
v
a
lu
es,
as
wel
l
as q
u
ar
t
iles
.
T
h
is
g
r
a
p
h
ica
l
r
e
p
r
es
en
tati
o
n
h
e
lp
s
i
n
u
n
d
e
r
s
t
a
n
d
in
g
t
h
e
d
is
t
r
i
b
u
ti
o
n
an
d
tr
e
n
d
s
o
f
t
h
e
d
at
a
in
o
u
r
d
a
taset
,
t
h
e
r
e
b
y
f
a
cili
tat
in
g
t
h
e
a
n
al
y
s
is
a
n
d
i
n
te
r
p
r
eta
ti
o
n
o
f
t
h
e
r
es
u
lts
.
Fig
u
r
e
7
.
Su
m
m
ar
y
s
tatis
tics
o
f
o
u
r
d
ataset
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
C
u
s
to
miz
ed
d
a
ta
s
et
-
b
a
s
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ma
ch
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a
p
p
r
o
a
ch
fo
r
b
la
ck
h
o
le
a
tta
ck
d
etec
tio
n
in
…
(
Ho
u
d
a
Mo
u
d
n
i
)
2145
6
.
2
.
Co
rr
ela
t
io
n m
a
t
rix
T
h
e
co
r
r
elatio
n
m
atr
i
x
d
is
p
lay
s
th
e
co
r
r
elatio
n
co
ef
f
icien
ts
b
etwe
en
d
if
f
er
en
t
v
a
r
iab
les
ass
o
ciate
d
with
p
ac
k
et
tr
an
s
m
is
s
io
n
in
a
n
etwo
r
k
.
T
h
e
v
alu
es r
an
g
e
f
r
o
m
-
1
to
1
.
A
p
o
s
itiv
e
co
r
r
elatio
n
in
d
icate
s
th
at
t
h
e
two
v
ar
iab
les
ten
d
to
in
cr
ea
s
e
o
r
d
e
cr
ea
s
e
to
g
eth
e
r
,
wh
ile
a
n
eg
ativ
e
c
o
r
r
elatio
n
m
ea
n
s
t
h
at
as
o
n
e
v
ar
iab
le
in
cr
ea
s
es,
th
e
o
th
er
d
ec
r
ea
s
es,
an
d
v
ice
v
er
s
a.
A
v
alu
e
o
f
0
im
p
lies
n
o
co
r
r
elatio
n
.
T
h
e
co
r
r
elatio
n
m
atr
ix
o
f
o
u
r
d
ataset
is
s
h
o
wn
in
Fig
u
r
e
8
.
Her
e
ar
e
s
o
m
e
im
p
o
r
tan
t f
in
d
in
g
s
:
a.
T
h
e
v
ar
iab
les
“
p
ac
k
et
s
en
t,
”
“
p
ac
k
et
r
ec
eiv
e
d
,
”
an
d
“
p
ac
k
et
d
r
o
p
p
ed
”
a
r
e
h
ig
h
ly
co
r
r
el
ated
with
ea
ch
o
th
er
(
all
v
alu
es
ar
e
ab
o
v
e
0
.
9
6
)
.
T
h
is
m
ea
n
s
th
at
wh
en
m
o
r
e
p
ac
k
ets
ar
e
s
en
t,
th
er
e
is
a
h
i
g
h
er
lik
elih
o
o
d
o
f
r
ec
eiv
in
g
an
d
d
r
o
p
p
in
g
th
e
m
.
b.
T
h
e
v
ar
iab
le
“
p
ac
k
et
f
o
r
war
d
ed
”
is
n
o
t
co
r
r
elate
d
with
“
p
a
ck
et
s
en
t
”
an
d
“
p
ac
k
et
r
ec
eiv
e
d
.
”
T
h
is
im
p
lies
th
at
n
o
t a
ll sen
t o
r
r
ec
eiv
ed
p
a
ck
ets ar
e
b
ein
g
f
o
r
war
d
ed
.
c.
T
h
e
“
f
o
r
war
d
p
ac
k
et
r
atio
”
h
as
a
s
lig
h
t
n
eg
ativ
e
co
r
r
elatio
n
with
“
p
ac
k
et
s
en
t,
”
“
p
ac
k
et
r
ec
eiv
ed
,
”
an
d
“
p
ac
k
et
d
r
o
p
p
ed
.
”
T
h
is
s
u
g
g
e
s
ts
th
at
as
th
e
n
u
m
b
er
o
f
s
en
t
,
r
ec
eiv
ed
,
o
r
d
r
o
p
p
ed
p
ac
k
et
s
in
cr
ea
s
es,
th
e
f
o
r
war
d
p
ac
k
et
r
atio
d
ec
r
ea
s
es.
d.
T
h
e
v
ar
iab
les
“
s
en
t
R
R
E
Q
”
(
r
o
u
te
r
eq
u
est)
an
d
“
r
ec
eiv
e
R
R
E
Q
”
(
r
ec
eiv
ed
r
o
u
te
r
eq
u
est)
ar
e
p
o
s
itiv
ely
co
r
r
elate
d
,
as e
x
p
ec
ted
.
e.
T
h
e
“
en
er
g
y
lef
t
”
v
ar
ia
b
le
s
h
o
ws
a
n
eg
ativ
e
c
o
r
r
elatio
n
with
p
ac
k
et
-
r
elate
d
v
ar
ia
b
les
(
p
ac
k
et
s
en
t,
p
ac
k
et
r
ec
eiv
ed
,
p
ac
k
et
d
r
o
p
p
e
d
)
,
s
u
g
g
esti
n
g
th
at
m
o
r
e
tr
an
s
m
is
s
io
n
ac
tiv
ity
r
esu
lts
in
less
r
em
ain
in
g
en
er
g
y
.
Fig
u
r
e
8
.
C
o
r
r
elatio
n
m
atr
ix
6
.
3
.
E
v
a
lua
t
io
n o
f
ma
chine le
a
rning
m
o
dels
T
h
e
co
n
f
u
s
io
n
m
atr
ix
f
o
r
th
e
f
o
u
r
m
o
d
els
-
lo
g
is
tic
r
eg
r
ess
i
o
n
,
DT
,
RF
,
an
d
k
-
NN
-
ar
e
p
r
esen
ted
in
T
ab
le
s
2
,
3
,
4
,
a
n
d
5
,
r
esp
ec
t
iv
ely
.
T
ab
le
6
p
r
o
v
id
es
th
e
e
v
alu
atio
n
m
etr
ics
f
o
r
th
e
f
o
u
r
m
ac
h
in
e
lear
n
in
g
m
o
d
els
ap
p
lied
to
class
if
y
b
lack
h
o
le
attac
k
s
u
s
in
g
th
e
c
u
s
to
m
ized
MA
NE
T
d
ataset.
T
h
e
Fig
u
r
e
9
s
h
o
wca
s
es
th
e
p
er
f
o
r
m
an
ce
o
f
f
o
u
r
m
ac
h
in
e
-
lear
n
in
g
m
o
d
els
f
o
r
o
u
r
b
in
ar
y
-
class
class
if
icatio
n
p
r
o
b
lem
.
T
h
e
r
esu
lts
d
is
p
lay
th
e
b
est
s
co
r
es
an
d
tes
t
s
co
r
es
f
o
r
ea
ch
m
o
d
el.
Fo
r
L
R
,
th
e
h
ig
h
est
s
co
r
e
ac
h
iev
e
d
was
0
.
9
6
4
,
with
a
test
s
co
r
e
o
f
0
.
9
5
1
,
d
e
m
o
n
s
tr
atin
g
g
o
o
d
p
er
f
o
r
m
a
n
ce
o
n
u
n
s
ee
n
d
ata.
T
h
e
DT
m
o
d
el
at
tain
ed
h
ig
h
s
co
r
es,
with
a
b
est
s
co
r
e
o
f
0
.
9
7
1
an
d
a
test
s
co
r
e
o
f
0
.
9
5
6
5
,
d
em
o
n
s
tr
atin
g
ex
ce
llen
t
p
er
f
o
r
m
a
n
ce
o
n
t
h
e
test
d
ata.
T
h
e
R
F
m
o
d
el
p
er
f
o
r
m
ed
wel
l,
with
a
b
est
s
co
r
e
o
f
0
.
9
8
6
an
d
a
test
s
co
r
e
o
f
0
.
9
7
7
.
T
h
is
in
d
icate
s
th
at
th
e
m
o
d
el
g
en
e
r
alize
s
ef
f
ec
tiv
ely
to
p
r
ev
io
u
s
ly
u
n
s
ee
n
d
ata
.
T
h
e
k
-
NN
m
o
d
el
ac
h
ie
v
ed
a
b
est
s
co
r
e
o
f
0
.
9
5
5
an
d
a
test
s
co
r
e
o
f
0
.
9
4
9
.
Alth
o
u
g
h
th
e
test
s
co
r
e
is
s
lig
h
tly
lo
w
er
,
it
s
till
d
em
o
n
s
tr
ates
s
atis
f
ac
to
r
y
p
e
r
f
o
r
m
an
ce
.
Ov
er
all,
th
ese
r
esu
lts
h
ig
h
lig
h
t
th
e
ef
f
ec
tiv
en
ess
o
f
m
ac
h
in
e
lear
n
in
g
m
o
d
els
in
d
ata
cla
s
s
if
icatio
n
.
T
h
e
DT
an
d
R
F
m
o
d
els
s
h
o
w
p
a
r
ticu
lar
ly
s
tr
o
n
g
p
er
f
o
r
m
a
n
ce
,
wh
ile
lo
g
is
tic
r
eg
r
ess
io
n
an
d
k
-
NN
also
p
r
o
v
id
e
r
esp
ec
tab
le
r
esu
lts
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
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2
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8
8
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I
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15
,
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2
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Ap
r
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20
25
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3
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2146
T
ab
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f
u
s
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atr
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r
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d
i
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t
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d
n
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l
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ab
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u
s
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atr
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o
r
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r
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a
l
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t
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7
9
3
3
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3
6
0
T
ab
le
4
.
C
o
n
f
u
s
io
n
m
atr
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f
o
r
R
F
P
r
e
d
i
c
t
e
d
n
o
r
ma
l
P
r
e
d
i
c
t
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d
a
t
t
a
c
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a
l
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o
r
ma
l
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6
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c
t
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a
l
a
t
t
a
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k
7
0
9
3
4
,
1
4
0
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ab
le
5
.
C
o
n
f
u
s
io
n
m
atr
ix
f
o
r
k
-
NN
P
r
e
d
i
c
t
e
d
n
o
r
ma
l
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r
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t
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5
1
6
3
2
,
5
0
1
T
ab
le
6
.
E
v
alu
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n
m
et
r
ics f
o
r
m
ac
h
in
e
lear
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in
g
m
o
d
els
M
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l
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c
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r
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LR
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0
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9
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5
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5
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9
5
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0
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9
7
0
0
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9
6
5
0
.
9
6
8
0
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9
6
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0
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9
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0
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9
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Fig
u
r
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9
.
Per
f
o
r
m
an
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e
m
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ics b
y
m
o
d
el
6
.
4
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Dis
cu
s
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io
n o
f
t
he
re
s
ults
T
h
e
p
er
f
o
r
m
a
n
ce
ev
alu
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n
o
f
th
e
m
ac
h
in
e
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n
i
n
g
m
o
d
els
in
o
u
r
s
tu
d
y
d
em
o
n
s
tr
ates
th
at
RF
,
lo
g
is
tic
r
eg
r
ess
io
n
,
k
-
n
ea
r
est
n
eig
h
b
o
r
s
,
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d
DT
m
o
d
els
ca
n
class
if
y
b
lack
h
o
le
attac
k
s
in
MA
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T
s
with
a
r
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n
ab
le
d
eg
r
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o
f
ac
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r
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n
p
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ticu
lar
,
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e
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o
d
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h
iev
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th
e
h
ig
h
est
s
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r
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in
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icatin
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o
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g
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ize
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T
h
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u
lts
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n
d
er
s
co
r
e
th
e
p
o
ten
tial
o
f
th
ese
m
o
d
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to
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f
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tiv
ely
d
etec
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lac
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alig
n
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g
with
o
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r
p
r
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a
r
y
o
b
jectiv
e
t
o
en
h
a
n
ce
MA
N
E
T
s
ec
u
r
ity
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
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&
C
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p
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I
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N:
2088
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r
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la
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h
o
le
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tta
ck
d
etec
tio
n
in
…
(
Ho
u
d
a
Mo
u
d
n
i
)
2147
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itio
n
s
an
d
c
h
allen
g
es
s
p
ec
if
ic
to
MA
NE
T
en
v
ir
o
n
m
en
ts
.
T
h
is
h
as
allo
wed
u
s
to
o
b
tain
r
esu
lt
s
th
at
ar
e
m
o
r
e
a
p
p
licab
le
t
o
r
ea
l
-
wo
r
ld
s
ce
n
a
r
io
s
.
T
h
e
s
tr
en
g
t
h
o
f
o
u
r
ap
p
r
o
ac
h
is
n
o
t
o
n
ly
i
n
th
e
s
elec
tio
n
o
f
ef
f
ec
tiv
e
m
ac
h
in
e
lear
n
i
n
g
m
o
d
els,
s
u
ch
as
DT
an
d
RF
,
b
u
t
also
in
th
e
r
elev
a
n
ce
an
d
s
p
ec
if
icity
o
f
o
u
r
d
ata,
wh
ich
en
h
an
ce
s
th
e
m
o
d
els'
p
er
f
o
r
m
an
ce
an
d
r
eliab
ilit
y
.
Ho
wev
er
,
a
lim
itatio
n
o
f
o
u
r
s
tu
d
y
is
th
at
th
e
d
at
aset
m
ay
n
o
t
en
co
m
p
ass
all
p
o
s
s
ib
le
v
ar
iatio
n
s
o
f
attac
k
p
atter
n
s
.
Fu
tu
r
e
wo
r
k
c
o
u
ld
in
v
o
lv
e
ex
p
a
n
d
in
g
t
h
e
d
a
taset
with
m
o
r
e
d
iv
er
s
e
s
ce
n
ar
io
s
o
r
co
m
b
in
in
g
it
with
o
th
er
d
atasets
to
f
u
r
t
h
er
i
m
p
r
o
v
e
th
e
r
o
b
u
s
tn
ess
o
f
th
e
m
o
d
els.
I
n
s
u
m
m
ar
y
,
o
u
r
s
tu
d
y
p
r
o
v
i
d
es
v
alu
ab
le
in
s
ig
h
ts
in
to
th
e
ap
p
licatio
n
o
f
m
ac
h
in
e
lear
n
in
g
m
o
d
els,
p
ar
ticu
lar
ly
DT
an
d
RF
,
f
o
r
d
etec
tin
g
b
lack
h
o
le
attac
k
s
in
MA
NE
T
s
.
T
h
ese
f
in
d
in
g
s
co
n
tr
ib
u
te
to
th
e
b
r
o
ad
e
r
f
ield
o
f
n
etwo
r
k
s
ec
u
r
ity
b
y
d
em
o
n
s
tr
atin
g
th
e
f
ea
s
ib
ilit
y
o
f
d
ep
lo
y
in
g
m
ac
h
in
e
lear
n
in
g
-
b
ased
in
tr
u
s
io
n
d
etec
tio
n
s
y
s
tem
s
in
r
eso
u
r
ce
-
co
n
s
tr
ain
ed
en
v
ir
o
n
m
en
ts
.
Fu
tu
r
e
r
esear
ch
co
u
ld
ex
p
an
d
o
n
th
is
wo
r
k
b
y
ex
p
lo
r
in
g
th
e
in
teg
r
atio
n
o
f
th
ese
m
o
d
els
with
o
th
e
r
s
ec
u
r
ity
m
ec
h
an
is
m
s
in
M
ANE
T
s
,
as
well
as
in
v
esti
g
atin
g
th
eir
p
e
r
f
o
r
m
an
c
e
in
m
o
r
e
d
iv
er
s
e
an
d
d
y
n
am
i
c
n
etwo
r
k
s
ce
n
ar
i
o
s
.
7.
CO
NCLU
SI
O
N
I
n
co
n
clu
s
io
n
,
o
u
r
p
r
o
p
o
s
ed
ap
p
r
o
ac
h
to
d
esig
n
in
g
an
in
t
r
u
s
io
n
d
etec
tio
n
s
y
s
tem
f
o
r
b
lack
h
o
le
attac
k
s
in
MA
NE
T
s
h
as
d
e
m
o
n
s
tr
ated
its
ef
f
ec
tiv
e
n
ess
th
r
o
u
g
h
co
m
p
r
e
h
en
s
iv
e
d
ata
co
llectio
n
,
f
ea
tu
r
e
s
elec
tio
n
,
an
d
m
o
d
el
e
v
alu
ati
o
n
.
T
h
e
r
esu
lts
o
b
tain
ed
f
r
o
m
tr
ain
in
g
an
d
e
v
alu
atin
g
v
ar
io
u
s
m
ac
h
in
e
lear
n
in
g
m
o
d
els,
in
clu
d
in
g
RF
,
lo
g
is
tic
r
eg
r
ess
io
n
,
k
-
n
ea
r
est
n
ei
g
h
b
o
r
s
,
a
n
d
DT
,
u
n
d
er
s
co
r
e
th
e
p
o
ten
tial
o
f
th
ese
m
o
d
els
in
ac
cu
r
ately
class
if
y
in
g
b
lack
h
o
le
attac
k
s
.
Par
ticu
lar
ly
,
th
e
DT
an
d
RF
m
o
d
els
ex
ce
lled
,
s
h
o
wca
s
in
g
th
eir
ab
ilit
y
to
g
e
n
er
alize
well
to
u
n
s
ee
n
d
ata,
wh
ich
is
cr
u
cia
l f
o
r
r
ea
l
-
w
o
r
ld
d
e
p
lo
y
m
e
n
t.
T
h
e
f
in
d
i
n
g
s
o
f
o
u
r
s
tu
d
y
h
av
e
s
ig
n
if
ican
t
im
p
licatio
n
s
f
o
r
t
h
e
f
ield
o
f
n
etwo
r
k
s
ec
u
r
ity
,
p
ar
ticu
lar
ly
in
th
e
c
o
n
tex
t
o
f
MA
NE
T
s
.
B
y
d
em
o
n
s
tr
atin
g
th
e
f
ea
s
ib
ilit
y
o
f
a
p
p
ly
in
g
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
s
to
d
etec
t
b
lack
h
o
le
attac
k
s
,
we
co
n
tr
ib
u
te
to
th
e
o
n
g
o
in
g
ef
f
o
r
ts
to
en
h
an
ce
th
e
s
ec
u
r
ity
an
d
r
esil
ien
ce
o
f
th
ese
n
etwo
r
k
s
.
T
h
is
r
esear
ch
lay
s
th
e
g
r
o
u
n
d
wo
r
k
f
o
r
d
e
v
elo
p
in
g
m
o
r
e
s
o
p
h
is
ticated
in
tr
u
s
io
n
d
etec
tio
n
s
y
s
tem
s
th
at
ca
n
ad
ap
t t
o
th
e
ev
o
lv
in
g
n
atu
r
e
o
f
n
etwo
r
k
th
r
ea
ts
.
Mo
v
in
g
f
o
r
war
d
,
o
u
r
wo
r
k
o
p
en
s
s
ev
er
al
av
e
n
u
es
f
o
r
f
u
tu
r
e
r
esear
ch
.
O
n
e
p
o
ten
tial
ap
p
licatio
n
is
th
e
in
teg
r
atio
n
o
f
o
u
r
in
t
r
u
s
io
n
d
etec
tio
n
s
y
s
tem
with
o
t
h
er
s
ec
u
r
ity
m
ec
h
an
is
m
s
,
s
u
ch
as
en
cr
y
p
tio
n
a
n
d
an
o
m
aly
d
etec
tio
n
,
to
c
r
ea
te
a
m
o
r
e
co
m
p
r
eh
e
n
s
iv
e
d
ef
e
n
s
e
s
tr
ateg
y
f
o
r
MA
NE
T
s
.
Ad
d
it
io
n
ally
,
e
x
p
an
d
in
g
th
e
d
ataset
to
in
clu
d
e
a
b
r
o
ad
er
r
an
g
e
o
f
attac
k
s
ce
n
ar
io
s
a
n
d
test
in
g
t
h
e
m
o
d
els
in
m
o
r
e
d
y
n
am
ic
an
d
lar
g
e
-
s
ca
le
en
v
ir
o
n
m
e
n
ts
co
u
ld
f
u
r
th
er
v
alid
ate
an
d
en
h
an
ce
th
e
r
o
b
u
s
tn
ess
o
f
o
u
r
ap
p
r
o
ac
h
.
I
n
s
u
m
m
a
r
y
,
o
u
r
f
in
d
in
g
s
p
r
o
v
id
e
a
s
o
lid
f
o
u
n
d
atio
n
f
o
r
f
u
r
t
h
er
r
esear
ch
a
n
d
d
ev
el
o
p
m
en
t
i
n
s
ec
u
r
in
g
MA
NE
T
s
,
with
th
e
p
o
ten
tial
to
s
ig
n
if
ican
tly
im
p
a
ct
th
e
f
ield
an
d
co
n
tr
ib
u
te
t
o
th
e
b
r
o
a
d
er
c
o
m
m
u
n
ity
'
s
ef
f
o
r
ts
in
s
af
eg
u
ar
d
i
n
g
cr
itical
co
m
m
u
n
icatio
n
in
f
r
ast
r
u
ctu
r
es.
RE
F
E
R
E
NC
E
S
[
1
]
D
.
R
a
m
p
h
u
l
l
,
A
.
M
u
n
g
u
r
,
S
.
A
r
mo
o
g
u
m
,
a
n
d
S
.
P
u
d
a
r
u
t
h
,
“
A
r
e
v
i
e
w
o
f
mo
b
i
l
e
a
d
h
o
c
n
e
t
w
o
r
k
(
M
A
N
ET)
p
r
o
t
o
c
o
l
s
a
n
d
t
h
e
i
r
a
p
p
l
i
c
a
t
i
o
n
s,
”
i
n
2
0
2
1
5
t
h
I
n
t
e
rn
a
t
i
o
n
a
l
C
o
n
f
e
re
n
c
e
o
n
I
n
t
e
l
l
i
g
e
n
t
C
o
m
p
u
t
i
n
g
a
n
d
C
o
n
t
r
o
l
S
y
st
e
m
s
(
I
C
I
C
C
S
)
,
I
EEE,
M
a
y
2
0
2
1
,
p
p
.
2
0
4
–
211
,
d
o
i
:
1
0
.
1
1
0
9
/
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C
I
C
C
S
5
1
1
4
1
.
2
0
2
1
.
9
4
3
2
2
5
8
.
[
2
]
M
.
A
l
R
u
b
a
i
e
i
,
H
.
s
h
J
a
ssi
m
,
B
.
T.
S
h
a
r
e
f
,
S
.
S
a
f
d
a
r
,
Z.
T.
S
h
a
r
e
f
,
a
n
d
F
.
L.
M
a
l
a
l
l
a
h
,
“
C
u
r
r
e
n
t
v
u
l
n
e
r
a
b
i
l
i
t
i
e
s
,
c
h
a
l
l
e
n
g
e
s
a
n
d
a
t
t
a
c
k
s o
n
r
o
u
t
i
n
g
p
r
o
t
o
c
o
l
s
f
o
r
mo
b
i
l
e
a
d
h
o
c
n
e
t
w
o
r
k
:
a
r
e
v
i
e
w
,
”
i
n
S
w
a
r
m
I
n
t
e
l
l
i
g
e
n
c
e
f
o
r R
e
s
o
u
rce
M
a
n
a
g
e
m
e
n
t
i
n
I
n
t
e
rn
e
t
o
f
T
h
i
n
g
s
,
E
l
se
v
i
e
r
,
2
0
2
0
,
p
p
.
1
0
9
–
1
2
9
,
d
o
i
:
1
0
.
1
0
1
6
/
B
9
7
8
-
0
-
12
-
8
1
8
2
8
7
-
1
.
0
0
0
1
2
-
7.
[
3
]
B
.
B
a
n
e
r
j
e
e
a
n
d
S
.
N
e
o
g
y
,
“
A
b
r
i
e
f
o
v
e
r
v
i
e
w
o
f
sec
u
r
i
t
y
a
t
t
a
c
k
s
a
n
d
p
r
o
t
o
c
o
l
s
i
n
M
A
N
ET
,
”
i
n
2
0
2
1
I
EE
E
1
8
t
h
I
n
d
i
a
C
o
u
n
c
i
l
I
n
t
e
r
n
a
t
i
o
n
a
l
C
o
n
f
e
r
e
n
c
e
(
I
N
D
I
C
O
N
)
,
I
EEE,
D
e
c
.
2
0
2
1
,
p
p
.
1
–
6
,
d
o
i
:
1
0
.
1
1
0
9
/
I
N
D
I
C
O
N
5
2
5
7
6
.
2
0
2
1
.
9
6
9
1
5
5
4
.
[
4
]
H
.
M
o
u
d
n
i
,
M
.
Er
-
R
o
u
i
d
i
,
H
.
M
o
u
n
c
i
f
,
a
n
d
B
.
El
H
a
d
a
d
i
,
“
P
e
r
f
o
r
ma
n
c
e
a
n
a
l
y
si
s
o
f
A
O
D
V
r
o
u
t
i
n
g
p
r
o
t
o
c
o
l
i
n
M
A
N
ET
u
n
d
e
r
t
h
e
i
n
f
l
u
e
n
c
e
o
f
r
o
u
t
i
n
g
a
t
t
a
c
k
s,
”
i
n
2
0
1
6
I
n
t
e
rn
a
t
i
o
n
a
l
C
o
n
f
e
r
e
n
c
e
o
n
El
e
c
t
ri
c
a
l
a
n
d
I
n
f
o
rm
a
t
i
o
n
T
e
c
h
n
o
l
o
g
i
e
s
(
I
C
EI
T
)
,
I
EEE,
M
a
y
2
0
1
6
,
p
p
.
5
3
6
–
5
4
2
,
d
o
i
:
1
0
.
1
1
0
9
/
EI
Te
c
h
.
2
0
1
6
.
7
5
1
9
6
5
8
.
[
5
]
F
.
H
a
mza
a
n
d
S
.
M
a
r
i
a
C
e
l
e
s
t
i
n
V
i
g
i
l
a
,
“
R
e
v
i
e
w
o
f
m
a
c
h
i
n
e
l
e
a
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