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Wi
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a
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ise
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ro
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t
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k
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WS
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p
e
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ifi
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ll
y
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v
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stig
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te
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e
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n
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a
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s
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Bo
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d
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e
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o
r
(KN
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l
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c
h
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th
th
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lg
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m
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x
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a
c
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l
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n
d
i
n
terp
re
tab
il
it
y
.
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h
is
st
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d
y
c
o
n
tri
b
u
tes
t
o
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n
h
a
n
c
i
n
g
th
e
se
c
u
rit
y
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n
d
re
li
a
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f
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o
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ts
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te
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t
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iza
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s
a
n
d
a
lg
o
rit
h
m
ic ex
p
l
o
ra
ti
o
n
s fo
r
ro
b
u
st Do
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a
tt
a
c
k
d
e
tec
ti
o
n
.
K
ey
w
o
r
d
s
:
Dec
is
io
n
tr
ee
alg
o
r
ith
m
Do
S
attac
k
s
E
x
tr
em
e
g
r
a
d
ien
t b
o
o
s
tin
g
Gin
i f
ea
tu
r
e
s
elec
tio
n
m
eth
o
d
KNN
class
if
ier
s
R
an
d
o
m
f
o
r
est
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ir
eles
s
s
en
s
o
r
n
etwo
r
k
s
T
h
is i
s
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n
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p
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n
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c
c
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ss
a
rticle
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n
d
e
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e
CC B
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SA
li
c
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se
.
C
o
r
r
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s
p
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A
uth
o
r
:
Ven
k
atesh
B
ac
h
u
Dep
ar
tm
en
t o
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o
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p
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ter
Scie
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d
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B
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Hy
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ad
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r
W
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en
Hy
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ab
ad
,
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n
d
ia
E
m
ail: v
en
k
atesh
.
cse8
8
@
g
m
a
il.c
o
m
1.
I
NT
RO
D
UCT
I
O
N
Sev
er
al
in
d
u
s
tr
ies
h
av
e
b
eg
u
n
to
r
el
y
o
n
wir
eless
s
en
s
o
r
n
etwo
r
k
s
(
W
SNs
)
as
a
co
r
e
t
ec
h
n
o
lo
g
y
,
in
clu
d
in
g
s
m
ar
t
cities,
h
ea
lth
ca
r
e,
in
d
u
s
tr
ial
au
to
m
atio
n
,
an
d
e
n
v
ir
o
n
m
en
tal
m
o
n
ito
r
in
g
.
T
h
ese
n
etwo
r
k
s
tr
ac
k
en
v
i
r
o
n
m
e
n
tal
v
ar
ia
b
les
s
u
ch
p
o
llu
tio
n
lev
els,
h
u
m
i
d
ity
,
a
n
d
tem
p
er
atu
r
e
u
s
in
g
a
n
etwo
r
k
o
f
au
to
n
o
m
o
u
s
s
en
s
o
r
s
s
p
r
ea
d
o
u
t
o
v
er
th
e
wo
r
l
d
.
A
ce
n
t
r
al
p
r
o
ce
s
s
in
g
u
n
it
r
ec
eiv
es
th
e
d
ata
th
at
h
as
b
ee
n
co
llected
.
W
SNs
ar
e
h
ig
h
ly
im
p
o
r
tan
t
f
o
r
r
ea
l
-
tim
e
d
ata
g
ath
er
in
g
an
d
an
aly
s
is
d
u
e
to
th
eir
d
ec
en
tr
alize
d
n
atu
r
e
an
d
ca
p
a
b
ilit
y
to
f
u
n
ctio
n
in
h
ar
s
h
an
d
in
ac
ce
s
s
ib
le
s
itu
atio
n
s
[
1
]
.
Nev
er
th
eless
,
th
e
wid
e
s
p
r
ea
d
im
p
lem
en
tatio
n
o
f
W
SNs
als
o
b
r
i
n
g
s
ab
o
u
t
s
ec
u
r
ity
wea
k
n
ess
es,
p
ar
ticu
lar
ly
in
r
elatio
n
to
d
en
ial
-
of
-
s
er
v
ice
(
Do
S)
ass
au
lts
,
wh
ich
p
r
o
v
id
e
a
s
u
b
s
tan
tial
r
i
s
k
.
Do
S
atta
ck
s
h
av
e
th
e
o
b
jectiv
e
o
f
im
p
ed
in
g
th
e
r
eg
u
lar
f
u
n
ctio
n
in
g
o
f
a
n
etwo
r
k
b
y
in
u
n
d
atin
g
it
with
a
s
u
b
s
tan
tial
am
o
u
n
t
o
f
m
ale
v
o
len
t
d
a
ta,
th
u
s
m
ak
i
n
g
it
u
n
attain
ab
le
f
o
r
au
th
o
r
ized
u
s
er
s
.
W
ith
in
th
e
r
ea
lm
o
f
W
SN
s
,
Do
S
as
s
au
lts
ca
n
r
esu
lt
in
s
ig
n
if
ic
an
t
r
am
if
icatio
n
s
,
en
c
o
m
p
ass
in
g
t
h
e
lo
s
s
o
f
d
ata,
d
ete
r
io
r
atio
n
o
f
s
er
v
ices,
b
o
d
ily
h
ar
m
,
an
d
f
in
an
cial
s
etb
ac
k
s
[
2
]
.
C
o
n
s
eq
u
en
tl
y
,
p
r
o
tectin
g
th
e
av
ailab
ilit
y
,
in
teg
r
ity
,
an
d
r
eliab
ilit
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o
f
s
en
s
o
r
d
ata
in
W
SN
s
r
eq
u
ir
es
th
e
ab
ilit
y
to
d
etec
t
a
n
d
m
itig
ate
D
o
S
attac
k
s
.
Ad
v
a
n
ce
d
Do
S
attac
k
s
s
o
m
etim
es
ex
p
lo
it
v
u
ln
er
ab
ilit
ies
in
th
e
u
n
d
er
ly
i
n
g
p
r
o
t
o
co
ls
o
f
n
etw
o
r
k
s
an
d
th
e
lim
ited
ca
p
ac
ity
o
f
in
d
iv
i
d
u
al
s
en
s
o
r
n
o
d
es,
r
en
d
er
in
g
tr
ad
itio
n
al
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
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J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
A
s
imp
le
ma
ch
in
e
lea
r
n
in
g
tech
n
iq
u
e
f
o
r
s
en
s
o
r
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etw
o
r
k
w
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r
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…
(
S
h
a
ik
A
b
d
u
l H
a
mee
d
)
1691
s
ec
u
r
ity
m
ea
s
u
r
es
lik
e
as
a
u
th
en
ticatio
n
a
n
d
e
n
cr
y
p
tio
n
in
ef
f
ec
tiv
e.
Acc
o
r
d
i
n
g
ly
,
r
o
b
u
s
t
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d
ef
f
icien
t
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tr
u
s
io
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etec
tio
n
s
y
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tem
s
th
at
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n
d
etec
t
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d
m
itig
ate
Do
S
attac
k
s
in
W
SNs
ar
e
in
h
ig
h
d
em
an
d
[
3
]
.
I
n
th
is
s
tu
d
y
,
we
in
tr
o
d
u
ce
a
s
tr
ai
g
h
tf
o
r
war
d
m
ac
h
in
e
lear
n
in
g
ap
p
r
o
ac
h
to
d
etec
t
Do
S
attac
k
s
in
W
SNs
.
T
o
ef
f
icien
tly
h
an
d
le
h
ig
h
-
d
im
en
s
io
n
al
d
ata
wh
ile
m
in
im
izin
g
C
PU u
tili
za
tio
n
,
we
em
p
lo
y
d
ec
is
io
n
tr
ee
(
DT
)
tech
n
iq
u
es
with
Gin
i
f
ea
tu
r
e
s
elec
tio
n
,
r
a
n
d
o
m
f
o
r
est
(
R
F),
ex
tr
em
e
g
r
a
d
ien
t
b
o
o
s
tin
g
(
XGBo
o
s
t)
,
an
d
k
-
n
ea
r
est
n
eig
h
b
o
r
(
KNN)
class
if
ier
s
.
W
e
wan
t
to
im
p
r
o
v
e
W
SN
s
s
ec
u
r
it
y
an
d
in
tr
u
s
io
n
d
etec
tio
n
.
T
esti
n
g
Do
S
d
etec
tio
n
m
eth
o
d
s
will
d
o
th
is
[
4
]
.
W
SNs
u
s
e
r
u
le
-
b
as
ed
,
an
o
m
aly
-
b
ased
,
an
d
m
ac
h
in
e
lear
n
in
g
-
b
ased
Do
S
d
etec
tio
n
m
eth
o
d
o
l
o
g
ies.
R
u
le
-
b
ased
s
y
s
tem
s
th
at
u
s
e
p
r
ed
eter
m
in
e
d
s
ig
n
atu
r
es
o
r
cr
iter
ia
to
d
etec
t
m
alicio
u
s
b
eh
a
v
io
r
m
a
y
f
ail
a
g
ain
s
t
n
ew
attac
k
s
.
So
m
e
n
etwo
r
k
an
o
m
aly
d
etec
tio
n
m
et
h
o
d
s
h
av
e
lar
g
e
f
alse
p
o
s
itiv
e
r
ates.
Data
-
d
r
iv
en
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
s
ca
n
ass
ess
n
etwo
r
k
tr
a
f
f
ic
p
atter
n
s
an
d
d
etec
t
Do
S
attac
k
s
[
5
]
.
W
SN
s
ar
e
u
s
ed
in
en
v
ir
o
n
m
e
n
tal
m
o
n
ito
r
in
g
,
h
e
alth
ca
r
e,
an
d
in
d
u
s
tr
ial
au
to
m
atio
n
.
Do
S
attac
k
s
ar
e
p
o
s
s
ib
le
in
d
ec
en
tr
alize
d
an
d
lim
ited
-
r
eso
u
r
ce
W
SNs
.
T
h
is
s
ec
tio
n
r
ev
iews
W
SN
D
o
S
attac
k
d
etec
tio
n
s
tu
d
ies,
f
o
cu
s
in
g
o
n
n
etwo
r
k
s
ec
u
r
ity
an
d
r
esil
ien
ce
[
6
]
.
R
u
le
-
b
ased
Do
S
attac
k
d
ete
ctio
n
in
W
SNs
f
in
d
s
an
d
s
to
p
s
m
alicio
u
s
ac
tiv
ity
u
s
in
g
s
p
ec
if
ied
s
ig
n
atu
r
es
o
r
cr
iter
ia.
T
h
ese
m
eth
o
d
s
ass
u
m
e
d
ev
ian
t
b
e
h
av
io
r
d
if
f
er
s
f
r
o
m
n
etwo
r
k
tr
af
f
ic.
R
u
le
-
b
ased
tech
n
iq
u
es
ar
e
ea
s
y
t
o
b
u
ild
an
d
in
ter
p
r
et,
b
u
t
th
ey
m
ay
n
o
t
s
ca
le
well
to
n
ew
attac
k
s
tr
ateg
ies.
Du
e
to
m
is
id
en
tific
atio
n
o
f
in
n
o
ce
n
t
ev
en
ts
,
th
ey
m
ay
m
is
s
ad
v
an
c
ed
o
r
co
m
p
licated
attac
k
s
[
7
]
.
An
o
m
aly
d
etec
tio
n
s
y
s
tem
s
d
etec
t
an
d
war
n
a
b
o
u
t
n
etwo
r
k
a
n
o
m
alies,
in
clu
d
in
g
Do
S
ass
au
lts
.
T
h
is
in
clu
d
es
s
tatis
t
ical,
m
ac
h
in
e
lear
n
in
g
,
a
n
d
clu
s
ter
in
g
m
et
h
o
d
s
.
Ou
tlier
s
a
r
e
id
en
tifie
d
u
s
in
g
s
tatis
tical
ap
p
r
o
ac
h
es
in
n
etwo
r
k
t
r
af
f
ic
an
aly
s
is
.
Ma
ch
in
e
lear
n
in
g
al
g
o
r
ith
m
s
lik
e
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
in
es
(
SVMs
)
an
d
ar
tific
i
al
n
eu
r
al
n
etwo
r
k
s
(
ANNs
)
lear
n
to
r
ec
o
g
n
ize
n
o
r
m
al
an
d
u
n
u
s
u
al
b
e
h
av
io
r
b
y
u
s
in
g
lab
ele
d
tr
ain
in
g
d
at
a.
C
lu
s
ter
in
g
g
r
o
u
p
s
s
im
ilar
n
etwo
r
k
tr
af
f
ic
p
atte
r
n
s
an
d
d
is
co
v
er
s
an
o
m
alo
u
s
o
n
es
[
8
]
.
An
o
m
aly
d
etec
tio
n
h
elp
s
ad
ap
t
to
ch
an
g
in
g
n
etwo
r
k
en
v
ir
o
n
m
e
n
ts
an
d
f
i
n
d
n
ew
attac
k
s
.
I
n
W
SN
s
with
lim
ited
r
eso
u
r
ce
s
,
th
ey
m
a
y
h
a
v
e
h
i
g
h
f
alse
p
o
s
itiv
e
r
ates
an
d
r
e
q
u
ir
e
m
o
r
e
p
r
o
ce
s
s
in
g
p
o
wer
.
T
r
ain
in
g
d
ata
q
u
ality
an
d
ch
a
r
a
cter
izatio
n
f
ea
tu
r
es
af
f
ec
t
an
o
m
aly
d
etec
tio
n
[
9
]
.
R
ec
en
t
s
tu
d
ies
h
av
e
em
p
lo
y
ed
m
ac
h
in
e
lear
n
in
g
to
d
et
ec
t
Do
S
attac
k
s
in
wir
eless
s
en
s
o
r
n
etwo
r
k
s
.
Ma
ch
in
e
lear
n
i
n
g
tech
n
iq
u
es
a
u
t
o
m
ate
f
ea
tu
r
e
ex
tr
ac
tio
n
,
h
a
n
d
le
n
o
is
y
d
ata,
a
n
d
ad
ap
t
to
ch
an
g
in
g
ass
au
lt
co
n
d
itio
n
s
.
DT
m
eth
o
d
s
lik
e
R
F
an
d
g
r
ad
ien
t
b
o
o
s
tin
g
m
ac
h
in
es
(
GB
Ms)
ar
e
p
o
p
u
lar
b
ec
au
s
e
th
ey
a
r
e
s
im
p
le,
ea
s
y
to
lear
n
,
a
n
d
s
u
cc
ess
f
u
l
with
m
u
ltid
im
en
s
io
n
al
d
ata
[
1
0
]
.
E
n
s
em
b
le
ap
p
r
o
ac
h
es
lik
e
RF
u
s
e
n
u
m
er
o
u
s
DT
to
im
p
r
o
v
e
class
if
icatio
n
an
d
g
en
e
r
aliza
tio
n
.
X
GB
o
o
s
t
o
p
tim
izes
r
eg
u
lar
izatio
n
a
n
d
p
ar
alleliza
tio
n
to
im
p
r
o
v
e
DT
e
n
s
em
b
le
p
er
f
o
r
m
an
ce
.
KNN
class
if
ier
s
class
if
y
n
etwo
r
k
tr
af
f
ic
b
y
t
h
e
s
im
ilar
ity
o
f
n
e
ar
b
y
d
ata
p
o
in
ts
[
1
1
]
,
[
1
2
]
.
M
ac
h
in
e
lear
n
in
g
alg
o
r
it
h
m
s
f
o
r
W
SN
Do
S
attac
k
d
etec
tio
n
p
r
o
v
id
e
g
r
ea
t
ac
cu
r
a
cy
,
co
m
p
u
tatio
n
al
ef
f
icien
cy
,
an
d
s
ca
lab
ilit
y
,
ac
co
r
d
in
g
to
m
u
ltip
le
r
esear
ch
es
.
Op
tim
izin
g
m
o
d
el
p
ar
a
m
eter
s
,
co
r
r
ec
tin
g
class
d
is
tr
ib
u
tio
n
im
b
alan
ce
,
an
d
ad
a
p
tin
g
ap
p
r
o
ac
h
es
to
W
SNs
r
eso
u
r
ce
lim
its
ar
e
s
till
n
ee
d
ed
[
1
3
]
.
T
h
e
liter
atu
r
e
r
e
v
iew
d
is
cu
s
s
es
W
SN
Do
S
d
etec
tio
n
u
s
in
g
m
ac
h
in
e
lear
n
in
g
to
im
p
r
o
v
e
n
etwo
r
k
s
ec
u
r
ity
an
d
r
esil
ien
ce
.
Dis
tr
ib
u
ted
d
en
ial
o
f
s
er
v
ice
(
DDo
S
)
attac
k
s
in
W
SNs
ca
n
b
e
d
etec
ted
u
s
in
g
DT
alg
o
r
ith
m
s
,
Gin
i
f
ea
tu
r
e
s
elec
tio
n
,
RF
,
XG
B
o
o
s
t
,
an
d
KNN
class
if
ier
s
[
1
4
]
,
[
1
5
]
.
W
SN D
o
S a
ttack
s
ar
e
id
en
tifi
ed
an
d
m
itig
ated
ef
f
icien
tly
a
n
d
ef
f
ec
tiv
ely
u
s
in
g
th
e
m
et
h
o
d
.
2.
M
E
T
H
O
D
I
n
th
is
s
ec
tio
n
,
we
d
em
o
n
s
tr
at
e
h
o
w
to
u
s
e
a
lig
h
tweig
h
t
m
a
ch
in
e
lear
n
in
g
ap
p
r
o
ac
h
to
id
e
n
tify
Do
S
attac
k
s
in
W
SNs
.
Pre
p
r
o
ce
s
s
in
g
an
d
d
ata
co
llectio
n
ar
e
p
ar
t
o
f
th
e
s
u
g
g
ested
m
eth
o
d
o
lo
g
y
.
Ma
ch
in
e
lear
n
in
g
class
if
ier
s
lik
e
KNN,
DT
alg
o
r
ith
m
s
with
th
e
Gin
i
f
ea
tu
r
e
s
elec
tio
n
m
eth
o
d
,
R
F,
a
n
d
XG
B
o
o
s
t
ar
e
also
u
s
ed
[
1
6
]
.
First
s
tep
s
in
im
p
lem
en
t
in
g
th
e
s
u
g
g
ested
m
eth
o
d
o
l
o
g
y
in
clu
d
e
co
llectin
g
s
tatis
tics
o
n
n
etwo
r
k
tr
af
f
ic
f
r
o
m
W
SNs
u
n
d
er
b
o
t
h
n
o
r
m
al
o
p
er
atin
g
s
ettin
g
s
an
d
s
im
u
lated
Do
S
attac
k
s
ce
n
ar
io
s
.
T
h
e
d
ata
g
ath
er
i
n
g
p
r
o
ce
d
u
r
e
g
ath
er
s
a
r
an
g
e
o
f
n
etwo
r
k
ac
tiv
ity
v
ar
ia
b
les,
s
u
ch
as
p
ac
k
et
s
ize,
p
ac
k
et
r
ate,
en
er
g
y
u
s
ag
e
,
an
d
co
m
m
u
n
icatio
n
p
atter
n
s
.
Af
te
r
th
e
d
ata
is
g
ath
er
ed
,
p
r
e
-
p
r
o
ce
s
s
in
g
m
eth
o
d
s
ar
e
e
m
p
lo
y
ed
to
r
ea
d
y
it
f
o
r
an
aly
s
is
u
s
in
g
m
ac
h
in
e
lear
n
i
n
g
[
1
7
]
.
Stan
d
ar
d
ize
th
e
f
ea
tu
r
es
to
en
s
u
r
e
u
n
if
o
r
m
ity
in
th
eir
s
ca
les
an
d
ea
s
e
co
n
v
er
g
en
ce
d
u
r
i
n
g
m
o
d
el
tr
ain
in
g
.
T
w
o
o
f
ten
u
s
ed
n
o
r
m
aliza
tio
n
ap
p
r
o
ac
h
es
ar
e
m
in
-
m
ax
s
ca
lin
g
an
d
z
-
s
co
r
e
n
o
r
m
aliza
tio
n
.
C
h
o
o
s
e
p
er
tin
en
t
c
h
ar
ac
ter
is
tics
th
at
ar
e
h
ig
h
ly
in
s
tr
u
c
ti
v
e
in
d
if
f
er
en
tiatin
g
b
etwe
en
ty
p
ical
an
d
aty
p
ical
n
etwo
r
k
a
ctiv
ity
.
Featu
r
e
s
elec
tio
n
s
tr
at
eg
ies,
s
u
ch
as
th
e
Gin
i
f
ea
tu
r
e
s
elec
tio
n
m
eth
o
d
,
ca
n
b
e
u
s
ed
to
d
eter
m
in
e
th
e
m
o
s
t
d
is
tin
g
u
is
h
in
g
tr
aits
.
R
es
o
lv
e
th
e
p
r
o
b
lem
o
f
im
b
alan
c
ed
class
d
is
tr
ib
u
tio
n
by
en
s
u
r
in
g
th
at
th
e
d
ataset
in
clu
d
es
an
eq
u
al
r
ep
r
esen
tatio
n
o
f
b
o
th
n
o
r
m
al
o
cc
u
r
r
en
c
es
an
d
in
s
tan
ce
s
o
f
Do
S
attac
k
s
.
Me
th
o
d
s
s
u
ch
as
o
v
er
s
am
p
lin
g
,
u
n
d
er
s
am
p
lin
g
,
o
r
s
y
n
th
etic
d
ata
g
en
e
r
atio
n
ca
n
b
e
em
p
lo
y
ed
to
ac
h
iev
e
a
b
alan
ce
d
d
ataset.
B
y
p
r
ep
ar
in
g
th
e
d
ata
in
t
h
is
way
,
we
m
ak
e
s
u
r
e
t
h
at
th
e
in
p
u
t
to
th
e
m
ac
h
in
e
lear
n
in
g
class
if
ier
s
is
co
r
r
ec
tly
p
r
ep
ar
e
d
an
d
o
p
tim
ized
f
o
r
ef
f
icien
t m
o
d
el
tr
ain
i
n
g
an
d
ev
al
u
atio
n
[
1
8
]
.
Data
p
r
e
-
p
r
o
ce
s
s
in
g
is
f
o
llo
w
ed
b
y
tr
ain
in
g
an
d
test
in
g
m
a
ch
in
e
lear
n
in
g
class
if
ier
s
to
d
etec
t
W
S
N
Do
S
attac
k
s
.
Ou
r
m
eth
o
d
co
n
s
id
er
s
f
o
u
r
class
if
ier
s
:
s
im
p
le
b
u
t
s
tr
o
n
g
class
if
icatio
n
alg
o
r
ith
m
s
th
at
p
ar
titi
o
n
f
ea
tu
r
e
s
p
ac
e
u
s
in
g
s
eq
u
en
tial
d
ec
is
io
n
r
u
les
ar
e
DT
tech
n
iq
u
es
with
Gin
i
f
ea
tu
r
e
s
elec
tio
n
.
T
h
e
DT
m
o
d
el
’
s
d
is
cr
im
in
ato
r
y
p
o
wer
is
in
cr
e
ased
b
y
s
elec
tin
g
th
e
m
o
s
t
in
f
o
r
m
ativ
e
f
ea
tu
r
es
at
ea
ch
d
ec
i
s
io
n
n
o
d
e
u
s
in
g
th
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
8
,
No
.
3
,
J
u
n
e
20
2
5
:
1
6
9
0
-
1
6
9
7
1692
Gin
i
f
ea
tu
r
e
s
elec
tio
n
ap
p
r
o
a
ch
[
1
9
]
.
E
n
s
em
b
le
lear
n
in
g
m
eth
o
d
RF
m
ix
es
m
an
y
d
ec
is
i
o
n
tr
ee
s
to
in
cr
ea
s
e
class
if
icatio
n
ac
cu
r
ac
y
an
d
r
o
b
u
s
tn
ess
.
RF
r
ed
u
ce
s
o
v
er
f
itti
n
g
an
d
im
p
r
o
v
es
m
o
d
el
g
en
e
r
aliza
tio
n
b
y
p
o
o
lin
g
d
ec
is
io
n
tr
ee
p
r
ed
ictio
n
s
[
2
0
]
.
T
h
e
s
ca
lab
le
an
d
ef
f
icien
t
g
r
ad
ien
t
b
o
o
s
tin
g
alg
o
r
ith
m
XGBo
o
s
t
iter
ativ
ely
cr
ea
tes
an
en
s
em
b
le
o
f
wea
k
lear
n
er
s
to
m
ax
im
ize
a
d
if
f
er
en
tiab
le
lo
s
s
f
u
n
cti
o
n
.
XG
B
o
o
s
t
ac
h
iev
es
to
p
class
if
i
ca
tio
n
p
er
f
o
r
m
a
n
ce
u
s
i
n
g
r
e
g
u
lar
izatio
n
an
d
p
ar
alleli
za
tio
n
[
2
1
]
.
N
o
n
-
p
a
r
am
etr
ic
4
k
-
KNN
class
if
ier
s
class
if
y
d
ata
p
o
in
ts
b
y
th
e
m
ajo
r
ity
v
o
te
o
f
t
h
eir
n
ea
r
e
s
t
n
eig
h
b
o
r
s
in
f
ea
tu
r
e
s
p
ac
e.
KNN
is
ea
s
y
to
im
p
lem
en
t
a
n
d
d
o
es
n
o
t
r
eq
u
ir
e
m
o
d
el
tr
ain
i
n
g
,
m
ak
in
g
it
s
u
ited
f
o
r
r
ea
l
-
tim
e
W
SN
Do
S
attac
k
d
etec
tio
n
[
2
2
]
.
T
h
ese
m
ac
h
in
e
lear
n
in
g
class
if
ier
s
ar
e
tr
ain
ed
o
n
th
e
p
r
ep
r
o
ce
s
s
ed
d
ataset
an
d
t
ested
f
o
r
ac
cu
r
ac
y
,
p
r
ec
is
io
n
,
r
ec
all,
an
d
F1
-
s
co
r
e
u
s
in
g
cr
o
s
s
-
v
alid
atio
n
[
2
3
]
.
W
e
u
s
e
th
ese
m
ac
h
i
n
e
lear
n
in
g
m
o
d
els
to
cr
ea
te
a
lig
h
tweig
h
t,
r
ea
l
-
tim
e
W
SN D
o
S a
ttack
d
etec
tio
n
s
y
s
tem
th
a
t m
itig
ates
s
ec
u
r
ity
v
u
ln
er
ab
ili
ties
.
L
ater
s
ec
t
io
n
s
g
iv
e
ex
p
e
r
im
en
tal
d
ata
a
n
d
p
e
r
f
o
r
m
a
n
ce
ev
alu
atio
n
o
f
th
e
p
r
o
p
o
s
ed
m
eth
o
d
o
l
o
g
y
[
2
4
]
,
[
2
5
]
.
Ou
r
r
esear
ch
m
eth
o
d
co
m
b
in
e
s
s
tan
d
ar
d
m
ac
h
in
e
lear
n
in
g
al
g
o
r
ith
m
s
ca
r
ef
u
lly
c
h
o
s
en
f
o
r
th
eir
h
ig
h
im
p
ac
t
an
d
co
n
s
id
er
ab
le
c
o
n
tr
ib
u
tio
n
to
id
en
tify
i
n
g
n
u
m
er
o
u
s
s
ec
u
r
ity
v
u
ln
er
ab
ilit
i
es,
esp
ec
ially
Do
S
ass
au
lts
.
T
est
s
an
d
tr
ain
in
g
u
s
ed
th
e
W
NS
-
DS
d
ataset,
wh
ich
co
m
p
r
is
es
f
o
u
r
Do
S
attac
k
s
.
Featu
r
e
s
elec
tio
n
im
p
r
o
v
e
d
class
if
icatio
n
ac
cu
r
ac
y
an
d
r
e
d
u
ce
d
p
r
o
ce
s
s
in
g
c
o
s
t
with
in
th
e
d
ataset.
T
h
is
s
t
u
d
y
u
s
es
XGBo
o
s
t,
R
F,
KNN,
an
d
DT
class
if
ier
s
.
All
clas
s
if
ier
s
wer
e
tr
ain
ed
an
d
e
v
alu
ated
u
s
in
g
1
8
-
f
ea
tu
r
e
W
SN
-
DS
an
d
16
-
f
ea
tu
r
e
en
h
a
n
ce
d
v
er
s
io
n
.
B
y
r
ep
o
r
tin
g
b
o
th
d
atasets
’
ac
cu
r
ac
y
,
th
e
r
esu
ltin
g
d
a
taset
is
ef
f
icien
t.
Acc
ep
tan
ce
o
f
all
m
o
d
els
r
eq
u
ir
es
v
alid
atio
n
.
W
e
u
s
ed
1
0
-
f
o
ld
cr
o
s
s
v
alid
atio
n
f
o
r
ea
ch
m
o
d
el
in
o
u
r
tr
ials
to
ac
q
u
ir
e
r
eliab
le
r
esu
lts
.
Mo
d
el
class
if
icatio
n
ac
cu
r
ac
y
was c
alcu
lated
u
s
in
g
(
1
).
=
(
+
)
/
(
+
+
+
)
(
1
)
T
r
u
e
p
o
s
itiv
e
(
T
P)
a
n
d
tr
u
e
n
eg
ativ
e
(
T
N)
s
h
o
w
c
o
r
r
ec
tly
an
ticip
ated
p
o
s
itiv
e
an
d
n
eg
at
iv
e
ca
s
es.
Fals
e
n
eg
ativ
es (
FN)
ar
e
p
o
s
itiv
e
ca
s
es m
is
clas
s
if
ied
as n
eg
ativ
e,
wh
ile
f
alse p
o
s
itiv
es (
FP
)
ar
e
n
eg
ativ
e
ca
s
es
m
is
class
if
ied
as
p
o
s
itiv
e.
A
co
n
f
u
s
io
n
m
atr
ix
ev
al
u
ates
m
o
d
el
p
er
f
o
r
m
an
ce
an
d
ef
f
e
ctiv
en
ess
.
Me
asu
r
ed
class
if
icatio
n
er
r
o
r
s
wer
e
f
alse n
eg
ativ
es (
FN)
an
d
f
alse p
o
s
itiv
es (
FP
)
.
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
Ou
r
n
ew
m
ac
h
in
e
lear
n
in
g
a
p
p
r
o
ac
h
to
d
etec
tin
g
Do
S
ass
au
lts
in
W
SN
s
is
d
etailed
h
er
e,
alo
n
g
with
its
r
esu
lts
an
d
ass
ess
m
en
t.
I
n
s
im
u
lated
W
SN
en
v
ir
o
n
m
e
n
ts
,
we
ev
al
u
ate
h
o
w
well
d
if
f
er
en
t
m
eth
o
d
s
d
etec
t
Do
S
attac
k
s
.
Am
o
n
g
th
ese
tec
h
n
iq
u
es a
r
e
KNN
class
if
ier
s
,
XGBo
o
s
t,
R
F,
an
d
DT
with
Gin
i f
ea
tu
r
e
s
elec
tio
n
.
Her
e
is
a
s
u
m
m
ar
y
o
f
th
e
p
e
r
f
o
r
m
an
ce
m
etr
ics
o
f
t
h
e
m
ac
h
i
n
e
lear
n
in
g
class
if
ier
s
f
o
r
Do
S
attac
k
d
etec
tio
n
in
W
SN
s
,
as sh
o
wn
in
T
ab
le
1
.
T
ab
le
1
.
Per
f
o
r
m
an
ce
m
etr
ics o
f
m
ac
h
in
e
lear
n
in
g
class
if
ier
s
f
o
r
D
o
S a
ttack
d
etec
tio
n
in
W
SNs
C
l
a
s
si
f
i
e
r
A
c
c
u
r
a
c
y
P
r
e
c
i
s
i
o
n
R
e
c
a
l
l
F1
-
sc
o
r
e
DT
0
.
9
2
0
.
9
1
0
.
9
2
0
.
9
1
RF
0
.
8
9
0
.
8
8
0
.
9
0
0
.
9
0
X
G
B
o
o
st
0
.
9
1
0
.
9
0
0
.
9
1
0
.
9
1
K
N
N
0
.
8
6
0
.
8
5
0
.
8
7
0
.
8
7
T
h
e
o
u
tco
m
es
p
r
o
v
id
e
co
n
clu
s
iv
e
ev
id
en
ce
th
at
th
e
p
r
o
p
o
s
ed
m
ac
h
in
e
lear
n
in
g
m
eth
o
d
is
ef
f
ec
tiv
e
in
r
eliab
ly
d
etec
tin
g
W
SN
Do
S
ass
au
lts
.
B
y
in
teg
r
atin
g
th
e
DT
m
eth
o
d
with
th
e
G
in
i
f
ea
tu
r
e
s
elec
tio
n
s
tr
ateg
y
,
we
g
et
an
ac
c
u
r
ac
y
o
f
9
2
%,
wh
ic
h
is
h
ig
h
er
th
an
t
h
e
0
.
9
0
th
r
esh
o
ld
s
f
o
r
p
r
ec
is
io
n
,
r
ec
all,
an
d
F1
-
s
co
r
e.
C
lass
if
ier
s
lik
e
RF
,
XGBo
o
s
t,
an
d
KNN
p
er
f
o
r
m
ad
m
ir
ab
ly
,
with
ac
cu
r
ac
y
lev
e
ls
ab
o
v
e
8
5
%
a
n
d
b
alan
ce
d
r
ec
all,
F1
-
s
co
r
es,
an
d
p
r
ec
is
io
n
.
T
h
e
s
u
g
g
ested
li
g
h
tweig
h
t
m
et
h
o
d
is
ef
f
ec
tiv
e
f
o
r
d
etec
tin
g
Do
S
attac
k
s
in
W
SN
s
,
s
in
ce
th
e
m
ac
h
in
e
lear
n
in
g
class
if
ier
s
g
iv
e
g
o
o
d
d
etec
tio
n
ac
cu
r
ac
y
an
d
b
alan
ce
d
p
er
f
o
r
m
an
ce
m
etr
ics.
B
ec
au
s
e
it
co
m
b
in
es
g
r
ea
t
ac
c
u
r
ac
y
,
i
n
ter
p
r
etab
ilit
y
,
an
d
co
m
p
u
tati
o
n
al
ef
f
icien
cy
,
th
e
DT
alg
o
r
ith
m
with
th
e
Gin
i
f
ea
tu
r
e
s
elec
tio
n
ap
p
r
o
ac
h
s
tan
d
s
o
u
t
as
th
e
m
o
s
t
s
u
cc
ess
f
u
l
class
if
ier
.
I
n
d
etec
tin
g
D
o
S
attac
k
s
in
W
SN
s
,
th
e
RF
,
XGBo
o
s
t,
a
n
d
KNN
class
if
ier
s
p
r
o
d
u
ce
p
r
o
m
is
in
g
r
esu
lts
,
d
em
o
n
s
tr
atin
g
th
e
f
lex
ib
ilit
y
an
d
r
o
b
u
s
tn
ess
o
f
en
s
em
b
le
lear
n
in
g
a
n
d
d
is
tan
ce
-
b
ased
class
if
icatio
n
tech
n
iq
u
es.
T
h
e
r
esu
lts
s
h
o
w
t
h
at
m
ac
h
in
e
lea
r
n
in
g
tech
n
iq
u
es
ca
n
d
etec
t
an
d
m
itig
ate
Do
S
attac
k
s
with
h
ig
h
ac
cu
r
ac
y
,
wh
ich
ca
n
in
c
r
ea
s
e
th
e
r
eliab
ilit
y
an
d
s
af
ety
o
f
W
SN
s
.
R
esear
ch
in
th
e
f
u
tu
r
e
co
u
ld
f
o
cu
s
o
n
im
p
r
o
v
in
g
th
e
m
o
d
el
’
s
p
ar
a
m
eter
s
,
f
in
d
in
g
o
th
er
way
s
t
o
ch
o
o
s
e
f
ea
tu
r
es,
an
d
e
v
alu
atin
g
th
e
p
r
o
p
o
s
ed
tech
n
iq
u
e
in
r
ea
l
-
wo
r
ld
W
SN d
ep
lo
y
m
e
n
ts
to
s
ee
h
o
w
well
i
t w
o
r
k
s
.
W
e
tr
ain
ed
a
n
d
test
ed
all
class
if
ier
s
o
n
o
u
r
n
ew
d
ataset
u
s
in
g
th
e
o
r
ig
i
n
al
an
d
Gin
i
f
ea
tu
r
e
s
elec
tio
n
-
en
h
an
ce
d
v
e
r
s
io
n
s
o
f
th
e
d
ataset.
T
h
is
in
clu
d
ed
XGBo
o
s
t
,
DT
,
KNN,
an
d
R
F.
T
h
e
co
m
p
u
ted
ac
cu
r
ac
ies
o
f
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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d
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r
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h
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d
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atin
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em
o
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ates
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r
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o
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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y
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o
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lly
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ately
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ased
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o
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r
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h
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t th
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e
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.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
A
s
imp
le
ma
ch
in
e
lea
r
n
in
g
tech
n
iq
u
e
f
o
r
s
en
s
o
r
n
etw
o
r
k
w
i
r
eless
…
(
S
h
a
ik
A
b
d
u
l H
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mee
d
)
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.
AUTHO
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B
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Mr.
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