I
nte
rna
t
io
na
l J
o
urna
l o
f
E
lect
rica
l a
nd
Co
m
pu
t
er
E
ng
ineering
(
I
J
E
CE
)
Vo
l.
15
,
No
.
2
,
A
p
r
il
20
25
,
p
p
.
2
0
8
9
~
2
0
9
7
I
SS
N:
2088
-
8
7
0
8
,
DOI
: 1
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.
1
1
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9
1
/ijece.
v
15
i
2
.
pp
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8
9
-
2
0
9
7
2089
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//ij
ec
e.
ia
esco
r
e.
co
m
A lightweig
h
t
ma
chine learni
ng
appro
a
ch f
o
r den
ia
l
-
of
-
serv
ice
a
tt
a
cks det
ection
in wireless
senso
r
net
wo
rks
M
o
ha
m
ed
L
o
ug
hm
a
ri,
Ana
s
s
E
l A
f
f
a
r
En
g
i
n
e
e
r
i
n
g
S
c
i
e
n
c
e
s
La
b
o
r
a
t
o
r
y
(
LS
I
)
,
P
o
l
y
d
i
s
c
i
p
l
i
n
a
r
y
F
a
c
u
l
t
y
o
f
Ta
z
a
,
S
i
d
i
M
o
h
a
me
d
B
e
n
A
b
d
e
l
l
a
h
U
n
i
v
e
r
si
t
y
,
Ta
z
a
,
M
o
r
o
c
c
o
Art
icle
I
nfo
AB
S
T
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
J
u
l 1
7
,
2
0
2
4
R
ev
is
ed
Sep
3
,
2
0
2
4
Acc
ep
ted
Oct
1
,
2
0
2
4
Wi
re
les
s
se
n
so
r
n
e
two
r
k
s
(W
S
N
s)
a
re
in
c
re
a
sin
g
l
y
p
re
v
a
len
t
in
t
h
e
In
ter
n
e
t
o
f
T
h
in
g
s
e
c
o
sy
ste
m
a
n
d
h
a
v
e
b
e
e
n
u
se
d
i
n
se
v
e
ra
l
fiel
d
s
su
c
h
a
s
e
n
v
iro
n
m
e
n
tal
m
o
n
it
o
ri
n
g
,
m
il
it
a
ry
,
a
n
d
h
e
a
lt
h
c
a
re
.
Ho
we
v
e
r,
t
h
e
ir
li
m
it
e
d
re
so
u
rc
e
s
a
n
d
d
istri
b
u
ted
a
rc
h
it
e
c
tu
re
re
m
a
in
two
m
a
in
c
h
a
ll
e
n
g
e
s:
e
n
e
rg
y
a
n
d
se
c
u
rit
y
.
F
u
rt
h
e
rm
o
re
,
d
e
n
ia
l
o
f
se
rv
ice
(Do
S
)
a
tt
a
c
k
s
a
re
o
n
e
o
f
th
e
p
rin
c
i
p
a
l
c
y
b
e
r
t
h
re
a
ts
to
W
S
Ns
.
Th
is
re
se
a
rc
h
p
ro
p
o
se
s
a
l
ig
h
twe
i
g
h
t
m
a
c
h
in
e
lea
rn
in
g
(
M
L)
a
p
p
ro
a
c
h
b
a
se
d
o
n
t
h
e
e
x
trem
e
g
ra
d
ien
t
b
o
o
stin
g
(XG
Bo
o
st)
m
o
d
e
l
to
d
e
tec
t
th
e
s
e
a
t
tac
k
s
in
WS
Ns
.
Th
ro
u
g
h
a
n
e
x
ten
siv
e
in
v
e
stig
a
t
io
n
,
we
e
v
a
lu
a
te
f
o
u
r
p
ro
m
in
e
n
t
M
L
a
lg
o
rit
h
m
s:
ra
n
d
o
m
fo
re
st
(RF
),
k
-
n
e
a
re
st
n
e
ig
h
b
o
r
(KN
N),
sto
c
h
a
stic
g
ra
d
ien
t
d
e
sc
e
n
t
(S
G
D),
a
n
d
XG
B
o
o
st,
u
sin
g
t
h
e
WS
N
-
DS
d
a
tas
e
t.
In
a
d
d
it
i
o
n
,
we
imp
le
m
e
n
t
a
n
d
in
v
e
stig
a
te
se
v
e
ra
l
fe
a
tu
re
se
lec
ti
o
n
tec
h
n
iq
u
e
s
in
o
rd
e
r
t
o
h
a
v
e
a
n
imp
ro
v
e
d
v
e
rsio
n
o
f
th
e
o
rig
in
a
l
d
a
tas
e
t.
M
o
re
o
v
e
r,
we
e
v
a
lu
a
te
t
h
e
p
e
rfo
rm
a
n
c
e
u
sin
g
v
a
rio
u
s
p
e
rfo
rm
a
n
c
e
m
e
tri
c
s,
wh
ich
in
c
l
u
d
e
a
c
c
u
ra
c
y
,
p
re
c
isio
n
,
re
c
a
ll
,
F
1
-
sc
o
re
,
a
n
d
p
r
o
c
e
ss
in
g
ti
m
e
.
T
h
e
latter
is
a
c
ru
c
ial
c
o
n
si
d
e
ra
ti
o
n
i
n
WS
N
e
n
v
iro
n
m
e
n
ts.
F
o
r
v
a
li
d
a
ti
o
n
,
we
h
a
v
e
e
m
p
l
o
y
e
d
5
-
f
o
ld
c
ro
ss
-
v
a
li
d
a
ti
o
n
t
o
e
n
su
re
ro
b
u
st
a
n
d
re
l
iab
le
re
su
lt
s.
Th
e
p
ro
p
o
se
d
m
o
d
e
l
h
a
s
a
c
h
iev
e
d
g
o
o
d
p
e
rfo
rm
a
n
c
e
in
a
ll
m
e
tri
c
s,
wit
h
a
m
a
x
imu
m
a
c
c
u
ra
c
y
o
f
u
p
to
9
9
.
7
3
%
,
a
n
d
a
6
8
%
lo
we
r
p
ro
c
e
ss
in
g
ti
m
e
c
o
m
p
a
re
d
to
th
e
o
th
e
r
in
v
e
sti
g
a
ted
c
la
ss
ifi
e
rs.
K
ey
w
o
r
d
s
:
C
y
b
er
s
ec
u
r
ity
Den
ial
o
f
s
er
v
ice
I
n
tr
u
s
io
n
d
etec
tio
n
Ma
ch
in
e
lear
n
in
g
W
ir
eles
s
s
en
s
o
r
n
etwo
r
k
W
ir
eles
s
s
en
s
o
r
n
etwo
r
k
s
ec
u
r
ity
T
h
is i
s
a
n
o
p
e
n
a
c
c
e
ss
a
rticle
u
n
d
e
r th
e
CC B
Y
-
SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
Mo
h
am
ed
L
o
u
g
h
m
ar
i
E
n
g
in
ee
r
in
g
Scien
ce
s
L
ab
o
r
at
o
r
y
(
L
SI
)
,
Po
ly
d
is
cip
lin
ar
y
Facu
lty
o
f
T
az
a
,
Sid
i M
o
h
am
e
d
B
en
Ab
d
ellah
Un
iv
er
s
ity
,
B
.
P.
1
2
2
3
,
T
az
a,
Mo
r
o
cc
o
E
m
ail: m
o
h
am
ed
.
lo
u
g
h
m
ar
i@
u
s
m
b
a.
ac
.
m
a
1.
I
NT
RO
D
UCT
I
O
N
W
ir
eles
s
s
en
s
o
r
n
etwo
r
k
s
(
W
SNs
)
ar
e
b
ec
o
m
in
g
an
ess
en
tial
p
iece
o
f
th
e
in
ter
n
et
o
f
th
in
g
s
(
I
o
T
)
ec
o
s
y
s
tem
,
an
in
teg
r
al
p
ar
t
o
f
m
o
d
er
n
tech
n
o
lo
g
y
,
an
d
h
a
v
e
b
ee
n
a
d
o
p
te
d
f
o
r
v
ar
io
u
s
ap
p
licatio
n
s
ec
to
r
s
s
u
ch
as
en
v
ir
o
n
m
en
tal
m
o
n
ito
r
i
n
g
,
m
ilit
ar
y
,
h
ea
lth
ca
r
e,
ag
r
ic
u
ltu
r
e
[
1
]
.
T
h
ese
wir
eless
n
etwo
r
k
s
co
n
s
is
t
o
f
m
an
y
au
to
n
o
m
o
u
s
ch
ea
p
an
d
lo
w
-
p
o
wer
s
en
s
o
r
n
o
d
es,
wh
ich
a
r
e
d
ep
lo
y
ed
ac
r
o
s
s
v
ar
io
u
s
ar
ea
s
to
co
llect
im
p
o
r
tan
t
d
ata
an
d
th
e
n
tr
an
s
m
itted
in
a
co
o
p
er
ati
v
e
m
an
n
e
r
v
ia
wir
eless
m
ea
n
s
to
a
h
ig
h
er
p
er
f
o
r
m
an
ce
n
o
d
e
co
m
m
o
n
l
y
r
ef
e
r
r
ed
t
o
as
th
e
s
in
k
n
o
d
e
o
r
b
ase
s
tatio
n
[
2
]
.
Ho
wev
er
,
d
u
e
to
th
eir
wir
el
ess
co
m
m
u
n
icatio
n
n
atu
r
e,
lim
ited
co
m
p
u
tatio
n
al
r
eso
u
r
ce
s
,
an
d
e
n
er
g
y
c
o
n
s
tr
ai
n
ts
,
W
SN
s
ar
e
ex
p
o
s
ed
to
v
ar
io
u
s
cy
b
er
-
attac
k
s
,
esp
ec
ially
d
en
ial
o
f
s
er
v
ice
(
D
o
S)
attac
k
s
[
3
]
.
Do
S a
ttack
aim
s
to
co
m
p
r
o
m
is
e
th
e
av
ailab
ilit
y
an
d
in
teg
r
ity
o
f
th
e
W
SN
s
y
s
tem
s
,
b
y
o
v
er
wh
elm
in
g
th
e
s
en
s
o
r
n
o
d
es,
ca
u
s
in
g
s
er
v
ice
d
is
r
u
p
tio
n
s
a
n
d
d
a
ta
lo
s
s
.
p
r
o
tectin
g
ag
ain
s
t
Do
S
attac
k
s
is
v
er
y
c
h
allen
g
in
g
b
ec
au
s
e
th
ey
ca
n
b
e
g
e
n
er
ated
f
r
o
m
a
n
y
wh
er
e
,
p
ar
ticu
lar
l
y
in
th
e
co
n
tex
t
o
f
wir
eless
s
en
s
o
r
s
d
ep
lo
y
e
d
o
n
a
m
ass
iv
e
s
ca
le.
C
u
r
r
en
t
n
etwo
r
k
s
ec
u
r
ity
s
o
lu
tio
n
s
,
s
u
ch
as
tr
ad
itio
n
al
in
tr
u
s
io
n
d
etec
tio
n
s
y
s
tem
s
(
I
DS)
m
ay
n
o
t
b
e
s
u
f
f
icien
t
f
o
r
W
SNs
as
th
ey
d
if
f
er
f
r
o
m
u
s
u
al
co
m
p
u
ter
n
etwo
r
k
s
,
th
e
p
r
e
v
e
n
tio
n
p
r
o
tectio
n
tech
n
o
l
o
g
y
d
esig
n
ed
f
o
r
W
SN
in
f
r
astru
ctu
r
e
m
u
s
t
b
e
h
ig
h
ly
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
0
8
9
-
2
0
9
7
2090
ac
cu
r
ate,
f
ast,
an
d
s
u
itab
le
to
en
s
u
r
e
m
in
im
al
o
v
e
r
h
ea
d
[
4
]
,
[
5
]
.
T
h
er
e
f
o
r
e,
th
er
e
is
a
p
r
ess
in
g
n
ee
d
f
o
r
an
ef
f
icien
t lig
h
tweig
h
t in
tr
u
s
io
n
d
etec
tio
n
tech
n
iq
u
e.
W
SN
s
ar
e,
lik
e
an
y
n
etwo
r
k
ty
p
e,
liab
le
to
v
ar
io
u
s
s
ec
u
r
it
y
th
r
ea
ts
an
d
v
u
ln
er
a
b
ilit
ies.
Ho
wev
er
,
th
eir
u
n
iq
u
e
ch
a
r
ac
ter
is
tics
,
lik
e
r
eso
u
r
ce
co
n
s
tr
ain
ts
an
d
d
ep
lo
y
m
en
t
en
v
ir
o
n
m
en
ts
,
r
eq
u
ir
e
s
p
ec
ialized
d
ef
en
s
e
s
ch
em
es.
Nu
m
er
o
u
s
ex
is
tin
g
s
tu
d
ies
ad
d
r
ess
ed
th
ese
ch
allen
g
es
b
y
in
v
esti
g
atin
g
th
e
p
o
ten
tial
o
f
m
ac
h
in
e
lear
n
in
g
(
ML
)
m
o
d
els
f
o
r
cy
b
er
th
r
ea
t
d
etec
tio
n
wh
ile
also
h
ig
h
lig
h
tin
g
th
e
d
if
f
icu
lties
d
u
e
to
r
eso
u
r
ce
co
n
s
tr
ain
ts
[
6
]
.
R
av
i
et
a
l.
[
7
]
p
r
esen
ted
a
n
en
d
-
to
-
en
d
d
ee
p
lear
n
in
g
(
DL
)
a
p
p
r
o
ac
h
u
s
in
g
r
ec
u
r
r
e
n
t
n
eu
r
al
n
etwo
r
k
s
(
R
NNs)
f
o
r
in
tellig
en
t
n
etwo
r
k
in
tr
u
s
io
n
d
etec
tio
n
an
d
class
if
icatio
n
.
T
h
ey
ev
alu
ated
th
eir
wo
r
k
m
ain
ly
o
n
th
e
s
o
f
twar
e
-
d
ef
in
ed
n
etwo
r
k
i
n
g
-
I
o
T
(
S
DN
-
I
o
T
)
[
8
]
an
d
v
ar
io
u
s
b
e
n
ch
m
ar
k
d
atasets
,
in
clu
d
in
g
KDD
-
C
u
p
-
1
9
9
9
[
9
]
,
UNSW
-
N
B
1
5
[
1
0
]
,
W
SN
-
DS
[
2
]
,
an
d
C
I
C
I
DS
-
2
0
1
7
[
1
1
]
,
ac
h
iev
in
g
a
m
ax
im
u
m
ac
cu
r
ac
y
o
f
9
9
%
f
o
r
in
tr
u
s
io
n
d
etec
tio
n
an
d
9
7
%
f
o
r
in
tr
u
s
io
n
class
if
icatio
n
.
A
s
o
p
h
is
ticated
in
tr
u
s
io
n
d
etec
tio
n
m
eth
o
d
(
SLGBM)
s
p
ec
if
ically
d
esig
n
ed
f
o
r
W
SNs
was
p
r
o
p
o
s
ed
b
y
J
ian
g
et
a
l.
[
1
2
]
,
wh
ich
co
m
b
in
es
th
e
s
eq
u
en
ti
al
b
ac
k
war
d
s
elec
tio
n
(
SB
S)
f
ea
tu
r
e
s
elec
tio
n
tech
n
iq
u
e
an
d
th
e
L
ig
h
tGB
M
alg
o
r
ith
m
,
th
eir
e
x
p
er
im
e
n
tal
r
esu
lts
d
etec
tio
n
r
ates
ex
ce
e
d
ed
9
6
%
f
o
r
ea
ch
o
f
th
e
f
iv
e
c
ateg
o
r
ies
with
in
th
e
W
SN
-
DS
d
ataset.
I
n
th
eir
wo
r
k
,
W
az
ir
ali
an
d
Ah
m
a
d
[
1
3
]
i
n
v
esti
g
ated
a
v
ar
iety
o
f
ML
al
g
o
r
ith
m
s
to
an
aly
ze
W
SN
tr
af
f
ic
d
ata
an
d
id
en
tify
Do
S/DDo
S
attac
k
s
,
an
d
am
o
n
g
th
e
test
ed
alg
o
r
ith
m
s
,
G
B
o
o
s
t
ac
h
iev
ed
th
e
h
ig
h
est
av
er
a
g
e
p
er
f
o
r
m
an
ce
,
with
9
9
.
6
%
ac
cu
r
ac
y
an
d
a
m
o
d
er
ate
t
r
ain
in
g
tim
e
o
f
1
.
4
1
s
ec
o
n
d
s
.
R
am
an
a
et
a
l.
[
1
4
]
d
ev
elo
p
e
d
a
n
o
v
el
in
tr
u
s
io
n
d
etec
tio
n
s
y
s
tem
(
W
OG
R
U
-
I
DS)
th
at
em
p
lo
y
ed
wh
ale
o
p
tim
izatio
n
alg
o
r
ith
m
(
W
OA)
to
o
p
tim
iz
e
th
e
h
y
p
er
p
a
r
am
eter
s
o
f
a
l
o
n
g
s
h
o
r
t
-
ter
m
m
em
o
r
y
(
L
S
T
M)
n
etwo
r
k
.
T
h
eir
r
esu
lts
d
em
o
n
s
tr
ated
an
av
er
a
g
e
ac
cu
r
ac
y
o
f
9
9
.
8
5
%
f
o
r
all
attac
k
ty
p
es.
Me
en
ak
s
h
i
an
d
Kar
u
n
k
u
z
h
ali
[
1
5
]
p
r
o
p
o
s
ed
a
n
o
v
el
in
tr
u
s
io
n
d
etec
tio
n
m
eth
o
d
em
p
lo
y
in
g
c
o
lo
r
wien
er
f
ilter
in
g
(
C
W
F)
i
n
th
e
p
r
ep
r
o
ce
s
s
in
g
p
h
ase
an
d
T
asm
an
ian
d
ev
il
o
p
tim
izatio
n
(
T
DO)
in
th
e
f
ea
t
u
r
e
s
elec
tio
n
p
h
ase
wh
ile
u
tili
zin
g
o
p
tim
ized
s
elf
-
atten
tio
n
-
b
ased
p
r
o
v
is
io
n
al
v
ar
iatio
n
al
au
to
-
en
c
o
d
er
g
e
n
er
ativ
e
ad
v
er
s
ar
ial
n
etwo
r
k
(
SAPVAGA
N)
f
o
r
b
in
ar
y
class
if
icatio
n
o
f
W
SN
-
DS
d
ata,
th
eir
n
o
v
el
m
et
h
o
d
p
r
o
v
id
es
h
i
g
h
er
ac
c
u
r
ac
y
a
n
d
lo
wer
co
m
p
u
tatio
n
tim
e
wh
en
co
m
p
ar
e
d
s
o
lely
t
o
SLGBM
[
1
2
]
,
R
NN
[
7
]
,
a
n
d
W
OGRU
[
1
4
]
m
eth
o
d
s
.
Ma
n
ju
la
an
d
Priy
a
[
1
6
]
in
tr
o
d
u
ce
d
a
VGG
-
19
-
b
ased
C
NN
-
L
STM
f
r
am
ewo
r
k
to
a
ch
iev
e
a
h
i
g
h
class
if
icatio
n
a
cc
u
r
ac
y
o
f
9
8
.
8
6
%.
L
iu
et
a
l.
[
1
7
]
s
u
g
g
ested
a
n
e
d
g
e
-
b
ased
in
tr
u
s
io
n
d
etec
tio
n
m
o
d
el
(
K
NN
-
PL
-
AOA)
tailo
r
ed
f
o
r
W
SNs
,
th
eir
ap
p
r
o
ac
h
em
p
lo
y
ed
an
im
p
r
o
v
ed
k
-
n
ea
r
est
n
eig
h
b
o
r
(
K
NN)
class
if
ier
in
teg
r
ated
with
a
n
o
v
el
o
p
tim
izatio
n
alg
o
r
ith
m
(
PL
-
AOA)
,
th
e
m
o
d
el
d
em
o
n
s
tr
ated
p
r
o
m
is
in
g
p
er
f
o
r
m
an
ce
with
an
ac
cu
r
ac
y
r
ate
o
f
9
9
%.
Siv
ag
am
in
ath
an
et
a
l.
[
1
8
]
ex
am
in
ed
th
e
ap
p
licatio
n
o
f
p
ar
ticle
s
war
m
o
p
tim
izatio
n
(
PS
O)
as
a
f
ea
tu
r
e
s
elec
tio
n
tech
n
iq
u
e
in
c
o
n
ju
n
ctio
n
s
with
v
ar
io
u
s
ML
alg
o
r
i
th
m
s
,
th
e
r
esu
lts
s
u
g
g
ested
th
at
th
is
co
m
b
in
atio
n
ac
h
iev
ed
s
u
p
er
i
o
r
class
if
icatio
n
ac
cu
r
ac
y
co
m
p
a
r
ed
to
o
th
er
m
eth
o
d
s
in
th
eir
ca
s
e
s
tu
d
y
.
R
am
esh
k
u
m
ar
et
a
l.
[
1
9
]
ass
ay
ed
r
ein
f
o
r
ce
m
en
t
le
ar
n
in
g
(
R
L
)
b
y
c
o
m
b
in
in
g
a
d
ee
p
Q
n
etwo
r
k
with
tr
an
s
f
er
lear
n
in
g
(
T
L
DQN)
ap
p
r
o
ac
h
f
o
r
DDo
S
n
atu
r
e
att
ac
k
s
in
wir
eless
m
u
ltime
d
ia
s
en
s
o
r
n
etwo
r
k
s
(
W
MSNs)
f
o
r
s
m
ar
t
ag
r
icu
ltu
r
e
ap
p
licatio
n
s
.
T
h
e
au
th
o
r
s
claim
ed
th
at
th
eir
tech
n
iq
u
e
ac
h
i
ev
es
9
0
%
th
r
o
u
g
h
p
u
t,
9
5
%
p
ac
k
et
d
eliv
er
y
r
atio
,
8
9
%
DDo
S
d
etec
tio
n
ac
cu
r
ac
y
,
an
d
9
5
%
m
u
ltip
ath
an
aly
s
is
ac
cu
r
ac
y
.
I
n
2
0
2
4
,
Sh
ak
y
a
et
a
l.
[
2
0
]
h
ig
h
lig
h
te
d
th
e
p
o
ten
tial
o
f
co
m
b
in
in
g
DL
an
d
R
L
f
o
r
in
tr
u
s
io
n
d
etec
tio
n
in
W
SNs
b
y
p
r
o
p
o
s
in
g
an
in
n
o
v
ativ
e
a
p
p
r
o
ac
h
:
I
R
ADA
in
teg
r
ated
RL
-
b
ased
ad
v
an
ce
d
DL
.
T
h
e
ev
alu
atio
n
s
h
o
wed
im
p
r
ess
iv
e
r
esu
lts
with
h
ig
h
ac
c
u
r
ac
y
(
9
9
.
5
0
%),
s
p
ec
if
icity
(
9
9
.
9
4
%),
s
en
s
itiv
ity
(
9
9
.
4
8
%),
F1
-
s
co
r
e
(
9
8
.
2
6
%),
Ka
p
p
a
s
tatis
tics
(
9
9
.
4
2
%),
an
d
ar
ea
u
n
d
er
t
h
e
cu
r
v
e
(
AUC)
(
9
9
.
3
8
%).
D
e
s
p
it
e
t
h
e
e
x
t
e
n
s
i
v
e
r
e
s
ea
r
ch
e
f
f
o
r
t
s
c
o
n
d
u
c
t
e
d
i
n
t
h
e
f
i
el
d
o
f
i
n
t
r
u
s
i
o
n
d
e
t
ec
t
i
o
n
,
d
e
v
e
l
o
p
i
n
g
a
n
a
p
p
l
i
c
a
b
l
e
s
o
l
u
ti
o
n
wi
t
h
t
h
e
a
b
i
l
i
t
y
t
o
e
f
f
ec
t
i
v
el
y
d
e
t
e
ct
D
o
S
th
r
e
a
t
s
i
n
W
SN
e
n
v
i
r
o
n
m
e
n
ts
r
e
m
a
i
n
s
a
s
i
g
n
i
f
i
ca
n
t
c
h
a
l
l
e
n
g
e
.
T
h
is
w
o
r
k
p
r
es
e
n
t
s
a
n
o
v
e
l
a
p
p
r
o
a
c
h
f
o
r
d
e
t
e
c
t
i
n
g
D
o
S
at
t
a
c
k
s
i
n
W
SN
s
.
T
h
e
m
a
i
n
c
o
n
t
r
i
b
u
t
i
o
n
s
o
f
t
h
i
s
p
a
p
e
r
a
r
e
:
A
l
i
g
h
t
w
ei
g
h
t
a
p
p
r
o
a
c
h
l
e
v
e
r
a
g
i
n
g
e
n
s
e
m
b
l
e
b
o
o
s
t
i
n
g
l
e
a
r
n
i
n
g
t
e
c
h
n
i
q
u
e
s
:
th
a
t
a
i
m
s
t
o
p
r
o
v
i
d
e
a
n
e
f
f
i
c
a
c
i
o
u
s
a
n
d
e
f
f
i
c
ie
n
t
s
o
lu
t
i
o
n
f
o
r
s
e
c
u
r
i
n
g
W
SN
s
a
g
a
i
n
s
t
D
o
S
at
t
a
c
k
s
a
n
d
p
o
t
e
n
t
i
a
l
i
n
tr
u
s
i
o
n
s
b
y
o
f
f
e
r
i
n
g
h
i
g
h
a
c
c
u
r
a
c
y
,
r
o
b
u
s
t
f
e
a
t
u
r
e
s
e
l
e
c
t
i
o
n
,
c
o
m
p
r
e
h
e
n
s
i
v
e
e
v
a
l
u
a
t
i
o
n
m
e
t
r
i
cs
,
a
n
d
r
e
d
u
c
e
d
p
r
o
c
e
s
s
i
n
g
t
i
m
e
m
a
k
i
n
g
i
t
a
s
u
i
ta
b
l
e
s
o
l
u
t
i
o
n
f
o
r
r
e
al
-
t
i
m
e
d
e
t
ec
t
i
o
n
i
n
s
u
c
h
e
n
v
i
r
o
n
m
e
n
t
s
;
A
n
o
v
e
l
I
DS
m
o
d
el
b
a
s
e
d
o
n
e
x
t
r
e
m
e
g
r
a
d
i
e
n
t
b
o
o
s
t
i
n
g
(
X
GB
o
o
s
t
)
,
a
n
d
t
h
e
u
t
i
l
i
za
t
i
o
n
o
f
t
h
e
r
ec
e
n
t
,
c
o
m
p
r
e
h
e
n
s
i
v
e
W
SN
-
DS
d
a
t
as
e
t
,
w
h
i
c
h
i
s
d
e
s
i
g
n
e
d
s
p
e
c
i
f
ic
a
l
l
y
f
o
r
e
v
a
l
u
a
t
i
n
g
D
o
S
a
t
t
a
c
k
s
i
n
W
SN
s
,
m
o
r
e
o
v
e
r
,
T
h
e
w
o
r
k
i
s
r
i
g
o
r
o
u
s
l
y
e
v
a
l
u
a
t
e
d
t
h
r
o
u
g
h
c
r
o
s
s
-
v
a
li
d
a
t
i
o
n
t
o
e
n
s
u
r
e
t
h
at
o
u
r
f
i
n
d
i
n
g
s
a
r
e
b
o
t
h
r
e
l
e
v
a
n
t
a
n
d
a
p
p
l
i
c
a
b
l
e
t
o
r
e
a
l
-
w
o
r
l
d
s
c
e
n
a
r
i
o
s
i
n
W
S
N
e
n
v
i
r
o
n
m
e
n
t
s
.
T
h
e
r
es
t
o
f
t
h
e
p
a
p
e
r
i
s
s
t
r
u
c
t
u
r
e
d
as
f
o
ll
o
w
s
:
s
e
c
t
i
o
n
2
d
e
s
c
r
i
b
e
s
t
h
e
p
r
o
p
o
s
ed
a
p
p
r
o
a
c
h
i
n
d
e
t
a
i
l,
s
e
c
ti
o
n
3
i
l
l
u
s
t
r
a
t
es
t
h
e
r
es
u
l
ts
a
n
d
d
i
s
c
u
s
s
i
o
n
,
a
n
d
s
ec
t
i
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r
o
p
s
all
d
ata
p
ac
k
ets f
r
o
m
r
ea
c
h
in
g
t
h
eir
in
ten
d
ed
d
esti
n
atio
n
s
.
b.
Gr
ay
h
o
le
attac
k
:
u
n
lik
e
th
e
f
i
r
s
t
o
n
e,
g
r
ay
h
o
le
d
id
n
o
t
p
e
r
f
o
r
m
a
co
m
p
lete
b
lo
ck
a
g
e
b
u
t
r
an
d
o
m
l
y
o
r
s
elec
tiv
ely
d
r
o
p
p
e
d
s
o
m
e
d
ata
p
ac
k
ets.
c.
T
im
e
d
iv
is
io
n
m
u
ltip
le
ac
ce
s
s
(
T
DM
A)
o
r
s
ch
ed
u
lin
g
:
th
is
attac
k
ex
p
lo
its
th
e
s
ch
ed
u
led
ac
ce
s
s
o
f
T
DM
A
b
y
m
a
n
ip
u
latin
g
th
is
p
r
o
to
co
l
to
d
is
r
u
p
t
th
e
s
ch
e
d
u
lin
g
an
d
co
o
r
d
in
atio
n
o
f
tr
a
n
s
m
is
s
io
n
s
b
etwe
en
n
o
d
es,
ca
u
s
in
g
co
llis
io
n
s
,
d
elay
s
,
o
r
in
ter
f
er
en
ce
.
d.
Flo
o
d
in
g
:
th
is
attac
k
aim
s
to
o
v
er
wh
elm
th
e
n
etwo
r
k
with
a
lar
g
e
v
o
lu
m
e
o
f
tr
af
f
ic,
wh
i
ch
ca
n
lead
to
d
en
ial
o
f
s
er
v
ice
f
o
r
le
g
itima
te
u
s
er
s
o
r
n
o
d
es b
y
c
o
n
s
u
m
in
g
in
f
in
ite
en
er
g
y
,
n
etwo
r
k
tr
af
f
i
c,
an
d
m
e
m
o
r
y
.
Fig
u
r
e
2
.
Dis
tr
ib
u
tio
n
o
f
W
SN
-
DS d
ataset
2
.
2
.
Da
t
a
prepro
ce
s
s
ing
T
h
e
W
SN
-
DS
tar
g
et
f
ea
tu
r
e
co
n
s
is
ts
o
f
s
tr
in
g
s
th
at
r
ep
r
esen
t
d
if
f
er
e
n
t
attac
k
ty
p
es.
T
h
ese
tex
tu
al
lab
els
ar
e
tr
an
s
lated
in
to
n
u
m
er
ical
r
ep
r
esen
tatio
n
s
to
p
r
ep
ar
e
an
d
f
ac
ilit
ate
u
n
if
o
r
m
d
ata
h
an
d
lin
g
f
o
r
alg
o
r
ith
m
ic
p
r
o
ce
s
s
in
g
.
No
r
m
al
tr
af
f
ic
is
en
co
d
ed
as
0
,
wh
ile
th
e
f
o
u
r
attac
k
t
y
p
es:
b
lack
h
o
le,
g
r
a
y
h
o
le,
f
lo
o
d
in
g
,
an
d
T
DM
A,
ar
e
en
co
d
ed
as
1
,
2
,
3
,
an
d
4
,
r
esp
ec
tiv
ely
.
T
h
e
o
th
e
r
d
ata
p
r
e
p
r
o
ce
s
s
in
g
o
p
er
atio
n
s
in
clu
d
e
id
en
tify
i
n
g
an
d
h
an
d
lin
g
m
is
s
in
g
v
alu
es,
n
u
ll
v
alu
es
,
in
f
in
ity
v
alu
es,
an
d
d
u
p
licate
r
em
o
v
al
in
o
r
d
e
r
to
p
r
eser
v
e
d
ata
f
id
elity
an
d
m
itig
ate
p
o
ten
tial so
u
r
ce
s
o
f
b
ia
s
o
r
n
o
is
e.
2
.
3
.
F
e
a
t
ure
s
elec
t
io
n
T
h
e
f
ea
tu
r
e
s
elec
tio
n
tec
h
n
iq
u
e
is
in
ten
d
ed
to
s
elec
t
th
e
o
p
tim
al
f
ea
tu
r
es
in
th
e
d
ataset.
I
t
is
a
p
o
wer
f
u
l
p
h
ase
o
f
an
y
m
ac
h
i
n
e
lear
n
in
g
p
r
o
ce
s
s
,
an
d
it
ty
p
ically
im
p
ac
ts
th
e
class
if
ier
'
s
p
er
f
o
r
m
an
ce
,
d
ata
u
n
d
er
s
tan
d
i
n
g
,
r
eso
u
r
ce
s
,
an
d
tim
e
co
n
s
u
m
p
tio
n
[
2
3
]
.
T
h
i
s
is
p
ar
ticu
lar
ly
im
p
o
r
tan
t
in
th
e
co
n
tex
t
o
f
th
e
r
esear
ch
,
wh
er
e
we
a
r
e
d
ea
lin
g
with
a
W
SN e
n
v
ir
o
n
m
e
n
t th
at
o
f
ten
h
as lim
ited
co
m
p
u
tin
g
r
eso
u
r
ce
s
.
T
h
er
ef
o
r
e,
th
is
r
esear
ch
ex
p
l
o
r
ed
v
ar
i
o
u
s
f
ea
tu
r
e
s
elec
tio
n
tech
n
iq
u
es,
s
u
ch
as
r
ec
u
r
s
iv
e
f
ea
tu
r
e
elim
in
atio
n
(
R
FE)
,
s
eq
u
e
n
tial
b
ac
k
war
d
s
elec
tio
n
an
d
f
o
r
war
d
(
SB
SF
)
,
an
d
f
ea
tu
r
e
im
p
o
r
ta
n
ce
-
b
ased
m
eth
o
d
s
.
Ho
wev
er
,
th
ese
ap
p
r
o
ac
h
es
d
id
n
o
t
y
ield
th
e
d
esire
d
im
p
r
o
v
em
en
ts
in
th
e
r
esu
lts
.
T
h
e
m
ain
p
r
o
g
r
ess
was
ac
h
iev
ed
th
r
o
u
g
h
a
s
tr
aig
h
tf
o
r
war
d
y
et
ef
f
ici
en
t
ac
tio
n
-
th
e
ex
clu
s
io
n
o
f
th
e
in
d
ex
co
lu
m
n
,
wh
ich
was
d
eter
m
i
n
ed
t
o
b
e
in
s
ig
n
if
ican
t
f
o
r
th
e
d
etec
tio
n
p
r
o
ce
s
s
.
Fu
r
th
er
m
o
r
e,
f
r
o
m
t
h
e
g
e
n
er
ated
SNS
h
ea
tm
ap
o
f
f
ea
tu
r
e
co
r
r
elatio
n
in
Fig
u
r
e
3
,
we
d
r
o
p
p
ed
th
e
‘
SC
H_
R
’
f
ea
tu
r
e
b
ased
o
n
t
h
e
an
aly
s
is
to
id
en
tify
an
d
r
em
o
v
e
th
e
h
ig
h
ly
co
r
r
ela
ted
f
ea
tu
r
es,
th
is
was
d
o
n
e
in
p
r
ef
er
en
ce
to
d
eletin
g
th
e
‘
J
OI
N_
S’
f
ea
tu
r
e,
as
it
was
d
ee
m
ed
t
o
h
av
e
g
r
ea
ter
f
e
atu
r
e
im
p
o
r
tan
ce
.
I
n
ad
d
itio
n
,
th
e
ab
s
en
ce
o
f
an
y
co
l
u
m
n
s
w
ith
a
s
in
g
le
u
n
i
q
u
e
v
alu
e
was
ch
ec
k
e
d
an
d
co
n
f
ir
m
ed
.
Af
ter
th
is
iter
ativ
e
f
ea
tu
r
e
s
elec
tio
n
p
r
o
ce
s
s
,
we
wer
e
l
ef
t
with
1
6
f
ea
tu
r
es
f
r
o
m
th
e
o
r
ig
in
al
d
ataset,
wh
ich
we
th
en
u
s
ed
to
tr
ain
a
n
d
e
v
alu
ate
th
e
ex
am
in
e
d
m
o
d
els.
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
A
lig
h
tw
eig
h
t m
a
ch
in
e
le
a
r
n
in
g
a
p
p
r
o
a
ch
fo
r
d
en
ia
l
-
of
-
s
ervice
a
tta
ck
s
…
(
Mo
h
a
med
Lo
u
g
h
ma
r
i
)
2093
Fig
u
r
e
3
.
Hea
tm
ap
o
f
f
ea
tu
r
e
c
o
r
r
elatio
n
2
.
4
.
Va
lid
a
t
i
o
n
A
cr
o
s
s
-
v
alid
atio
n
was
p
er
f
o
r
m
ed
to
v
alid
ate
o
u
r
p
r
o
p
o
s
ed
m
o
d
el
r
ea
s
o
n
ab
ly
an
d
o
b
ject
iv
ely
.
W
e
em
p
lo
y
ed
5
f
o
ld
s
to
ev
al
u
ate
its
p
er
f
o
r
m
an
ce
o
n
u
n
s
ee
n
d
ata.
Fu
r
th
er
m
o
r
e,
th
e
s
u
g
g
ested
m
o
d
el
was
ev
alu
ated
u
s
in
g
s
ev
er
al
p
er
f
o
r
m
an
ce
m
etr
ics,
in
clu
d
in
g
ac
c
u
r
ac
y
,
p
r
ec
is
io
n
,
r
ec
all
,
F1
-
s
c
o
r
e,
an
d
p
r
o
ce
s
s
in
g
tim
e.
T
h
e
ca
lcu
latio
n
o
f
th
ese
m
ea
s
u
r
es
r
elies
o
n
th
e
v
alu
es
f
r
o
m
th
e
co
n
f
u
s
io
n
m
atr
ix
,
i
n
clu
d
in
g
tr
u
e
p
o
s
itiv
es (
T
P),
tr
u
e
n
eg
ati
v
es (
T
N)
,
f
alse p
o
s
itiv
es (
FP
)
,
an
d
f
alse n
e
g
ativ
es (
FN)
.
a.
Acc
u
r
ac
y
:
co
n
s
id
er
b
o
th
in
s
tan
ce
s
o
f
n
o
r
m
al
an
d
ab
n
o
r
m
al
d
ata
with
o
th
er
in
s
tan
ce
s
.
=
+
+
+
+
(
1
)
b.
Pre
cisi
o
n
:
p
er
ce
n
tag
e
o
f
co
r
r
e
ctly
class
if
ied
ab
n
o
r
m
al
(
attac
k
)
in
s
tan
ce
s
o
u
t
o
f
all
in
s
tan
c
es
class
if
ied
as
ab
n
o
r
m
al.
=
+
(
2
)
c.
R
e
c
al
l
:
p
r
o
p
o
r
t
i
o
n
o
f
c
o
r
r
e
ct
l
y
c
l
a
s
s
i
f
i
e
d
a
b
n
o
r
m
a
l
s
a
m
p
l
es
r
e
l
a
t
i
v
e
t
o
t
h
e
t
o
t
a
l
n
u
m
b
e
r
o
f
a
c
t
u
a
l
a
b
n
o
r
m
a
l
s
a
m
p
l
es
.
=
+
(
3
)
d.
F1
-
s
co
r
e:
C
o
m
b
in
atio
n
o
f
p
r
e
cisi
o
n
an
d
r
ec
all
in
to
a
s
in
g
le
s
co
r
e
v
alu
e
b
y
tak
in
g
th
eir
h
a
r
m
o
n
ic
m
ea
n
.
1
−
=
2
+
(
4
)
2
.
5
.
E
x
perim
ent
a
l e
nv
ir
o
nm
ent
T
h
e
s
elec
tio
n
o
f
th
e
h
y
p
e
r
p
a
r
am
eter
co
m
b
i
n
atio
n
f
o
r
th
e
in
v
esti
g
ated
ML
m
o
d
els
was
o
p
tim
ized
u
s
in
g
th
e
r
an
d
o
m
ized
s
ea
r
c
h
tech
n
iq
u
e
[
2
4
]
f
r
o
m
t
h
e
R
an
d
o
m
ized
Sear
c
h
C
V
s
cik
it
-
le
ar
n
lib
r
ar
y
,
wh
ich
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
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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
:
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2094
ef
f
icien
tly
ex
p
l
o
r
ed
a
wid
e
r
a
n
g
e
o
f
h
y
p
er
p
a
r
am
eter
v
al
u
es,
to
en
s
u
r
e
th
at
th
e
m
o
d
els
wer
e
f
in
e
-
tu
n
e
d
f
o
r
th
e
b
est
p
er
f
o
r
m
an
ce
.
T
h
e
e
x
p
er
i
m
en
ts
p
r
esen
ted
in
th
is
p
a
p
er
wer
e
co
n
d
u
cted
o
n
th
e
Go
o
g
l
e
C
o
lab
p
latf
o
r
m
,
a
clo
u
d
-
b
ased
J
u
p
y
ter
No
te
b
o
o
k
en
v
ir
o
n
m
en
t.
T
h
e
r
u
n
tim
e
wa
s
s
p
ec
if
ically
as f
o
llo
ws:
−
R
u
n
tim
e
T
y
p
e:
Py
th
o
n
3
−
Har
d
war
e
Acc
eler
ato
r
: CP
U
−
R
AM
: 1
2
.
7
GB
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
Ou
r
r
esear
ch
m
eth
o
d
o
lo
g
y
e
v
alu
ates
o
u
r
p
r
o
p
o
s
ed
a
p
p
r
o
ac
h
in
b
o
th
o
r
ig
in
al
an
d
im
p
r
o
v
ed
d
atasets
af
ter
th
e
f
ea
tu
r
e
s
elec
tio
n
p
h
a
s
e;
All
m
o
d
els
ar
e
tr
ain
ed
an
d
test
ed
in
th
is
p
h
ase,
m
ak
in
g
a
clea
r
class
if
icatio
n
r
ep
o
r
t.
As
m
en
tio
n
ed
b
ef
o
r
e
i
n
th
e
im
p
r
o
v
e
d
v
er
s
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ea
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icate
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ea
t
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r
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r
ig
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aset
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lly
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ted
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r
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m
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ig
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ally
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r
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r
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cial
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o
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W
SN secu
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ity
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u
r
p
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2
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.
Fig
u
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o
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s
er
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ai
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atasets
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r
o
m
th
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m
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r
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s
h
o
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s
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h
t
im
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r
o
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em
en
t
i
n
th
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er
d
ataset
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er
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io
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m
o
s
t
s
ig
n
if
ican
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ch
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g
e
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o
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er
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r
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er
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lts
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ig
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g
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t
th
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r
o
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s
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en
s
em
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le
m
eth
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o
s
t a
n
d
R
F.
Fig
u
r
e
4
.
Acc
u
r
ac
y
e
x
h
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ited
b
y
all
class
if
ier
s
o
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r
i
g
in
al
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ataset
Fig
u
r
e
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.
Acc
u
r
ac
y
e
x
h
ib
ited
b
y
all
class
if
ier
s
o
n
th
e
im
p
r
o
v
ed
d
ataset
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
-
8
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atasets
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m
etr
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o
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ter
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v
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ataset.
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i
g
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R
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ar
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elativ
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ics o
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ataset
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C
o
m
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t
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icate
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th
at
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th
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er
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o
r
m
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co
n
s
is
ten
tly
in
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o
th
v
e
r
s
io
n
s
o
f
th
e
d
ataset
co
n
f
ir
m
in
g
th
at
e
n
s
em
b
le
lear
n
in
g
tech
n
i
q
u
es
ar
e
ef
f
ec
tiv
e
o
n
lar
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e
-
s
ca
le
d
ata
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2
5
]
.
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o
s
t
o
f
f
e
r
s
s
lig
h
tly
b
etter
p
er
f
o
r
m
a
n
ce
,
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h
ich
is
n
o
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le
in
th
e
co
n
tex
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o
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cy
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er
s
ec
u
r
ity
,
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er
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en
s
m
all
d
if
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er
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ce
s
in
r
ates
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n
h
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v
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ig
n
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ican
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im
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licatio
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s
in
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ea
l
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ld
s
ce
n
a
r
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s
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wev
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h
as
a
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r
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t
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m
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ay
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n
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it
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le
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o
r
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lim
ited
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r
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en
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r
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m
e
n
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also
g
iv
es
r
esp
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tab
le
r
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er
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o
r
m
a
n
ce
b
u
t
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s
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e
h
in
d
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GB
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o
s
t
an
d
R
F
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er
f
o
r
m
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ce
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etr
ics.
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n
a
d
d
itio
n
,
it
h
as
th
e
h
ig
h
est
tim
e
co
n
s
u
m
p
tio
n
.
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is
n
o
t
a
r
e
liab
le
class
if
ier
f
o
r
th
e
in
tr
u
s
io
n
d
etec
tio
n
task
s
in
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it p
er
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o
r
m
s
th
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s
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m
o
n
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e
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ier
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ter
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s
o
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etr
ics.
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h
e
k
e
y
f
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s
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o
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r
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ed
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t
m
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l
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o
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ates
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e
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ig
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est
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er
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o
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s
o
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etr
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r
e
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er
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ig
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r
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ce
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m
p
ar
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r
e
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r
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ely
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o
r
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o
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ig
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s
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ately
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n
d
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f
o
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r
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o
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ately
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h
ese
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lts
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icate
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at
th
e
lig
h
tweig
h
t
d
ev
elo
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e
d
m
o
d
el
is
ap
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licab
le
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o
r
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ited
r
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u
r
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etwo
r
k
s
lik
e
th
e
W
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wh
er
e
en
er
g
y
ef
f
icien
cy
is
cr
itical,
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d
an
y
d
ev
elo
p
e
d
I
DS
m
u
s
t
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n
s
u
m
e
m
in
im
al
en
er
g
y
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im
itatio
n
s
in
clu
d
e
r
ea
l
-
tim
e
im
p
lem
en
tatio
n
,
s
ca
lab
ilit
y
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n
ce
r
n
s
,
an
d
d
ataset
co
n
s
tr
ain
ts
wh
ich
m
ay
n
o
t
f
u
lly
r
ep
r
esen
t
th
e
wid
e
an
d
r
ec
en
t
v
ar
iety
o
f
Do
S
attac
k
s
.
I
n
o
r
d
er
to
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e
m
o
n
s
tr
ate
th
e
ef
f
ec
tiv
en
ess
an
d
ef
f
icien
c
y
o
f
th
e
s
u
g
g
ested
m
o
d
el,
we
co
n
d
u
cte
d
a
co
m
p
a
r
is
o
n
with
o
th
er
r
elev
an
t
p
ap
er
s
,
as sh
o
wn
in
Fig
u
r
e
6
.
Fig
u
r
e
6
.
C
o
m
p
a
r
is
o
n
o
f
o
u
r
p
r
o
p
o
s
ed
m
et
h
o
d
an
d
o
th
er
m
et
h
o
d
s
o
n
W
SN
-
DS
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
0
8
9
-
2
0
9
7
2096
4.
CO
NCLU
SI
O
N
Dete
ctin
g
d
en
ial
o
f
s
er
v
ice
in
W
SNs
b
ased
o
n
in
tr
u
s
io
n
d
etec
tio
n
with
h
ig
h
ac
cu
r
ac
y
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d
lo
w
co
m
p
u
tin
g
co
n
s
u
m
p
tio
n
r
em
ain
s
a
s
ig
n
if
ican
t
ch
allen
g
e
task
;
I
n
th
is
s
tu
d
y
,
we
h
av
e
d
em
o
n
s
tr
ated
a
lig
h
tweig
h
t
an
d
ef
f
ec
tiv
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ap
p
r
o
ac
h
tailo
r
ed
f
o
r
th
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p
u
r
p
o
s
e.
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h
r
o
u
g
h
e
x
ten
s
iv
e
ex
p
er
im
en
tatio
n
an
d
co
m
p
ar
is
o
n
c
o
n
d
u
cted
,
we
f
o
u
n
d
th
at
XGBo
o
s
t
p
r
o
v
id
es
h
ig
h
ac
cu
r
ac
y
u
p
to
9
9
.
7
3
%,
an
d
r
ed
u
ce
s
p
r
o
ce
s
s
in
g
tim
e
b
y
6
8
%
wh
en
d
etec
tin
g
m
alicio
u
s
ac
tiv
i
ties
,
co
m
p
ar
ed
to
r
a
n
d
o
m
f
o
r
est
(
R
F),
k
-
n
ea
r
est
n
eig
h
b
o
r
(
KNN)
,
an
d
s
to
ch
asti
c
g
r
ad
ien
t
d
escen
t
(
SGD)
m
o
d
els;
an
d
we
co
n
clu
d
e
th
at
X
GB
o
o
s
t
is
th
e
m
o
s
t
s
u
itab
le
m
ac
h
in
e
lear
n
in
g
clas
s
if
ier
f
o
r
W
SN
co
n
s
tr
ain
ts
s
in
ce
it
ac
h
ie
v
es
th
e
h
ig
h
est
ac
cu
r
ac
y
a
n
d
th
e
l
o
west
p
r
o
ce
s
s
in
g
tim
e
wh
ich
ar
e
a
h
ig
h
ly
im
p
o
r
tan
t
f
ac
to
r
s
in
a
d
d
itio
n
to
th
e
o
th
er
ev
alu
atio
n
m
etr
ics.
I
n
f
u
tu
r
e
wo
r
k
,
we
p
lan
to
c
o
n
s
id
er
t
h
e
in
teg
r
atio
n
o
f
th
e
p
r
o
p
o
s
ed
m
o
d
el
in
r
ea
l
-
tim
e
d
etec
tio
n
,
to
ex
am
i
n
e
its
ef
f
ec
tiv
en
ess
o
n
r
ea
l
d
ata
g
en
e
r
ated
f
r
o
m
I
o
T
d
ev
ices in
o
p
er
atio
n
al
s
ettin
g
s
.
RE
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E
R
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NC
E
S
[
1
]
M
.
A
.
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a
ms
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d
,
K
.
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l
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mr
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l
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l
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k
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.
[
3
]
B
.
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h
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.
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a
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g
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s,
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y
,
p
p
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2
5
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–
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2
,
2
0
0
8
.
[
5
]
I
.
B
a
t
r
a
,
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.
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ma
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a
v
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d
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.
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l
a
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b
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l
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c
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s
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r
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t
w
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r
k
,
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n
t
e
r
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t
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o
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a
l
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o
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r
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:
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0
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1
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a
c
.
4
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2
8
.
[
6
]
K
.
S
h
a
u
k
a
t
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t
a
l
.
,
“
P
e
r
f
o
r
m
a
n
c
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c
o
mp
a
r
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so
n
a
n
d
c
u
r
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n
t
c
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g
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s
o
f
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si
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g
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h
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r
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t
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c
h
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i
q
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s
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n
c
y
b
e
r
sec
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r
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t
y
,
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e
r
g
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s
,
v
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l
.
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o
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0
.
3
3
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n
1
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.
[
7
]
V
.
R
a
v
i
,
R
.
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h
a
g
a
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t
i
,
a
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d
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b
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p
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l
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l
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.
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5
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.
[
8
]
A
.
K
a
a
n
S
a
r
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c
a
a
n
d
P
.
A
n
g
i
n
,
“
A
n
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v
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D
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a
t
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t
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n
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o
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n
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t
w
o
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k
s
,
”
i
n
1
6
t
h
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n
t
e
r
n
a
t
i
o
n
a
l
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o
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f
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n
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t
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[
9
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M
.
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,
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.
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.
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d
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l
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h
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[
1
1
]
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.
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,
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b
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Pro
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