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
.
6
,
Decem
b
er
20
25
,
p
p
.
5
5
9
4
~
5
6
0
3
I
SS
N:
2088
-
8
7
0
8
,
DOI
: 1
0
.
1
1
5
9
1
/ijece.
v
15
i
6
.
pp
5
5
9
4
-
5
6
0
3
5594
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//ij
ec
e.
ia
esco
r
e.
co
m
Intrusio
n de
tect
io
n bas
ed on ima
g
e
trans
forma
tions
a
nd da
ta
a
ug
menta
tion
Na
da
Ali A
bo
o
d,
Asg
ha
r
A.
Asg
ha
ria
n Sa
rdro
ud
D
e
p
a
r
t
me
n
t
o
f
C
o
mp
u
t
e
r
E
n
g
i
n
e
e
r
i
n
g
,
U
r
mi
a
U
n
i
v
e
r
si
t
y
,
U
r
mi
a
,
I
r
a
n
Art
icle
I
nfo
AB
S
T
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
Feb
2
0
,
2
0
2
5
R
ev
is
ed
J
u
l 2
3
,
2
0
2
5
Acc
ep
ted
Sep
1
4
,
2
0
2
5
Th
e
in
c
re
a
sin
g
g
ro
wt
h
o
f
u
se
rs
a
n
d
c
o
m
m
u
n
ica
ti
o
n
n
e
two
r
k
s
in
d
iffere
n
t
p
latfo
rm
s
h
a
s
led
to
th
e
e
m
e
rg
e
n
c
e
o
f
v
a
rio
u
s
t
y
p
e
s
o
f
n
e
two
rk
a
tt
a
c
k
s.
in
tru
si
o
n
d
e
tec
ti
o
n
sy
ste
m
s
(IDS)
a
re
o
n
e
o
f
t
h
e
imp
o
r
tan
t
s
o
lu
t
io
n
s
to
c
o
p
e
with
th
e
se
p
r
o
b
lem
s.
An
ID
S
d
e
term
in
e
s
wh
e
th
e
r
in
c
o
m
in
g
traffic
is
in
tru
si
v
e
o
r
n
o
rm
a
l.
IDSs
o
fte
n
a
c
h
iev
e
h
i
g
h
e
fficie
n
c
y
with
m
e
th
o
d
s
b
a
se
d
o
n
d
e
e
p
n
e
u
ra
l
n
e
two
r
k
s.
Ho
w
e
v
e
r,
o
n
e
o
f
t
h
e
sh
o
rtco
m
i
n
g
s
o
f
th
e
se
m
e
th
o
d
s
is
t
h
e
lac
k
o
f
s
u
fficie
n
t
a
tt
e
n
ti
o
n
to
th
e
sp
a
ti
a
l
fe
a
tu
re
s
i
n
th
e
d
a
ta.
Th
is
re
se
a
rc
h
p
re
se
n
ts
a
n
i
n
tr
u
sio
n
d
e
tec
ti
o
n
m
e
th
o
d
b
a
se
d
o
n
ima
g
e
tran
sfo
rm
a
ti
o
n
s
a
n
d
d
a
ta
a
u
g
m
e
n
t
a
ti
o
n
is p
re
se
n
ted
.
In
th
e
p
r
o
p
o
se
d
m
e
th
o
d
,
th
e
in
tr
u
sio
n
d
e
tec
ti
o
n
p
ro
c
e
ss
is
p
e
rfo
rm
e
d
b
y
tra
n
sfo
rm
in
g
t
h
e
traffic
v
e
c
to
r
in
t
o
a
n
ima
g
e
u
sin
g
a
c
o
n
v
o
l
u
ti
o
n
a
l
n
e
u
ra
l
n
e
two
r
k
(CNN
).
Also
,
we
u
se
d
a
ta
a
u
g
m
e
n
tati
o
n
a
n
d
d
i
m
e
n
sio
n
re
d
u
c
ti
o
n
tec
h
n
iq
u
e
s
t
o
i
n
c
re
a
se
a
c
c
u
ra
c
y
a
n
d
re
d
u
c
e
c
o
m
p
lex
i
ty
i
n
th
e
p
ro
p
o
se
d
m
e
th
o
d
.
S
imu
latio
n
re
su
lt
s
o
n
n
e
two
r
k
se
c
u
rit
y
lab
o
ra
to
ry
-
k
n
o
wle
d
g
e
d
isc
o
v
e
ry
a
n
d
d
a
ta
m
in
i
n
g
(NSL
-
KD
D)
sh
o
w
th
a
t
th
e
p
r
o
p
o
se
d
IDS
c
a
n
c
las
sify
in
tru
sio
n
tr
a
ffic
with
a
n
a
c
c
u
ra
c
y
o
f
9
7
.
5
8
%
.
K
ey
w
o
r
d
s
:
Data
au
g
m
en
tatio
n
Dee
p
lear
n
in
g
Dim
en
s
io
n
r
ed
u
ctio
n
I
m
ag
e
tr
an
s
f
o
r
m
atio
n
I
n
tr
u
s
io
n
d
etec
tio
n
s
y
s
tem
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
:
Asg
h
ar
A.
Asg
h
r
ian
Sar
d
r
o
u
d
Dep
ar
tm
en
t Co
m
p
u
ter
E
n
g
i
n
e
er
in
g
,
Ur
m
ia
Un
i
v
er
s
ity
Ur
m
ia,
I
r
an
E
m
ail: a
.
asg
h
ar
ian
@
u
r
m
ia.
ac
.
ir
1.
I
NT
RO
D
UCT
I
O
N
I
n
th
e
d
ig
ital
wo
r
ld
,
v
ar
io
u
s
ty
p
es
o
f
co
m
p
u
ter
n
etwo
r
k
s
h
av
e
b
ec
o
m
e
v
ital
co
m
p
o
n
en
ts
o
f
o
r
g
an
izatio
n
s
,
i
n
d
u
s
tr
ies,
g
o
v
er
n
m
en
ts
,
an
d
in
d
iv
i
d
u
al
u
s
er
s
[
1
]
.
T
h
e
d
ev
elo
p
m
en
t
o
f
v
a
r
io
u
s
in
d
u
s
tr
ies
is
co
m
p
letely
tied
to
th
e
d
ev
elo
p
m
en
t o
f
co
m
p
u
ter
n
etwo
r
k
s
an
d
r
eq
u
ir
es m
o
r
e
atten
tio
n
[
2
]
.
I
n
th
is
r
eg
ar
d
,
with
th
e
in
cr
ea
s
in
g
d
e
p
en
d
e
n
ce
o
n
th
e
in
ter
n
et
a
n
d
n
etwo
r
k
s
y
s
tem
s
,
n
ew
m
eth
o
d
s
o
f
n
et
wo
r
k
s
ec
u
r
ity
an
d
in
f
r
astru
ctu
r
e
p
r
o
tectio
n
h
av
e
tr
an
s
f
o
r
m
ed
i
n
to
o
n
e
o
f
th
e
m
o
s
t
im
p
o
r
ta
n
t
is
s
u
es
in
th
e
f
ield
o
f
in
f
o
r
m
atio
n
an
d
co
m
m
u
n
icatio
n
tech
n
o
lo
g
y
[
3
]
.
On
th
e
o
th
e
r
h
an
d
,
n
ew
th
r
ea
ts
an
d
n
u
m
er
o
u
s
attac
k
s
em
er
g
e
in
th
e
cy
b
e
r
wo
r
ld
ev
er
y
d
a
y
th
at
ca
n
d
a
m
ag
e
th
e
s
en
s
itiv
e
an
d
v
ital
in
f
r
astru
ctu
r
e
o
f
o
r
g
an
izatio
n
s
an
d
ca
u
s
e
s
er
io
u
s
d
is
r
u
p
tio
n
s
an
d
ir
r
ep
ar
a
b
le
ec
o
n
o
m
ic
lo
s
s
es.
Desp
ite
th
e
d
ep
en
d
e
n
ce
o
f
b
u
s
in
ess
es
o
n
d
ig
ital
tech
n
o
lo
g
y
,
m
ain
tain
in
g
th
e
s
ec
u
r
ity
o
f
c
o
m
p
u
ter
n
etwo
r
k
s
an
d
p
r
o
tecti
n
g
d
ata
ag
ain
s
t
cy
b
er
-
attac
k
s
h
as
b
ec
o
m
e
a
v
ital
is
s
u
e
[
4
]
.
Netwo
r
k
s
ec
u
r
ity
r
e
f
er
s
to
a
s
et
o
f
s
tr
ateg
ies,
tech
n
o
lo
g
ies,
an
d
p
o
licies
u
s
ed
t
o
p
r
o
tect
co
m
p
u
ter
n
etwo
r
k
s
an
d
c
o
n
f
id
e
n
tial
d
a
ta
an
d
p
r
e
v
en
t
u
n
au
t
h
o
r
ized
in
tr
u
s
io
n
in
t
o
th
e
n
etwo
r
k
.
Am
o
n
g
th
e
n
etwo
r
k
th
r
ea
ts
ar
e
m
alwa
r
e
an
d
m
ali
cio
u
s
s
o
f
twar
e,
p
h
is
h
in
g
attac
k
s
,
d
is
tr
ib
u
ted
d
en
ial
o
f
s
er
v
i
ce
attac
k
s
aim
ed
at
d
is
ab
lin
g
o
n
lin
e
s
er
v
ices,
a
n
d
n
etwo
r
k
in
t
r
u
s
io
n
b
y
ex
p
l
o
itin
g
wea
k
n
ess
es
o
f
n
etwo
r
k
s
,
wh
ich
ca
n
b
e
p
r
ev
en
ted
b
y
u
s
in
g
n
etwo
r
k
s
ec
u
r
ity
[
5
]
.
W
ith
th
e
a
d
v
an
ce
m
en
t
o
f
tech
n
o
lo
g
y
,
th
e
r
e
h
a
v
e
b
ee
n
s
ig
n
if
ican
t
ch
an
g
es
in
th
e
way
cy
b
e
r
-
atta
ck
s
ar
e
ca
r
r
ied
o
u
t,
a
n
d
u
n
f
o
r
t
u
n
ately
,
th
e
n
u
m
b
er
o
f
p
eo
p
le
an
d
o
r
g
a
n
izatio
n
s
th
at
ar
e
v
ictim
s
o
f
t
h
ese
ty
p
es
o
f
attac
k
s
,
s
u
ch
as
d
is
tr
ib
u
ted
d
en
ial
o
f
s
er
v
ice
(
DDo
S)
attac
k
s
,
m
alwa
r
e,
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
I
n
tr
u
s
io
n
d
etec
tio
n
b
a
s
ed
o
n
ima
g
e
tr
a
n
s
fo
r
ma
tio
n
s
a
n
d
d
a
ta
a
u
g
men
t
a
tio
n
(
N
a
d
a
A
li A
b
o
o
d
)
5595
p
h
is
h
in
g
,
an
d
o
t
h
er
t
y
p
es
o
f
a
ttack
s
,
is
in
cr
ea
s
in
g
d
aily
.
T
o
in
cr
ea
s
e
n
etwo
r
k
s
ec
u
r
ity
an
d
p
r
ev
en
t
in
tr
u
s
io
n
an
d
co
u
n
ter
attac
k
s
,
in
tr
u
s
io
n
d
etec
tio
n
s
y
s
tem
(
I
DS)
is
an
ef
f
icien
t
an
d
f
am
iliar
m
eth
o
d
in
th
e
liter
atu
r
e.
A
wid
e
v
ar
iety
o
f
m
et
h
o
d
s
h
av
e
b
ee
n
p
r
o
p
o
s
ed
f
o
r
I
DS,
am
o
n
g
wh
ich
m
ac
h
in
e
lear
n
in
g
(
ML
)
-
b
ased
m
eth
o
d
s
ar
e
v
er
y
ef
f
ec
tiv
e
[
6
]
–
[
8
]
.
H
o
wev
er
,
less
r
esear
ch
h
as
ad
d
r
ess
ed
th
e
attr
ac
tiv
en
ess
o
f
wo
r
k
in
g
with
two
-
d
im
en
s
io
n
al
d
ata
an
d
s
p
atial
f
ea
tu
r
es.
Ou
r
co
n
tr
ib
u
tio
n
to
th
is
s
tu
d
y
is
to
p
r
esen
t
an
I
n
tr
u
s
io
n
d
etec
tio
n
m
eth
o
d
b
ased
o
n
im
ag
e
tr
an
s
f
o
r
m
atio
n
s
an
d
d
ata
a
u
g
m
e
n
tatio
n
.
I
n
th
e
p
r
o
p
o
s
ed
m
eth
o
d
,
th
e
I
n
tr
u
s
io
n
d
etec
tio
n
p
r
o
ce
s
s
is
p
er
f
o
r
m
e
d
with
a
co
n
v
o
lu
tio
n
al
n
e
u
r
al
n
etwo
r
k
an
d
b
ased
o
n
th
e
tr
af
f
ic
v
ec
to
r
tr
an
s
f
o
r
m
to
th
e
im
ag
e.
T
o
in
c
r
ea
s
e
th
e
ac
cu
r
ac
y
,
t
h
e
d
ata
au
g
m
en
tatio
n
tech
n
iq
u
e
is
u
s
ed
.
Als
o
,
to
r
ed
u
ce
th
e
co
m
p
u
tatio
n
al
co
m
p
lex
ity
,
a
d
im
en
s
io
n
ality
r
ed
u
ctio
n
m
et
h
o
d
is
in
clu
d
ed
in
th
e
f
r
am
ew
o
r
k
o
f
th
e
p
r
o
p
o
s
ed
m
eth
o
d
.
T
h
e
r
est
o
f
th
e
p
ap
e
r
is
as
f
o
llo
ws:
s
ec
tio
n
2
co
n
tain
s
th
e
b
ac
k
g
r
o
u
n
d
o
f
th
e
r
esea
r
ch
.
I
n
th
is
s
ec
tio
n
,
th
e
m
ain
I
DS
m
eth
o
d
s
ar
e
r
e
v
iewe
d
.
I
n
s
ec
tio
n
3
,
t
h
e
p
r
o
p
o
s
ed
I
n
tr
u
s
io
n
d
etec
tio
n
m
et
h
o
d
b
ased
o
n
im
ag
e
an
d
d
ata
au
g
m
e
n
tatio
n
is
p
r
e
s
en
ted
.
I
n
s
ec
tio
n
4
,
th
e
d
ata
s
et
is
in
tr
o
d
u
ce
d
an
d
th
e
s
im
u
latio
n
r
esu
lts
ar
e
d
is
cu
s
s
ed
.
Fin
ally
,
s
ec
tio
n
5
c
o
n
clu
d
es th
e
p
ap
er
.
2.
B
ACK
G
RO
UND
I
DSs
ca
n
b
e
d
iv
i
d
ed
in
to
two
ca
teg
o
r
ies,
tr
ad
itio
n
al
in
tr
u
s
i
o
n
d
etec
tio
n
m
eth
o
d
s
an
d
n
e
w
in
tr
u
s
io
n
d
etec
tio
n
m
eth
o
d
s
.
New
m
eth
o
d
s
tak
e
ad
v
a
n
tag
e
o
f
m
ac
h
in
e
lear
n
in
g
-
b
ased
m
eth
o
d
s
.
T
h
ese
m
eth
o
d
s
ar
e
s
p
ec
if
ied
in
T
ab
le
1
.
As
s
h
o
wn
in
th
e
ta
b
le,
tr
a
d
itio
n
al
m
et
h
o
d
s
u
s
u
ally
r
ely
o
n
s
tatis
tical
an
aly
s
is
o
f
tr
af
f
ic
v
ec
to
r
s
.
2
.
1
.
T
ra
ditio
na
l int
rus
io
n det
ec
t
io
n m
et
ho
ds
I
n
tr
ad
itio
n
al
m
eth
o
d
s
,
th
e
f
e
atu
r
e
v
ec
to
r
is
s
y
s
tem
atica
lly
ex
am
in
ed
.
I
n
th
ese
m
eth
o
d
s
,
th
e
b
asic
in
f
o
r
m
atio
n
av
ailab
le
in
th
e
in
co
m
in
g
tr
af
f
ic
m
u
s
t
b
e
id
en
t
if
ied
an
d
d
ec
is
io
n
s
m
ad
e
b
ased
o
n
it.
s
ig
n
atu
r
e
-
b
ased
d
etec
tio
n
[
9
]
,
r
u
le
-
b
ased
d
etec
tio
n
[
1
0
]
,
s
tatis
tical
m
eth
o
d
s
[
1
1
]
,
b
eh
a
v
io
r
-
b
ased
d
etec
tio
n
[
1
2
]
,
f
lo
w
-
b
ased
d
etec
tio
n
[
1
3
]
,
h
y
b
r
id
m
eth
o
d
s
,
a
n
d
an
o
m
aly
-
b
ased
d
etec
tio
n
a
r
e
s
o
m
e
o
f
t
h
e
m
o
s
t
im
p
o
r
tan
t
tr
ad
itio
n
al
m
eth
o
d
s
[
1
4
]
.
tr
a
d
itio
n
al
m
eth
o
d
s
ar
e
v
e
r
y
e
f
f
ec
tiv
e
f
o
r
s
tatic
a
n
d
l
o
w
-
d
im
en
s
io
n
al
tr
af
f
ic.
Ho
wev
er
,
th
e
y
u
s
u
ally
f
ail
a
g
ain
s
t
n
ew
an
d
d
y
n
am
ic
atta
ck
s
with
h
ig
h
d
im
e
n
s
io
n
s
.
Fo
r
ex
a
m
p
le,
DDOS
attac
k
s
ar
e
o
n
e
o
f
th
e
m
o
s
t
d
an
g
er
o
u
s
attac
k
s
ca
r
r
ied
o
u
t
o
n
th
e
I
n
ter
n
et.
T
h
e
g
o
al
o
f
th
es
e
attac
k
s
i
s
n
o
t
to
d
estro
y
th
e
d
esire
d
s
er
v
ice
b
u
t
to
f
o
r
ce
t
h
e
n
etwo
r
k
an
d
s
er
v
er
to
b
e
u
n
a
b
le
to
p
r
o
v
id
e
n
o
r
m
al
s
er
v
ice
b
y
tar
g
etin
g
n
etwo
r
k
b
a
n
d
wid
th
o
r
c
o
n
n
ec
tiv
ity
.
T
h
ese
attac
k
s
ar
e
ca
r
r
ied
o
u
t
b
y
s
en
d
in
g
d
ata
p
ac
k
ets
to
th
e
v
ictim
,
wh
ich
in
u
n
d
ates
th
e
v
ictim
's
n
etwo
r
k
o
r
p
r
o
ce
s
s
in
g
ca
p
ac
ity
with
i
n
f
o
r
m
atio
n
p
a
ck
ets
an
d
p
r
ev
en
ts
u
s
er
s
an
d
cu
s
to
m
er
s
f
r
o
m
ac
c
ess
in
g
th
e
s
er
v
ice.
I
n
g
e
n
er
al,
a
DDo
S
attac
k
o
n
a
s
ite
o
cc
u
r
s
wh
en
ac
c
ess
to
a
s
er
v
ice
o
r
n
etwo
r
k
r
eso
u
r
ce
is
in
ten
tio
n
ally
b
lo
ck
e
d
o
r
r
e
d
u
ce
d
b
ec
au
s
e
o
f
t
h
e
m
alicio
u
s
ac
tiv
ity
o
f
a
n
o
th
e
r
u
s
er
[
1
4
]
.
Giv
en
th
e
n
atu
r
e
o
f
th
ese
attac
k
s
,
r
ely
in
g
s
o
lely
o
n
tr
ad
itio
n
al
m
et
h
o
d
s
ca
n
n
o
t b
e
ef
f
ec
tiv
e.
2
.
2
.
M
et
ho
ds
ba
s
ed
o
n m
a
c
hin
e
lea
rning
a
lg
o
rit
hm
s
Un
lik
e
tr
ad
itio
n
al
in
tr
u
s
io
n
d
e
tectio
n
m
eth
o
d
s
,
i
n
m
eth
o
d
s
b
ased
o
n
m
ac
h
in
e
lear
n
i
n
g
alg
o
r
ith
m
s
,
a
class
if
icatio
n
p
r
o
b
lem
ar
is
es
[
1
5
]
.
T
h
ese
m
eth
o
d
s
ca
n
b
e
ca
l
led
m
o
d
er
n
in
tr
u
s
io
n
d
etec
tio
n
m
eth
o
d
s
.
I
n
th
ese
m
eth
o
d
s
,
th
er
e
is
a
tr
ain
in
g
d
a
taset.
T
h
is
d
atase
t is
d
iv
id
ed
in
to
two
p
ar
ts
: tr
ain
in
g
an
d
test
in
g
.
T
h
e
n
etwo
r
k
is
tr
ain
ed
b
ased
o
n
th
e
tr
ain
in
g
d
ata
(
tr
af
f
ic
lab
eled
as
n
o
r
m
a
l
tr
af
f
ic
o
r
attac
k
)
an
d
th
en
d
ec
id
es
th
e
n
atu
r
e
o
f
th
e
in
co
m
in
g
tr
af
f
ic
[
1
6
]
.
T
h
is
d
ec
is
io
n
is
m
ad
e
b
ased
o
n
th
e
ch
a
r
ac
ter
is
tics
o
f
th
e
in
co
m
in
g
t
r
af
f
ic.
ML
-
b
ased
I
DS
m
eth
o
d
s
ca
n
b
e
class
if
ied
in
to
d
if
f
er
en
t
ca
teg
o
r
ies.
T
h
ese
m
eth
o
d
s
m
ay
u
s
e
an
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
in
e
(
SVM)
[
1
7
]
o
r
a
n
ar
t
if
icial
n
eu
r
al
n
etwo
r
k
s
(
ANN)
[
1
8
]
in
th
e
d
etec
tio
n
p
r
o
ce
s
s
.
Ho
wev
er
,
in
r
ec
en
t
y
ea
r
s
,
m
eth
o
d
s
b
ased
o
n
d
ee
p
n
etwo
r
k
s
h
av
e
b
ee
n
v
er
y
p
r
o
m
is
in
g
an
d
ef
f
icie
n
t.
Me
th
o
d
s
b
ased
o
n
d
ee
p
n
eu
r
al
n
etwo
r
k
s
ca
n
also
h
av
e
d
if
f
e
r
en
t
s
u
b
s
ets.
T
h
e
two
im
p
o
r
tan
t
ca
teg
o
r
ies
o
f
t
h
ese
n
et
wo
r
k
s
f
o
r
in
t
r
u
s
io
n
d
etec
tio
n
ar
e
v
ec
to
r
-
b
ased
m
eth
o
d
s
an
d
r
ec
u
r
r
en
t
n
etwo
r
k
s
,
an
d
im
ag
e
-
b
ased
m
eth
o
d
s
an
d
co
n
v
o
lu
tio
n
al
n
etwo
r
k
s
.
R
ec
u
r
r
e
n
t
n
etwo
r
k
s
ar
e
th
em
s
elv
es
a
s
u
b
s
et
o
f
d
ee
p
n
etwo
r
k
s
.
I
n
th
ese
n
etwo
r
k
s
,
th
e
in
p
u
t
is
m
o
d
eled
as a
s
eq
u
e
n
ce
.
I
n
th
ese
m
eth
o
d
s
,
s
p
ec
ial
atte
n
tio
n
is
p
ai
d
to
th
e
tem
p
o
r
al
r
elatio
n
s
h
ip
b
etwe
en
s
am
p
les.
R
ec
u
r
r
en
t
n
etwo
r
k
-
b
ased
m
et
h
o
d
s
ar
e
u
s
u
ally
k
n
o
wn
as
lo
n
g
s
h
o
r
t
-
ter
m
m
em
o
r
y
(
L
STM
)
[
1
9
]
an
d
g
ated
r
ec
u
r
r
e
n
t u
n
its
(
GR
U)
[
2
0
]
n
etwo
r
k
s
.
T
h
e
r
esu
lts
o
f
p
r
e
v
io
u
s
s
tu
d
ies
s
h
o
w
th
at
L
STM
is
s
u
itab
le
f
o
r
m
o
d
elin
g
a
h
i
g
h
-
ac
cu
r
ac
y
class
if
icatio
n
m
o
d
el
an
d
its
p
er
f
o
r
m
an
ce
is
s
u
p
er
io
r
to
t
r
ad
itio
n
al
m
ac
h
in
e
lear
n
in
g
class
if
icatio
n
m
eth
o
d
s
in
b
in
ar
y
class
es.
T
h
e
L
STM
m
o
d
el
im
p
r
o
v
es
th
e
ac
cu
r
ac
y
o
f
i
n
tr
u
s
io
n
d
etec
t
io
n
an
d
p
r
o
v
id
es
a
n
ew
r
esear
ch
m
eth
o
d
f
o
r
in
tr
u
s
io
n
d
etec
tio
n
.
Ho
wev
e
r
,
i
n
L
STM
-
b
ased
m
eth
o
d
s
,
s
p
atial
f
ea
tu
r
es
in
t
h
e
d
ata
ar
e
n
o
t c
o
n
s
id
er
ed
.
I
m
ag
e
-
b
ased
m
eth
o
d
s
an
d
co
n
v
o
lu
tio
n
al
n
etwo
r
k
s
ar
e
also
ef
f
ec
tiv
e
m
eth
o
d
s
f
o
r
in
tr
u
s
io
n
d
etec
tio
n
th
at
o
p
e
r
ate
b
ased
o
n
f
ea
tu
r
e
ex
tr
ac
tio
n
f
r
o
m
s
p
atial
(
im
ag
e)
d
ata
[
2
1
]
.
Usi
n
g
th
e
in
tr
u
s
io
n
d
etec
tio
n
m
eth
o
d
with
co
n
v
o
lu
tio
n
al
n
e
u
r
al
n
etwo
r
k
(
C
NN)
h
as
a
g
r
ea
t
ad
v
an
tag
e
th
at
o
th
er
m
ac
h
in
e
lear
n
in
g
m
eth
o
d
s
d
o
n
o
t
co
n
s
id
er
.
T
h
is
ad
v
an
tag
e
is
th
at
it
co
n
s
id
er
s
th
e
s
p
atial
f
ea
tu
r
es
in
tr
af
f
ic
d
ata.
Ho
wev
er
,
a
d
ata
au
g
m
en
tatio
n
m
eth
o
d
m
u
s
t
also
b
e
u
s
ed
,
wh
ich
h
as
n
o
t
b
ee
n
co
n
s
id
er
ed
in
p
r
ev
io
u
s
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
.
6
,
Decem
b
e
r
20
25
:
5
5
9
4
-
5
6
0
3
5596
m
eth
o
d
s
.
B
ec
au
s
e
C
NN
-
b
ase
d
m
eth
o
d
s
r
eq
u
ir
e
a
lo
t
o
f
d
ata
[
1
]
,
[
2
2
]
.
On
th
e
o
t
h
er
h
an
d
,
u
s
in
g
th
e
d
ata
au
g
m
en
tatio
n
m
eth
o
d
m
ay
lead
to
a
d
ec
r
ea
s
e
in
s
p
ee
d
.
Fo
r
th
is
r
ea
s
o
n
,
it
is
n
ec
ess
ar
y
to
m
ak
e
th
e
d
ata
s
u
itab
le
f
o
r
n
etwo
r
k
tr
ai
n
in
g
with
a
d
im
en
s
io
n
ality
r
ed
u
c
tio
n
m
ec
h
an
is
m
.
I
n
t
h
is
s
tu
d
y
,
th
ese
id
ea
s
ar
e
p
r
esen
ted
in
a
n
in
tr
u
s
io
n
d
etec
tio
n
f
r
am
ewo
r
k
to
o
v
er
c
o
m
e
t
h
e
s
h
o
r
tco
m
i
n
g
s
o
f
p
r
ev
io
u
s
m
eth
o
d
s
.
T
ab
le
1
.
Gen
e
r
al
co
m
p
a
r
is
o
n
o
f
tr
ad
itio
n
al
a
n
d
n
ew
in
tr
u
s
io
n
d
etec
tio
n
m
et
h
o
d
s
N
e
w
met
h
o
d
s
T
r
a
d
i
t
i
o
n
a
l
met
h
o
d
s
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w
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k
s
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b
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e
c
t
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t
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b
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e
d
d
e
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e
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t
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9
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R
e
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r
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t
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b
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se
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d
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t
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e
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t
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1
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s
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d
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o
n
[
2
1
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,
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5
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S
t
a
t
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3.
P
RO
P
O
SE
D
M
E
T
H
O
D
Fig
u
r
e
1
s
h
o
ws
th
e
b
lo
ck
d
iag
r
am
o
f
th
e
p
r
o
p
o
s
ed
m
eth
o
d
.
T
h
e
p
r
o
p
o
s
ed
in
tr
u
s
io
n
d
etec
t
io
n
in
th
is
r
esear
ch
is
b
ased
o
n
a
s
ch
em
e
b
ased
o
n
im
a
g
e
tr
an
s
f
o
r
m
atio
n
s
an
d
d
ata
au
g
m
en
tatio
n
.
B
ased
o
n
th
is
d
iag
r
am
,
a
C
NN
n
etwo
r
k
m
u
s
t
b
e
tr
ain
ed
as
a
b
ase
m
o
d
el
at
t
h
e
f
ir
s
t
s
tep
.
T
o
tr
ain
th
is
n
etwo
r
k
,
t
h
e
in
co
m
in
g
tr
af
f
ic
m
u
s
t
b
e
tr
an
s
f
o
r
m
ed
i
n
to
an
im
ag
e.
I
n
th
is
m
eth
o
d
,
th
e
d
ata
au
g
m
en
tatio
n
tech
n
iq
u
e
is
u
s
ed
to
in
cr
ea
s
e
ac
cu
r
ac
y
.
Als
o
,
to
r
ed
u
ce
th
e
co
m
p
u
tatio
n
al
co
m
p
lex
ity
,
a
d
im
en
s
io
n
ality
r
ed
u
ctio
n
m
eth
o
d
(
p
r
in
cip
al
co
m
p
o
n
en
t
an
al
y
s
is
(
PC
A)
m
eth
o
d
[
2
6
]
)
is
in
clu
d
ed
in
th
e
f
r
am
ewo
r
k
o
f
th
e
p
r
o
p
o
s
ed
m
eth
o
d
.
Af
ter
th
e
C
NN
m
o
d
el
is
f
u
lly
tr
ain
ed
,
it
will b
e
u
s
ed
f
o
r
test
in
g
(
s
ep
ar
atin
g
n
o
r
m
al
an
d
attac
k
tr
af
f
ic
)
.
Fig
u
r
e
1
.
B
lo
ck
d
iag
r
am
o
f
th
e
p
r
o
p
o
s
ed
in
tr
u
s
io
n
d
etec
tio
n
m
eth
o
d
3
.
1
.
F
e
a
t
ure
v
ec
t
o
r
T
h
e
in
p
u
t
f
ea
tu
r
e
v
ec
to
r
s
p
ec
if
ies
th
e
tr
af
f
ic
ch
ar
ac
ter
is
tics
.
T
h
e
n
u
m
b
e
r
o
f
b
y
tes
r
ec
eiv
ed
an
d
s
en
t,
th
e
d
u
r
atio
n
o
f
th
e
ac
tiv
ity
,
th
e
n
u
m
b
er
o
f
lo
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in
attem
p
ts
,
an
d
ev
e
n
th
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I
P
ad
d
r
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s
ar
e
all
im
p
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r
tan
t
ch
ar
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ter
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tics
o
f
th
e
tr
af
f
ic
.
Fo
r
ex
am
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le,
a
f
i
x
ed
I
P
is
a
f
ix
ed
ad
d
r
ess
th
at
d
o
es
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o
t
c
h
an
g
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tim
e.
T
h
is
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
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I
SS
N:
2088
-
8
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0
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I
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tr
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as ω
.
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41
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T
h
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m
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f
f
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ataset.
I
n
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h
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4
1
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ea
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o
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th
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a
f
f
ic
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s
to
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ic
h
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e
r
ea
s
o
n
f
o
r
u
s
in
g
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e
NSL
-
KDD
d
ataset
[
2
7
]
.
W
e
u
s
e
to
d
en
o
te
th
e
to
tal
n
u
m
b
er
o
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v
ec
to
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s
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ailab
le
f
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r
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ain
in
g
.
=
{
1
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2
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(
2
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Als
o
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we
u
s
e
to
d
en
o
te
t
h
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n
u
m
b
er
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v
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to
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s
a
v
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f
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h
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v
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d
ata
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+
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=
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+
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3
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3
.
2
.
I
ma
g
e
t
ra
ns
f
o
rm
T
h
e
C
NN
n
etwo
r
k
wo
r
k
s
with
two
-
d
im
en
s
io
n
al
d
ata
f
o
r
tr
ai
n
in
g
an
d
f
ea
t
u
r
e
ex
tr
ac
tio
n
.
T
h
er
ef
o
r
e
,
it
is
b
etter
to
tr
an
s
f
o
r
m
th
e
tr
af
f
i
c
v
ec
to
r
with
a
s
p
ec
if
ic
m
ap
p
i
n
g
to
th
e
im
ag
e.
T
h
is
ca
n
b
e
r
ep
r
esen
ted
as
(
4
)
.
=
2
(
)
(
4
)
T
h
er
e
ar
e
two
g
en
er
al
m
eth
o
d
s
f
o
r
im
ag
e
tr
an
s
f
o
r
m
atio
n
.
T
h
e
d
ir
ec
t
co
p
y
m
eth
o
d
an
d
th
e
r
o
tar
y
s
h
if
t
m
eth
o
d
ar
e
s
h
o
wn
in
Fig
u
r
e
2
.
T
h
e
ad
v
a
n
tag
e
o
f
th
e
d
ir
ec
t
c
o
p
y
m
et
h
o
d
is
to
p
r
eser
v
e
s
p
atial
an
d
tem
p
o
r
al
in
f
o
r
m
atio
n
.
T
h
er
ef
o
r
e,
if
en
s
em
b
le
n
etwo
r
k
s
ar
e
u
s
ed
in
wh
ich
L
STM
o
r
GR
U
m
o
d
els
ar
e
p
r
esen
t,
it
is
b
etter
to
u
s
e
th
e
d
ir
ec
t
co
p
y
m
eth
o
d
to
p
r
eser
v
e
b
o
t
h
s
p
ati
al
an
d
tem
p
o
r
al
in
f
o
r
m
atio
n
.
Ho
wev
er
,
t
h
e
r
o
tar
y
s
h
if
t m
eth
o
d
is
m
o
r
e
ef
f
icien
t f
o
r
I
DSs
th
at
ar
e
o
n
ly
b
ased
o
n
im
ag
es.
I
n
th
is
r
esear
ch
,
th
e
r
o
tar
y
s
h
if
t m
eth
o
d
was u
s
ed
.
Fig
u
r
e
2
.
Dif
f
er
en
ce
b
etwe
en
t
h
e
d
ir
ec
t c
o
p
y
m
eth
o
d
a
n
d
th
e
r
o
tar
y
s
h
if
t
m
eth
o
d
in
im
ag
e
t
r
an
s
f
o
r
m
atio
n
3
.
3
.
Da
t
a
a
ug
m
ent
a
t
io
n
Usi
n
g
th
e
in
tr
u
s
io
n
d
etec
tio
n
m
eth
o
d
with
C
NN
h
as
a
g
r
ea
t
ad
v
an
tag
e
th
at
o
th
er
m
ac
h
i
n
e
lear
n
in
g
m
eth
o
d
s
d
o
n
o
t p
ay
atten
tio
n
to
.
T
h
is
ad
v
a
n
tag
e
is
to
p
a
y
atten
tio
n
to
th
e
s
p
atial
f
ea
tu
r
es
p
r
esen
t
in
th
e
tr
af
f
i
c
d
ata.
Ho
wev
er
,
a
d
ata
a
u
g
m
en
tatio
n
m
eth
o
d
m
u
s
t
also
b
e
u
s
ed
.
B
ec
au
s
e
C
NN
-
b
ased
m
eth
o
d
s
r
eq
u
ir
e
a
lo
t
o
f
d
ata.
On
th
e
o
th
er
h
an
d
,
u
s
in
g
th
e
d
ata
au
g
m
e
n
tatio
n
m
eth
o
d
m
ay
lead
to
a
d
ec
r
ea
s
e
in
s
p
ee
d
.
Fo
r
th
is
r
ea
s
o
n
,
a
d
ata
au
g
m
en
tatio
n
s
ch
em
e
s
h
o
u
ld
b
e
u
s
ed
in
th
e
p
r
o
p
o
s
ed
m
eth
o
d
.
I
n
th
e
p
r
o
p
o
s
ed
m
et
h
o
d
,
ea
ch
im
a
g
e
is
tr
an
s
f
o
r
m
ed
in
to
6
o
th
er
im
ag
es
ac
co
r
d
in
g
to
th
e
f
o
llo
win
g
r
elatio
n
s
h
ip
an
d
Fig
u
r
e
3
.
I
n
t
h
is
r
elatio
n
s
h
ip
,
is
th
e
tr
an
s
f
o
r
m
co
ef
f
icien
t,
wh
ich
is
g
en
er
ally
eq
u
al
to
o
n
e
b
u
t
ca
n
h
a
v
e
a
h
ig
h
er
v
alu
e
f
o
r
s
o
m
e
tr
an
s
f
o
r
m
atio
n
s
.
also
in
d
icate
s
th
e
tr
an
s
f
o
r
m
ty
p
e,
wh
ich
is
o
n
e
o
f
th
e
6
r
o
tatio
n
m
o
d
es.
→
[
0
,
∓
45
,
∓
90
,
180
]
(
5
)
̅
̅
̅
̅
̅
=
⋃
⋃
{
}
6
=
1
=
1
(
6
)
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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g
,
Vo
l.
15
,
No
.
6
,
Decem
b
e
r
20
25
:
5
5
9
4
-
5
6
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3
5598
Fig
u
r
e
3
.
Six
im
a
g
es c
r
ea
ted
with
th
e
d
ata
au
g
m
en
tatio
n
tec
h
n
iq
u
e
3
.
4
.
Di
m
ens
io
na
lity
re
du
ct
io
n
A
d
im
en
s
io
n
r
ed
u
ctio
n
alg
o
r
it
h
m
is
n
ec
ess
ar
y
a
f
ter
th
e
d
ata
au
g
m
en
tatio
n
tech
n
i
q
u
e
in
th
e
p
r
o
p
o
s
ed
m
eth
o
d
.
T
h
is
is
b
ec
au
s
e
th
e
d
ata
au
g
m
en
tatio
n
alg
o
r
ith
m
is
u
s
ed
in
th
e
p
r
o
p
o
s
ed
m
et
h
o
d
.
Fo
r
th
is
r
ea
s
o
n
,
it is
n
ec
ess
ar
y
to
r
ed
u
ce
th
e
d
im
e
n
s
io
n
s
o
f
th
e
in
p
u
t
d
ata
to
th
e
C
NN
n
etwo
r
k
.
T
h
er
e
ar
e
v
ar
io
u
s
m
eth
o
d
s
f
o
r
d
im
en
s
io
n
ality
r
ed
u
ctio
n
.
I
n
t
h
is
r
esear
ch
,
th
e
f
ast
PC
A
m
eth
o
d
is
u
s
ed
.
T
h
is
m
eth
o
d
is
a
g
o
o
d
m
eth
o
d
f
o
r
wo
r
k
in
g
with
tr
af
f
ic
im
ag
es
a
n
d
h
as
lo
w
co
m
p
le
x
ity
.
PC
A
is
a
p
o
p
u
la
r
d
ata
r
ed
u
ctio
n
te
ch
n
iq
u
e
in
m
ac
h
in
e
lear
n
in
g
a
n
d
a
s
u
b
g
r
o
u
p
o
f
u
n
s
u
p
er
v
is
ed
alg
o
r
ith
m
s
.
T
h
ese
alg
o
r
ith
m
s
ar
e
u
s
ed
to
id
e
n
tif
y
lin
ea
r
p
atter
n
s
in
h
ig
h
-
d
im
e
n
s
io
n
al
d
ata
[
2
8
]
.
T
h
e
m
ain
g
o
al
o
f
PC
A
is
to
f
i
n
d
a
lo
wer
d
im
e
n
s
io
n
al
s
p
ac
e
th
at
p
r
eser
v
es
th
e
m
ax
im
u
m
v
ar
ian
ce
in
d
ata.
T
h
is
m
ea
n
s
f
in
d
in
g
a
s
et
o
f
n
e
w
ax
es
th
at
ac
c
o
m
m
o
d
ate
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e
m
ax
im
u
m
am
o
u
n
t
o
f
d
is
p
er
s
io
n
in
th
e
d
ata.
T
h
ese
n
ew
a
x
es
ar
e
k
n
o
wn
as
p
r
in
cip
al
c
o
m
p
o
n
en
ts
o
r
PC
s
.
B
y
r
ed
u
cin
g
th
e
d
im
en
s
io
n
ality
o
f
th
e
d
ata,
P
C
A
r
ed
u
ce
s
co
m
p
u
tatio
n
al
c
o
m
p
lex
ity
a
n
d
h
elp
s
im
p
r
o
v
e
th
e
ef
f
icie
n
cy
o
f
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
s
.
I
t
also
r
ed
u
ce
s
th
e
n
o
is
e
in
th
e
d
ata
b
y
f
o
cu
s
in
g
o
n
th
e
p
r
i
n
cip
al
co
m
p
o
n
en
ts
an
d
h
elp
s
im
p
r
o
v
e
th
e
ac
cu
r
ac
y
o
f
m
ac
h
in
e
lear
n
i
n
g
alg
o
r
it
h
m
s
.
I
n
ad
d
itio
n
,
PC
A
f
ac
ilit
at
es
th
e
v
is
u
aliza
tio
n
o
f
th
e
d
ata
i
n
a
lo
wer
d
im
en
s
io
n
al
s
p
ac
e
b
y
r
ed
u
cin
g
th
e
d
i
m
en
s
io
n
ality
o
f
t
h
e
d
ata.
3
.
5
.
CNN
t
r
a
ini
ng
a
nd
t
esting
C
NN
n
eu
r
al
n
etwo
r
k
is
o
n
e
o
f
th
e
m
o
s
t
im
p
o
r
tan
t
d
ee
p
n
e
u
r
al
n
etwo
r
k
s
w
h
o
s
e
ar
ch
itectu
r
e
f
o
llo
ws
a
s
im
ilar
p
atter
n
to
th
e
co
n
n
ec
tio
n
o
f
b
r
ain
n
eu
r
o
n
s
to
ea
ch
o
th
er
an
d
is
m
o
d
eled
af
te
r
th
e
v
is
u
al
co
r
tex
s
ec
tio
n
.
Sm
all
g
r
o
u
p
s
o
f
v
is
u
a
l
n
eu
r
o
n
s
ar
e
e
n
g
ag
e
d
in
ea
ch
p
ar
t
alo
n
e
an
d
th
e
co
m
b
in
ati
o
n
o
f
th
ese
n
eu
r
o
n
s
an
d
th
e
cr
ea
tio
n
o
f
in
ter
co
n
n
e
cted
n
etwo
r
k
s
ca
u
s
es
th
e
v
is
io
n
o
f
an
a
r
ea
.
Fig
u
r
e
4
s
h
o
ws
th
e
ar
ch
itectu
r
e
o
f
th
e
C
NN
n
etwo
r
k
u
s
ed
.
T
h
e
c
o
n
v
o
lu
ti
o
n
la
y
er
is
t
h
e
b
ac
k
b
o
n
e
o
f
an
y
C
NN
wo
r
k
in
g
m
o
d
el.
T
h
is
lay
er
is
th
e
lay
er
wh
er
e
p
i
x
el
-
by
-
p
ix
el
s
ca
n
n
in
g
o
f
im
ag
es
tak
es
p
la
ce
an
d
cr
ea
tes
a
f
ea
tu
r
e
m
a
p
to
d
ef
i
n
e
f
u
tu
r
e
class
if
icatio
n
s
.
Po
o
lin
g
is
also
u
s
ed
as d
ata
s
am
p
lin
g
.
Fig
u
r
e
4
.
C
NN
n
etwo
r
k
ar
ch
it
ec
tu
r
e
in
in
tr
u
s
io
n
d
etec
tio
n
A
f
t
e
r
d
e
s
i
g
n
i
n
g
t
h
e
m
o
d
e
l
,
a
ll
d
a
t
a
m
u
s
t
b
e
d
i
v
i
d
e
d
i
n
t
o
t
wo
t
r
a
i
n
i
n
g
a
n
d
t
e
s
t
s
e
c
t
i
o
n
s
.
T
h
e
t
r
a
i
n
i
n
g
p
r
o
c
e
s
s
u
s
e
s
a
u
g
m
e
n
te
d
d
a
t
a
.
H
e
n
c
e
,
b
y
s
u
b
s
t
i
t
u
ti
n
g
t
h
e
a
u
g
m
e
n
t
e
d
d
a
t
a
i
n
s
t
e
a
d
o
f
t
h
e
t
r
ai
n
i
n
g
d
a
t
a
,
w
e
h
a
v
e
:
=
∪
̅
̅
̅
(
7
)
|
|
=
6
+
(
8
)
I
n
wh
ich
,
t
h
e
to
tal
d
ata
is
d
e
n
o
ted
b
y
,
th
e
test
d
ata
b
y
,
an
d
th
e
a
u
g
m
en
te
d
tr
ain
in
g
d
ata
b
y
̅
̅
̅
.
Fin
ally
,
th
e
n
etwo
r
k
is
tr
ain
e
d
with
d
ef
a
u
lt
h
y
p
er
p
ar
a
m
eter
s
an
d
th
e
n
it
co
u
l
d
class
if
y
I
n
tr
u
s
io
n
an
d
n
o
r
m
al
tr
af
f
ic.
T
h
e
p
r
o
p
o
s
ed
m
eth
o
d
o
f
th
is
s
tu
d
y
o
n
ly
co
m
p
letes
th
e
I
n
tr
u
s
io
n
d
etec
tio
n
o
p
er
ati
o
n
b
ased
o
n
im
ag
es.
T
h
is
is
a
n
ew
id
ea
th
at
h
as
b
e
en
d
is
cu
s
s
ed
in
a
f
ew
wo
r
k
s
.
T
h
e
p
r
o
p
o
s
ed
m
eth
o
d
s
h
o
u
ld
o
v
er
co
m
e
two
m
ain
p
r
o
b
lem
s
;
th
e
s
ec
o
n
d
p
r
o
b
le
m
ar
is
es
d
u
e
to
th
e
s
o
lu
tio
n
o
f
th
e
f
ir
s
t
p
r
o
b
lem
.
I
n
f
ac
t,
th
e
C
NN
n
etwo
r
k
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
I
n
tr
u
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n
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etec
tio
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a
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eq
u
ir
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e
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n
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s
p
atial
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ata.
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h
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r
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lem
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i
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h
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ed
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eth
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d
with
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ata
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g
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en
tatio
n
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u
t
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e
lf
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cr
ea
s
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m
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lex
ity
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k
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y
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tem
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y
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ed
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cin
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e
d
im
en
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io
n
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.
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e
g
e
n
er
al
s
ch
em
e
o
f
I
n
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tio
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o
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f
ast an
d
ac
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r
ate
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n
d
is
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ased
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n
ly
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n
im
ag
es.
Alg
o
r
ith
m
1
o
f
th
e
p
r
o
p
o
s
ed
m
eth
o
d
is
b
r
ief
ly
p
r
esen
ted
.
Alg
o
rit
h
m
1
1.
Set the parameters of the neural network and the number of input data samples.
2. Define traffic vectors with 41 features:
ω
=
{
f
e
j
}
3. Divide the input data into two parts, training and testing
:
D
R
=
D
T
s
+
D
T
r
4. Transform the vector data into an image using the rotary method:
I
in
p
u
t
=
f
2
(
ω
)
5. Multiply the image data by the data augmentation method by 6.
I
in
p
u
t
→
[
I
in
p
u
t
0
,
I
in
p
u
t
∓
45
,
I
in
p
u
t
∓
90
,
I
in
p
u
t
1
8
0
]
6. Perform dimensionality reduction on the data using the PCA method.
D
T
r
̅
̅
̅
̅
̅
=
⋃
⋃
{
a
j
D
gg
j
ω
i
}
6
i
=
1
M
i
=
1
7. Train the CNN network with the following data.
8. Evaluate the proposed method using test data and report the accuracy as
output.
⋃
⋃
{
a
j
D
gg
j
ω
i
}
6
i
=
1
M
i
=
1
9. End
4.
SI
M
UL
A
T
I
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N
A
ND
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V
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N
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h
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ased
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NSL
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ataset
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th
e
e
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ataset.
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h
e
N
SL
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KDD
d
ataset
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h
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at
aset
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as
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ata
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o
r
tr
ai
n
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d
2
2
,
5
4
4
d
ata
f
o
r
test
in
g
.
T
ab
le
2
s
h
o
ws
th
e
d
is
tr
ib
u
tio
n
o
f
attac
k
s
in
th
is
d
ataset.
As
it
is
clea
r
,
th
e
d
is
tr
ib
u
tio
n
o
f
d
ata
in
th
is
d
ataset
i
s
n
o
t
b
alan
ce
d
f
o
r
tr
af
f
ic
class
if
icatio
n
.
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wev
er
,
in
th
is
r
esear
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,
o
u
r
g
o
al
is
o
n
ly
b
in
ar
y
class
if
icatio
n
o
f
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n
tr
u
s
io
n
.
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n
o
th
er
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d
s
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eter
m
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m
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f
ic.
I
n
t
h
is
r
esp
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t,
th
is
d
ataset
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b
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ce
d
.
T
h
e
d
is
tr
ib
u
ti
o
n
o
f
attac
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s
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t
h
e
two
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f
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e
tr
ain
a
n
d
test
is
s
h
o
wn
in
th
e
d
iag
r
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f
Fig
u
r
e
5
.
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th
e
d
iag
r
a
m
o
f
Fig
u
r
e
6
s
h
o
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Do
S
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s
h
a
v
e
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lar
g
e
s
h
a
r
e
in
th
is
d
a
t
a
s
et
.
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h
i
s
at
t
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c
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2
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a
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Tr
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t
(
%)
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%)
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mal
54
52
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o
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P
r
o
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e
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6
R
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2
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2
R
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.
5
1
Fig
u
r
e
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.
Dis
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u
tio
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f
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in
th
e
two
tr
ain
a
n
d
test
s
ec
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n
s
0
10
20
30
40
50
60
N
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D
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R2L
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Per
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Ty
p
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T
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)
T
e
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t S
e
t (%
)
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
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g
,
Vo
l.
15
,
No
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6
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Decem
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r
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n
etwo
r
k
s
o
th
at
th
e
s
er
v
er
lo
s
es
th
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ab
ilit
y
to
r
esp
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n
d
to
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ea
l
u
s
er
s
'
r
eq
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ests
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h
er
ef
o
r
e,
in
th
is
ca
s
e,
th
e
n
u
m
b
er
o
f
lo
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s
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p
ts
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m
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en
t
a
n
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e
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n
u
s
u
al.
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r
th
is
r
ea
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th
is
attac
k
h
as tak
en
u
p
a
lar
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e
p
o
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tio
n
o
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th
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ata
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et.
4
.
1
.
Resul
t
s
a
nd
d
is
cu
s
s
io
n
T
ab
le
3
p
r
esen
ts
t
h
e
s
im
u
latio
n
r
esu
lts
f
o
r
t
h
r
ee
ca
s
es.
Fo
r
t
h
is
p
u
r
p
o
s
e,
a
co
m
p
ar
is
o
n
o
f
t
h
e
r
esu
lts
b
etwe
en
th
e
two
p
ar
am
eter
s
ac
cu
r
ac
y
an
d
s
p
ee
d
h
as
b
e
en
m
ad
e.
T
h
e
f
ir
s
t
ca
s
e
is
I
DS
with
o
u
t
Data
au
g
m
en
tatio
n
.
I
n
th
is
ca
s
e,
th
e
n
ee
d
f
o
r
C
NN
n
etwo
r
k
tr
ai
n
in
g
d
ata
h
as
n
o
t
b
ee
n
in
cr
ea
s
e
d
.
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h
e
s
ec
o
n
d
ca
s
e
is
I
DS
with
Data
au
g
m
en
tati
o
n
an
d
with
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u
t
PC
A.
Als
o
,
i
n
th
e
th
ir
d
ca
s
e,
th
e
PC
A
al
g
o
r
ith
m
is
ad
d
ed
to
r
ed
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ce
d
im
e
n
s
io
n
s
.
T
ab
le
3
.
C
o
m
p
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r
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o
n
o
f
r
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lts
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etwe
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two
m
o
d
es o
f
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ac
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d
s
p
ee
d
f
o
r
th
r
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D
S
M
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t
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d
A
c
c
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r
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Tr
a
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t
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1
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D
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C
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4
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D
S
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t
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n
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C
A
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7
.
5
8
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2
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latio
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lts
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h
a
t
n
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t
u
s
in
g
th
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d
im
en
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io
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a
lity
r
ed
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ctio
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al
g
o
r
ith
m
lea
d
s
to
th
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co
m
p
lex
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f
th
e
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alg
o
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h
m
.
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co
u
r
s
e,
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m
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lex
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n
ly
in
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e
tr
ain
in
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cy
cle.
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wev
er
,
th
e
d
ata
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g
m
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al
g
o
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in
th
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p
r
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p
o
s
ed
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DS
in
cr
ea
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th
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ac
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r
ac
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ec
a
u
s
e
m
o
r
e
p
atter
n
s
ar
e
lear
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ed
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y
th
e
n
etwo
r
k
.
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h
e
h
ig
h
est
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cu
r
ac
y
is
wh
en
th
e
P
C
A
alg
o
r
ith
m
is
r
em
o
v
ed
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h
e
r
ea
s
o
n
is
th
at,
in
th
is
ca
s
e,
th
e
n
etwo
r
k
lear
n
s
all
th
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f
ea
tu
r
es
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u
t
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ed
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,
wh
ich
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e
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ir
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m
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u
tatio
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wev
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u
s
t
b
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ad
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h
e
b
ar
ch
ar
t
in
Fig
u
r
e
6
s
h
o
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e
co
m
p
ar
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o
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th
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r
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th
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a
r
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eter
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n
d
s
p
ee
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r
th
r
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u
r
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6
.
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ar
ch
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r
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o
m
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ar
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r
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th
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ar
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eter
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ee
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ar
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o
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o
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eth
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d
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et
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s
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ad
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T
ab
le
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h
e
m
eth
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d
o
f
Kim
et
a
l.
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2
2
]
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as
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d
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.
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r
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r
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r
ea
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g
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eth
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ab
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m
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r
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u
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ased
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d
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cc
u
r
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f
th
e
Kim
et
a
l.
[
2
2
]
m
eth
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th
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-
lev
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d
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u
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ased
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[
2
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,
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ased
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5
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N
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6
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R
.
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[
7
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U
.
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