I
nd
o
ne
s
ia
n J
o
urna
l o
f
E
lect
rica
l En
g
ineering
a
nd
Co
m
pu
t
er
Science
Vo
l.
38
,
No
.
3
,
J
u
n
e
20
25
,
p
p
.
1
642
~
1
6
5
1
I
SS
N:
2502
-
4
7
5
2
,
DOI
: 1
0
.
1
1
5
9
1
/ijeecs.v
38
.i
3
.
pp
1
6
4
2
-
1
6
5
1
1642
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//ij
ee
cs.ia
esco
r
e.
co
m
SVM
a
lg
o
rithm
-
b
a
sed a
no
ma
ly
det
ection in
net
wo
rk
log
s a
nd
firewall lo
g
s
J
o
hn
B
enit
o
J
esu
da
s
a
n P
et
e
r
1
,
Nit
in Ra
k
esh
2
,
P
utt
a
s
wa
m
y
Re
k
ha
3
,
T
a
mm
ineni
Sree
la
t
ha
4
,
Velus
a
m
y
Su
j
a
t
ha
5
,
S
u
r
u
l
iv
e
l
u
M
uthum
a
rila
k
s
hm
i
6
,
Sh
a
nm
ug
a
m Suja
t
ha
7
1
C
y
b
e
r
,
D
a
t
a
S
c
i
e
n
c
e
a
n
d
En
g
i
n
e
e
r
i
n
g
,
C
a
p
i
t
a
l
O
n
e
S
e
r
v
i
c
e
s,
LL
C
,
R
i
c
h
m
o
n
d
,
U
S
A
2
S
y
m
b
i
o
si
s
I
n
st
i
t
u
t
e
o
f
Te
c
h
n
o
l
o
g
y
(
S
I
T)
,
S
y
mb
i
o
s
i
s I
n
t
e
r
n
a
t
i
o
n
a
l
(
D
e
e
m
e
d
U
n
i
v
e
r
s
i
t
y
)
,
N
a
g
p
u
r
,
I
n
d
i
a
3
D
e
p
a
r
t
me
n
t
o
f
El
e
c
t
r
o
n
i
c
s a
n
d
C
o
m
mu
n
i
c
a
t
i
o
n
En
g
i
n
e
e
r
i
n
g
,
B
N
M
I
n
st
i
t
u
t
e
o
f
T
e
c
h
n
o
l
o
g
y
,
B
a
n
g
a
l
o
r
e
,
I
n
d
i
a
4
D
e
p
a
r
t
me
n
t
o
f
El
e
c
t
r
o
n
i
c
s a
n
d
C
o
m
mu
n
i
c
a
t
i
o
n
En
g
i
n
e
e
r
i
n
g
,
K
o
n
e
r
u
La
k
s
h
ma
i
a
h
E
d
u
c
a
t
i
o
n
F
o
u
n
d
a
t
i
o
n
,
V
a
d
d
e
sw
a
r
a
m,
G
u
n
t
u
r
,
I
n
d
i
a
5
D
e
p
a
r
t
me
n
t
o
f
El
e
c
t
r
o
n
i
c
s a
n
d
C
o
m
mu
n
i
c
a
t
i
o
n
En
g
i
n
e
e
r
i
n
g
,
S
h
r
e
e
S
a
t
h
y
a
m C
o
l
l
e
g
e
o
f
E
n
g
i
n
e
e
r
i
n
g
a
n
d
T
e
c
h
n
o
l
o
g
y
,
S
a
l
e
m,
I
n
d
i
a
6
D
e
p
a
r
t
me
n
t
o
f
C
o
m
p
u
t
e
r
S
c
i
e
n
c
e
a
n
d
E
n
g
i
n
e
e
r
i
n
g
,
C
h
e
n
n
a
i
I
n
st
i
t
u
t
e
o
f
T
e
c
h
n
o
l
o
g
y
,
K
u
n
d
r
a
t
h
u
r
,
I
n
d
i
a
7
D
e
p
a
r
t
me
n
t
o
f
B
i
o
m
e
d
i
c
a
l
E
n
g
i
n
e
e
r
i
n
g
,
S
a
v
e
e
t
h
a
S
c
h
o
o
l
o
f
E
n
g
i
n
e
e
r
i
n
g
,
S
a
v
e
e
t
h
a
I
n
st
i
t
u
t
e
o
f
M
e
d
i
c
a
l
a
n
d
Te
c
h
n
i
c
a
l
S
c
i
e
n
c
e
s
,
S
a
v
e
e
t
h
a
U
n
i
v
e
r
si
t
y
,
T
h
a
n
d
a
l
a
m,
I
n
d
i
a
Art
icle
I
nfo
AB
S
T
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
Feb
24
,
2
0
2
4
R
ev
is
ed
Dec
21
,
2
0
2
4
Acc
ep
ted
Feb
28
,
2
0
2
5
Th
e
p
u
rp
o
se
o
f
m
a
n
y
a
d
v
a
n
c
e
d
fo
rm
s
o
f
c
y
b
e
ra
tt
a
c
k
is
t
o
d
e
c
e
iv
e
th
e
m
o
n
it
o
rs
,
a
n
d
a
s
a
re
su
lt
,
t
h
e
se
a
tt
a
c
k
s
o
ften
in
v
o
lv
e
se
v
e
ra
l
k
i
n
d
s,
lev
e
ls,
a
n
d
sta
g
e
s.
Ex
isti
n
g
a
n
o
m
a
ly
d
e
t
e
c
ti
o
n
sy
ste
m
s
o
f
ten
e
x
a
m
in
e
lo
g
s
o
r
traffic
fo
r
i
n
d
ica
ti
o
n
s
o
f
a
tt
a
c
k
s,
i
g
n
o
r
i
n
g
a
n
y
a
d
d
it
io
n
a
l
a
n
a
ly
sis
re
g
a
rd
in
g
a
tt
a
c
k
p
ro
c
e
d
u
re
s.
T
h
is
is
d
o
n
e
to
s
a
v
e
ti
m
e
.
F
o
r
e
x
a
m
p
le,
traffic
d
e
tec
ti
o
n
tec
h
n
o
l
o
g
ies
c
a
n
o
n
l
y
id
e
n
ti
fy
th
e
a
tt
a
c
k
flo
ws
in
a
g
e
n
e
ra
l
se
n
se
.
S
ti
ll
,
th
e
y
c
a
n
n
o
t
re
c
o
n
str
u
c
t
t
h
e
a
tt
a
c
k
e
v
e
n
t
p
ro
c
e
ss
o
r
e
x
p
o
se
th
e
p
re
se
n
t
c
o
n
d
i
ti
o
n
o
f
th
e
n
e
two
rk
n
o
d
e
.
I
n
a
d
d
it
io
n
,
th
e
lo
g
s
k
e
p
t
b
y
th
e
firew
a
ll
a
re
s
ig
n
ifi
c
a
n
t
so
u
rc
e
s
o
f
e
v
id
e
n
c
e
;
n
e
v
e
rth
e
les
s,
th
e
y
a
re
stil
l
c
h
a
ll
e
n
g
in
g
t
o
d
e
c
i
p
h
e
r.
T
h
is
p
a
p
e
r
i
n
tro
d
u
c
e
s
s
u
p
p
o
rt
v
e
c
to
r
m
a
c
h
i
n
e
a
lg
o
rit
h
m
-
b
a
se
d
An
o
m
a
ly
d
e
tec
ti
o
n
(S
VMA)
in
n
e
tw
o
rk
lo
g
s
a
n
d
firew
a
ll
l
o
g
s
to
p
ro
v
i
d
e
ro
b
u
st
se
c
u
rit
y
a
g
a
in
st
c
y
b
e
ra
tt
a
c
k
s.
Th
is
m
e
c
h
a
n
ism
c
o
n
sists
o
f
th
re
e
m
o
d
u
les
:
p
re
p
ro
c
e
ss
in
g
,
fe
a
tu
re
se
lec
ti
o
n
a
n
d
a
n
o
m
a
ly
d
e
tec
ti
o
n
.
T
h
e
g
e
n
e
ti
c
a
lg
o
rit
h
m
(G
A)
se
lec
ts
th
e
b
e
tt
e
r
fe
a
tu
re
fro
m
th
e
in
p
u
t
.
F
i
n
a
l
ly
,
t
h
e
su
p
p
o
rt
v
e
c
to
r
m
a
c
h
i
n
e
(S
VM)
is
o
late
s
a
n
a
n
o
m
a
ly
p
o
we
rfu
ll
y
.
Th
e
in
v
e
stig
a
ti
o
n
a
l
o
u
tco
m
e
s
il
l
u
stra
te
t
h
a
t
th
e
S
VM
A
m
in
imiz
e
s
th
e
re
q
u
ired
ti
m
e
to
se
lec
t
th
e
fe
a
tu
re
s a
n
d
e
n
h
a
n
c
e
s th
e
d
e
tec
ti
o
n
a
c
c
u
ra
c
y
.
K
ey
w
o
r
d
s
:
C
y
b
er
attac
k
s
Fire
wall
lo
g
s
Gen
etic
alg
o
r
ith
m
Netwo
r
k
lo
g
s
Su
p
p
o
r
t
v
ec
to
r
m
ac
h
in
e
alg
o
r
ith
m
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
:
Nitin
R
ak
esh
Sy
m
b
io
s
is
I
n
s
titu
te
o
f
T
ec
h
n
o
lo
g
y
(
SIT
)
,
Sy
m
b
io
s
is
I
n
ter
n
atio
n
al
(
Dee
m
ed
Un
i
v
er
s
ity
)
Nag
p
u
r
,
Ma
h
ar
ash
tr
a,
I
n
d
ia
E
m
ail: n
itin
.
r
ak
esh
@
g
m
ail.
co
m
1.
I
NT
RO
D
UCT
I
O
N
C
y
b
er
attac
k
s
alm
o
s
t
alwa
y
s
le
av
e
tr
ac
es
o
n
th
e
d
ev
ices
th
at
m
ak
e
u
p
th
e
n
etwo
r
k
.
T
h
e
att
ac
k
v
ec
to
r
o
f
an
attac
k
er
o
f
ten
in
v
o
lv
es
j
u
m
p
in
g
v
ia
n
u
m
e
r
o
u
s
r
o
u
ter
s
o
r
s
er
v
er
s
b
ef
o
r
e
th
e
attac
k
er
u
p
lo
ad
s
m
alicio
u
s
co
d
e
(
f
o
r
ex
am
p
le,
an
XSS
s
cr
ip
t)
,
im
p
lan
ts
m
alwa
r
e
(
f
o
r
ex
am
p
le,
a
b
o
tn
et)
,
a
n
d
s
u
b
m
its
an
u
n
ap
p
r
o
v
e
d
p
atch
th
at
co
n
tain
s
d
an
g
er
o
u
s
p
ay
lo
ad
s
[
1
]
.
I
n
m
o
s
t
ca
s
es,
th
e
tr
ac
es
o
f
a
cy
b
er
attac
k
m
ay
b
e
f
o
u
n
d
s
ca
tter
ed
o
v
er
th
e
ac
tiv
ity
lo
g
s
o
f
a
n
u
m
b
er
o
f
d
if
f
e
r
en
t
c
o
m
p
u
ter
s
b
elo
n
g
in
g
to
v
a
r
io
u
s
v
ictim
s
[
2
]
.
C
o
n
v
er
s
ely
,
s
in
ce
th
e
lo
g
s
ar
e
s
ca
tter
ed
o
v
er
a
v
ar
iety
o
f
u
n
co
n
n
ec
ted
s
o
u
r
ce
s
,
it is
s
till
n
ec
ess
ar
y
to
h
av
e
h
u
m
an
in
ter
v
en
tio
n
in
o
r
d
er
to
p
iece
to
g
eth
e
r
th
e
co
n
tex
tu
al
in
f
o
r
m
atio
n
o
f
ea
ch
b
ad
f
o
o
tp
r
in
t.
T
h
er
e
f
o
r
e,
r
ely
i
n
g
o
n
th
e
lo
g
s
f
o
r
an
o
m
aly
id
e
n
tif
icatio
n
will n
o
t p
r
o
v
id
e
th
e
d
esire
d
r
esu
lts
[
3
]
.
T
h
is
k
in
d
o
f
in
f
o
r
m
atio
n
is
an
ex
am
p
le
o
f
wh
a
t
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:
2502
-
4
7
5
2
S
V
M a
lg
o
r
ith
m
-
b
a
s
ed
a
n
o
m
a
ly
d
etec
tio
n
in
n
etw
o
r
k
lo
g
s
a
n
d
fir
ewa
ll lo
g
s
(
Jo
h
n
B
en
ito
J
esu
d
a
s
a
n
P
eter
)
1643
is
k
n
o
wn
as
co
m
p
lem
en
tar
y
e
v
id
en
ce
.
Ho
wev
e
r
,
u
s
in
g
m
er
ely
th
e
d
ata
f
r
o
m
n
etwo
r
k
tr
a
f
f
ic
is
in
ad
eq
u
ate
to
co
r
r
ec
tly
id
e
n
tify
attac
k
p
atter
n
s
an
d
p
r
o
v
id
e
a
co
m
p
r
eh
en
s
i
v
e
u
n
d
er
s
tan
d
in
g
o
f
ass
au
lts
.
I
n
ad
d
itio
n
,
f
ir
ewa
lls
p
r
o
v
id
e
th
e
f
u
n
ctio
n
o
f
co
n
tr
o
l
g
ates
th
at
n
etwo
r
k
p
ac
k
ets
m
u
s
t
g
o
th
r
o
u
g
h
t
o
p
ass
th
r
o
u
g
h
th
e
f
ir
ewa
ll.
Sy
s
tem
ad
m
in
is
tr
ato
r
s
ar
e
r
esp
o
n
s
ib
le
f
o
r
co
n
f
ig
u
r
in
g
f
ir
ewa
lls
in
a
m
an
n
er
th
at
is
co
m
p
lian
t w
ith
th
e
s
tan
d
ar
d
s
o
f
th
eir
b
u
s
in
ess
[
4
]
.
Fire
wall
is
an
ess
en
tial
p
ar
t o
f
to
d
ay
’
s
n
etwo
r
k
s
b
ec
au
s
e
o
f
th
e
cr
itical
r
o
le
th
ey
ac
t
in
s
af
eg
u
ar
d
in
g
th
e
n
etwo
r
k
ag
ai
n
s
t
an
y
th
r
ea
ts
,
wh
eth
er
th
ey
o
r
i
g
in
ate
f
r
o
m
in
s
id
e
th
e
n
etwo
r
k
o
r
f
r
o
m
th
e
o
u
ts
id
e.
B
ec
au
s
e
th
ey
d
ep
en
d
o
n
th
e
co
m
p
a
n
y
im
p
lem
e
n
tin
g
th
e
f
ir
ewa
ll,
th
ese
r
eg
u
latio
n
s
ar
e
s
u
b
ject
t
o
ch
a
n
g
e.
I
n
ad
d
itio
n
,
b
ec
a
u
s
e
o
f
t
ec
h
n
o
lo
g
ical
im
p
r
o
v
em
en
ts
a
n
d
th
e
e
v
er
-
s
h
if
tin
g
b
eh
av
io
r
o
f
th
e
s
u
r
r
o
u
n
d
i
n
g
e
n
v
ir
o
n
m
en
t,
r
e
v
is
in
g
th
ese
g
u
id
elin
es
is
a
lab
o
r
-
in
ten
s
iv
e
a
n
d
o
n
g
o
in
g
p
r
o
ce
s
s
[
5
]
.
Actio
n
s
ar
e
d
o
n
e
,
s
u
ch
as
Gr
an
t
,
Om
it
,
R
ef
u
s
e
,
o
r
R
ea
d
j
u
s
t
-
b
o
th
.
I
f
ch
o
o
s
e
th
e
wr
o
n
g
s
tep
to
tak
e
wh
ile
h
an
d
lin
g
a
s
ess
io
n
,
it
m
ig
h
t
le
ad
to
s
ec
u
r
ity
v
u
ln
er
ab
ilit
ies,
wh
ich
wo
u
ld
th
en
m
ak
e
it
p
o
s
s
ib
le
f
o
r
u
n
wan
ted
ev
en
ts
to
tak
e
p
lace
,
s
u
ch
as t
h
e
s
h
u
td
o
wn
o
f
d
ev
ices,
th
e
lo
s
s
o
f
s
er
v
ice,
in
d
ir
ec
t lo
s
s
o
f
p
r
o
f
it,
o
r
th
e
leak
in
g
o
f
s
en
s
itiv
e
m
ater
ials
[
6
]
.
On
th
e
o
th
er
h
an
d
,
t
h
e
m
an
a
g
em
en
t
o
f
a
n
d
th
e
d
ev
elo
p
m
en
t
o
f
th
e
r
u
les
th
at
d
ec
id
e
t
h
e
p
r
o
p
er
ac
tio
n
s
h
av
e
g
r
o
wn
in
tr
icate
an
d
p
r
o
n
e
to
m
is
tak
es
[
7
]
.
Ma
ch
in
e
l
ea
r
n
in
g
(
ML
)
tech
n
iq
u
es
h
av
e
en
o
r
m
o
u
s
p
r
o
m
is
e
in
v
ar
io
u
s
d
is
cip
lin
es,
in
clu
d
in
g
cy
b
er
s
ec
u
r
ity
.
T
h
er
e
is
a
g
r
o
win
g
u
n
d
er
s
tan
d
in
g
,
in
th
e
r
ea
lm
o
f
cy
b
er
s
ec
u
r
ity
,
o
f
t
h
e
p
o
s
itiv
e
u
s
ag
e
o
f
ML
alg
o
r
ith
m
s
.
T
h
ese
r
ec
o
r
d
s
k
ee
p
a
wea
l
th
o
f
in
f
o
r
m
atio
n
th
at
is
h
id
d
en
f
r
o
m
v
iew.
T
h
is
in
f
o
r
m
atio
n
,
co
u
p
led
with
th
e
p
atter
n
s
in
th
e
p
r
o
p
e
r
ties
o
f
th
e
n
etwo
r
k
tr
af
f
ic,
m
a
y
b
e
f
o
u
n
d
an
d
w
o
r
k
ed
v
ia
ML
t
o
d
ev
elo
p
m
o
d
els
th
at
h
elp
id
en
tify
n
etwo
r
k
r
is
k
s
[
8
]
.
T
r
ai
n
in
g
t
h
ese
s
y
s
tem
s
en
ab
les
th
em
to
p
r
o
d
u
ce
war
n
in
g
s
wh
en
d
a
n
g
er
s
ar
e
d
is
co
v
er
ed
,
id
e
n
tify
n
ew
f
o
r
m
s
o
f
m
alwa
r
e
an
d
s
ec
u
r
e
en
ter
p
r
is
es
’
p
er
s
o
n
al
d
etails.
I
n
ad
d
itio
n
,
ML
a
p
p
r
o
ac
h
es
v
ital
to
o
ls
f
o
r
m
an
y
y
ea
r
s
o
win
g
to
th
eir
ca
p
ac
ity
to
im
p
r
o
v
e
d
ec
is
io
n
-
m
ak
i
n
g
with
o
u
t
th
e
n
ee
d
f
o
r
d
ir
ec
t
h
u
m
an
in
v
o
lv
e
m
en
t,
wh
ich
p
av
es
th
e
way
f
o
r
m
o
r
e
ef
f
ec
tiv
e
an
aly
s
is
o
n
a
b
r
o
a
d
e
r
s
ca
le
[
9
]
.
Hier
ar
ch
ical
an
o
m
aly
-
b
ased
d
etec
tio
n
o
f
d
is
s
im
in
ated
d
o
m
ain
n
am
e
s
y
s
te
m
(
DNS)
a
ttack
s
(
HAD
A)
in
s
id
e
c
o
r
p
o
r
ate
n
etwo
r
k
s
.
T
h
is
s
y
s
tem
r
ec
o
g
n
izes
s
tr
an
g
e
p
atter
n
s
in
DNS
tr
a
f
f
ic.
T
h
is
s
y
s
tem
m
ak
es
it
p
o
s
s
ib
le
to
id
en
tif
y
m
alicio
u
s
ac
tio
n
s
;
f
o
r
e
x
am
p
le,
d
en
i
al
o
f
s
er
v
ice
(
Do
S)
attac
k
s
.
T
h
is
s
o
lu
tio
n
u
s
es
n
u
m
er
o
u
s
lay
e
r
s
o
f
an
aly
s
is
to
d
is
tin
g
u
is
h
b
etwe
en
b
en
ig
n
a
s
well
as
m
alicio
u
s
DNS
tr
af
f
ic,
wh
ich
u
ltima
tely
r
esu
lts
in
an
i
m
p
r
o
v
ed
s
ec
u
r
ity
p
o
s
tu
r
e
f
o
r
th
e
n
etwo
r
k
.
Ho
wev
er
,
th
is
m
ec
h
an
is
m
ca
n
’
t
d
etec
t
th
e
f
i
r
ewa
ll
lo
g
s
.
Fu
r
th
er
m
o
r
e,
it
d
etec
ts
th
e
Do
S
attac
k
,
an
d
it
i
s
n
o
t
ab
le
to
d
etec
t
th
e
an
o
m
aly
[
1
0
]
.
SVM
with
clu
s
ter
in
g
to
d
if
f
er
en
tiate
am
o
n
g
n
o
r
m
al
as
well
as
ab
n
o
r
m
al
t
r
af
f
ic
p
atter
n
s
.
C
lu
s
ter
in
g
ass
is
ts
in
o
r
g
an
izin
g
d
ata
p
o
in
ts
th
at
ar
e
q
u
ite
s
im
ilar
to
o
n
e
an
o
th
e
r
.
T
h
e
m
o
d
el
im
p
r
o
v
es
th
e
ac
cu
r
ac
y
o
f
an
o
m
aly
d
etec
tio
n
(
AD)
in
n
et
wo
r
k
tr
af
f
ic
d
u
e
to
th
e
in
teg
r
atio
n
o
f
d
if
f
er
en
t a
p
p
r
o
ac
h
es,
wh
ich
in
tu
r
n
m
ak
es
th
e
m
o
d
el
m
o
r
e
ef
f
icien
t
an
d
d
ep
en
d
a
b
le
[
1
1
]
.
I
s
o
latio
n
F
o
r
est
an
d
two
d
ee
p
a
u
to
en
c
o
d
er
s
ar
e
u
s
ed
in
th
e
p
r
o
p
o
s
ed
u
n
s
u
p
er
v
is
ed
lear
n
in
g
f
o
r
lo
g
m
ess
ag
e
an
o
m
aly
id
en
tific
atio
n
th
at
u
s
es
th
e
m
o
d
el.
T
h
e
a
u
to
en
co
d
e
r
n
etwo
r
k
s
ar
e
u
s
ed
f
ir
s
t
f
o
r
tr
ai
n
in
g
an
d
f
ea
tu
r
e
ex
tr
a
ctio
n
an
d
s
u
b
s
eq
u
en
tly
f
o
r
AD
,
wh
ile
th
e
is
o
latio
n
f
o
r
est
is
u
tili
ze
d
f
o
r
p
o
s
itiv
e
s
am
p
le
p
r
ed
ictio
n
[
1
2
]
.
T
h
e
GA
is
u
s
ed
f
o
r
f
ea
tu
r
e
s
elec
tio
n
,
wh
il
e
th
e
Naiv
e
B
ay
es
class
if
icatio
n
m
eth
o
d
is
u
s
ed
f
o
r
d
ata
a
n
aly
s
is
.
T
o
o
p
tim
ize
t
h
e
f
ea
tu
r
e
s
u
b
s
et
f
o
r
AD
,
a
G
A
is
u
s
ed
to
ch
o
o
s
e
th
e
ch
ar
ac
ter
is
tics
f
r
o
m
th
e
d
ataset
th
at
ar
e
m
o
s
t
r
elev
an
t.
T
h
e
d
ata
th
at
was
p
ick
ed
f
o
r
an
aly
s
is
is
th
en
s
en
t
in
to
a
Naiv
e
B
ay
es
c
lass
if
ier
,
wh
ich
lo
o
k
s
f
o
r
ir
r
eg
u
lar
ities
in
th
e
d
ata
th
at
was
co
llec
ted
v
ia
f
o
g
co
m
p
u
tin
g
.
T
h
is
h
y
b
r
id
a
p
p
r
o
ac
h
im
p
r
o
v
e
s
th
e
ac
cu
r
ac
y
o
f
AD
wh
ile
s
im
u
ltan
eo
u
s
ly
in
cr
ea
s
in
g
its
ef
f
icien
cy
[
1
3
]
.
On
e
-
class
SVM
alg
o
r
ith
m
is
d
ev
el
o
p
ed
f
o
r
o
n
e
-
class
class
if
icati
o
n
,
wh
ic
h
m
ea
n
s
th
at
it
is
tr
ai
n
ed
o
n
ly
o
n
th
e
d
ata
b
elo
n
g
in
g
to
th
e
n
o
r
m
al
clas
s
an
d
r
ec
o
g
n
izes
a
n
o
m
alies.
I
n
th
is
p
ar
ticu
lar
s
ce
n
ar
io
,
it
r
ef
er
s
to
th
e
lo
g
s
g
en
er
ated
b
y
th
e
J
u
n
ip
er
r
o
u
ter
d
ev
ices.
T
h
e
s
co
p
e
o
f
th
is
r
esear
ch
is
to
en
h
an
ce
th
e
s
ec
u
r
ity
an
d
p
er
f
o
r
m
an
ce
m
o
n
ito
r
in
g
ca
p
a
b
ilit
ies
o
f
J
u
n
ip
e
r
r
o
u
ter
d
ev
i
ce
s
b
y
r
eliab
ly
id
en
tify
in
g
a
n
d
f
lag
g
i
n
g
ab
er
r
an
t
p
atter
n
s
o
r
o
cc
u
r
r
e
n
ce
s
in
s
id
e
th
e
r
o
u
ter
lo
g
s
[
1
4
]
.
T
h
e
J
4
8
tech
n
iq
u
e
f
o
r
ML
is
b
ased
o
n
d
ec
is
io
n
tr
ee
s
,
an
d
th
e
u
p
d
ated
v
er
s
io
n
c
o
n
tain
s
Ken
d
all
’
s
co
r
r
elatio
n
co
ef
f
icien
t,
wh
ich
ev
alu
ates
th
e
d
eg
r
ee
to
th
at
two
v
ar
iab
les
ar
e
co
n
n
ec
ted
wi
th
o
n
e
an
o
th
e
r
.
T
h
e
p
u
r
p
o
s
e
o
f
t
h
is
r
esear
ch
is
to
in
v
esti
g
ate
if
ad
d
in
g
Ken
d
all
’
s
co
r
r
elatio
n
co
ef
f
icien
t
in
to
th
e
J
4
8
alg
o
r
ith
m
f
o
r
class
if
icatio
n
task
s
im
p
r
o
v
es its
ac
cu
r
ac
y
an
d
r
eliab
ilit
y
[
1
5
]
.
I
n
in
ter
n
et
o
f
th
i
n
g
s
(
I
o
T
)
co
n
tex
ts
,
d
ata
o
f
p
o
o
r
q
u
ality
m
ay
in
clu
d
e
in
f
o
r
m
ati
o
n
th
at
is
n
o
is
y
,
f
r
ag
m
en
tar
y
,
o
r
er
r
o
n
eo
u
s
.
T
h
e
p
u
r
p
o
s
e
o
f
t
h
is
m
ec
h
an
is
m
is
to
an
aly
ze
h
o
w
ML
m
o
d
els
ca
n
s
u
cc
ess
f
u
lly
d
ea
l
with
s
u
ch
d
if
f
icu
lt
d
ata
s
itu
atio
n
s
.
T
h
is
m
ec
h
an
is
m
r
ep
r
esen
ts
an
AD
m
o
d
el
wh
ich
ca
n
ef
f
e
ctiv
ely
d
is
tin
g
u
is
h
u
n
ex
p
ec
te
d
p
atter
n
s
o
r
o
cc
u
r
r
en
ce
s
with
in
I
o
T
d
ata
d
esp
ite
th
e
lo
w
q
u
ality
o
f
th
e
d
ata
[
1
6
]
.
A
tech
n
iq
u
e
t
h
at
u
s
es
d
is
tan
ce
to
id
en
tif
y
u
n
u
s
u
al
ch
ar
ac
te
r
is
tics
in
s
id
e
lo
g
d
ata.
L
o
g
f
il
es
in
clu
d
e
r
ec
o
r
d
s
o
f
a
v
a
r
iety
o
f
ac
tio
n
s
an
d
o
cc
u
r
r
en
c
es,
an
d
it
is
ess
en
t
ial
f
o
r
th
e
s
ec
u
r
ity
o
f
th
e
s
y
s
tem
an
d
f
o
r
tr
o
u
b
lesh
o
o
ti
n
g
p
u
r
p
o
s
es
to
id
en
tify
an
y
ir
r
eg
u
lar
ities
with
in
th
ese
d
ata.
T
h
e
d
is
tan
ce
s
b
etwe
en
ch
ar
ac
ter
is
tics
in
lo
g
e
n
tr
ies
a
r
e
b
ein
g
m
ea
s
u
r
e
d
as
p
ar
t
o
f
t
h
e
s
u
g
g
ested
tech
n
iq
u
e
[
1
7
]
.
T
h
e
ap
p
r
o
ac
h
m
ay
d
is
co
v
er
f
ea
tu
r
es
th
at
c
o
n
s
id
er
ab
ly
d
if
f
er
f
r
o
m
th
e
p
r
ed
ic
ted
p
atter
n
s
b
y
c
o
m
p
u
tin
g
t
h
ese
d
is
tan
ce
s
an
d
co
m
p
ar
in
g
th
em
.
T
h
is
m
eth
o
d
allo
ws
th
e
id
en
tific
atio
n
o
f
ab
er
r
an
t
ac
tiv
ity
in
s
id
e
lo
g
f
iles
,
g
iv
in
g
s
y
s
tem
ad
m
in
is
tr
ato
r
s
an
d
s
ec
u
r
ity
ex
p
er
ts
a
h
elp
f
u
l
to
d
is
c
o
v
er
p
o
s
s
ib
le
s
ec
u
r
ity
co
n
ce
r
n
s
o
r
b
e
h
av
io
r
s
th
at
ar
e
n
o
t
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
5
2
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
38
,
No
.
3
,
J
u
n
e
20
25
:
1
6
4
2
-
1
6
5
1
1644
n
o
r
m
al
[
18
]
.
A
n
AD
s
y
s
tem
k
n
o
wn
as
Sm
ar
tR
ad
ar
was
d
ev
elo
p
ed
p
r
im
ar
ily
f
o
r
u
s
e
in
s
c
en
ar
io
s
th
at
in
clu
d
e
r
em
o
te
wo
r
k
.
Sm
ar
tR
ad
ar
u
s
es
th
e
SVM
[
1
9
]
f
o
r
AD
[
2
0
]
.
T
h
e
s
y
s
tem
is
a
n
i
n
tellig
en
t
p
iece
o
f
s
o
f
twar
e
d
ev
elo
p
e
d
to
m
o
n
ito
r
an
d
ass
ess
th
e
g
o
in
g
s
-
o
n
in
r
em
o
te
wo
r
k
in
g
e
n
v
ir
o
n
m
en
ts
.
I
t
is
ab
le
to
d
if
f
er
e
n
tiate
b
etwe
en
u
s
u
al
p
atter
n
s
o
f
b
eh
av
io
r
wh
e
n
wo
r
k
in
g
r
em
o
tely
an
d
a
b
er
r
a
n
t
o
r
s
u
s
p
icio
u
s
ac
tiv
ity
th
an
k
s
to
th
e
u
s
e
o
f
SVM
[
2
1
]
.
T
h
e
m
aj
o
r
o
b
jectiv
e
o
f
Sm
ar
tR
ad
ar
is
to
im
p
r
o
v
e
s
af
ety
a
n
d
p
r
o
d
u
cti
v
ity
b
y
r
ec
o
g
n
izin
g
an
d
n
o
tify
in
g
u
s
er
s
o
f
p
o
te
n
tially
d
an
g
e
r
o
u
s
o
r
u
n
lawf
u
l
b
eh
a
v
io
r
s
in
r
em
o
te
wo
r
k
in
g
s
ettin
g
s
[
2
2
]
.
An
ar
tific
ial
n
eu
r
al
n
etwo
r
k
to
r
ap
id
ly
s
if
t
th
r
o
u
g
h
Sp
ar
k
lo
g
s
d
ata
an
d
o
p
e
r
atin
g
s
y
s
tem
o
b
s
er
v
atio
n
to
p
r
ec
is
ely
n
o
tice
an
d
ca
teg
o
r
i
ze
an
o
m
alo
u
s
b
eh
a
v
io
r
s
b
y
t
h
e
Sp
ar
k
r
esil
ien
t
f
ea
tu
r
es
[
2
3
]
.
A
g
r
ap
h
-
b
ased
ap
p
r
o
ac
h
to
u
n
s
u
p
er
v
is
ed
lo
g
an
o
m
aly
id
en
tific
atio
n
h
as
b
ee
n
d
e
v
elo
p
ed
an
d
n
am
ed
L
o
g
s
2
Gr
ap
h
s
.
T
h
is
ap
p
r
o
ac
h
f
ir
s
t
tr
an
s
f
o
r
m
s
ev
e
n
t
lo
g
s
in
to
g
r
ap
h
s
th
at
ar
e
a
ttrib
u
ted
,
d
i
r
ec
ted
,
a
n
d
weig
h
ted
an
d
t
h
en
u
s
es
g
r
ap
h
n
eu
r
al
n
etwo
r
k
s
to
co
n
d
u
ct
AD
at
th
e
g
r
a
p
h
le
v
el
[
2
4
]
.
Usi
n
g
d
ata
m
in
in
g
an
d
ML
,
d
is
tr
ib
u
ted
f
ir
ewa
lls
m
ay
im
p
lem
en
t
AD
o
f
r
u
les,
wh
ich
lo
o
k
s
f
o
r
u
n
u
s
u
al
b
eh
a
v
io
r
.
T
h
e
s
o
lu
tio
n
t
h
at
h
as
b
e
en
s
u
g
g
ested
is
u
s
ed
o
n
lar
g
e
lo
g
s
th
at
co
m
e
f
r
o
m
d
is
tr
ib
u
ted
f
ir
ewa
lls
[
2
5
]
.
C
lo
u
d
co
m
p
u
tin
g
[
2
6
]
allo
ws
th
e
p
r
o
p
o
s
ed
s
eizu
r
e
p
r
ed
ictio
n
m
ec
h
an
is
m
is
m
o
r
e
ac
ce
s
s
ib
le
an
d
s
ca
lab
le
[
2
7
]
.
C
lo
u
d
-
b
ased
d
ec
is
io
n
-
s
u
p
p
o
r
t
s
y
s
tem
[
2
8
]
ap
p
ly
in
g
K
-
n
ea
r
est
n
eig
h
b
o
r
s
’
alg
o
r
ith
m
with
I
o
T
s
tr
u
ctu
r
e
s
y
s
tem
in
teg
r
ates
I
o
T
d
ev
ices
[
2
9
]
to
co
llect
an
d
tr
an
s
m
it
th
e
d
ata
[
3
0
]
.
Fu
zz
y
lo
g
ic
with
b
ay
esian
d
ec
is
io
n
alg
o
r
ith
m
is
o
lates
th
e
Do
S
p
ac
k
ets
ef
f
icien
tly
.
T
h
e
f
u
zz
y
lo
g
ic
s
y
s
tem
is
ap
p
l
ied
to
v
alid
ate
th
e
d
ata
p
ac
k
et
s
[
3
1
]
.
2.
P
RO
P
O
SE
D
M
E
T
H
O
D
T
h
is
wo
r
k
m
o
s
tly
in
ten
d
s
to
ex
ec
u
te
AD
d
u
r
in
g
in
co
r
p
o
r
atin
g
th
e
n
etwo
r
k
with
f
ir
e
wall
lo
g
s
.
Par
ticu
lar
ly
,
th
e
n
etwo
r
k
lo
g
s
an
d
f
ir
ewa
ll
lo
g
s
ar
e
in
co
r
p
o
r
ated
b
y
ass
o
ciatio
n
r
u
les.
T
h
is
ap
p
r
o
ac
h
ca
n
ef
f
icien
tly
en
h
an
ce
th
e
AD
p
er
f
o
r
m
an
c
e
an
d
r
eb
u
ild
t
h
e
n
etwo
r
k
attac
k
p
r
o
ce
d
u
r
e
th
at
c
h
an
g
es
to
co
m
p
r
eh
e
n
d
a
n
en
tire
v
iew
o
f
th
e
n
etwo
r
k
s
u
r
r
o
u
n
d
in
g
s
.
T
h
e
en
v
ir
o
n
m
en
talis
m
o
f
t
h
e
n
etwo
r
k
is
h
ig
h
l
y
co
m
p
licated
s
in
ce
th
er
e
a
r
e
s
o
m
an
y
d
if
f
er
e
n
t
k
in
d
s
o
f
att
ac
k
s
.
Fo
r
ex
am
p
le,
b
o
tn
ets
h
av
e
to
f
ir
s
t
tr
an
s
m
it
co
n
tr
o
l
i
n
s
tr
u
ctio
n
s
to
ea
ch
co
m
m
an
d
an
d
co
n
tr
o
l
(
C
&
C
)
Se
r
v
er
b
ef
o
r
e
s
en
d
in
g
th
e
m
to
t
h
e
co
n
tr
o
lled
h
o
s
t.
I
n
co
n
tr
ast,
wo
r
m
s
h
av
e
to
ea
r
lies
t
u
p
lo
ad
m
alicio
u
s
co
d
e
to
th
e
tar
g
et
h
o
s
t
b
ef
o
r
e
in
f
ec
tin
g
ad
d
itio
n
al
co
m
p
u
ter
s
v
ia
t
h
e
tar
g
et
h
o
s
t.
As
a
r
esu
lt,
th
e
n
etwo
r
k
lo
g
s
an
d
tr
af
f
ic
f
lo
ws
h
av
e
th
e
p
o
ten
tial
to
p
lay
ex
tr
em
ely
s
ig
n
if
ican
t
r
o
les in
t
h
e
id
en
tific
atio
n
o
f
cy
b
er
attac
k
s
.
I
n
itially
,
we
r
etr
iev
e
th
e
tr
af
f
ic
f
lo
ws
v
ia
p
o
r
t
m
i
r
r
o
r
i
n
g
a
n
d
th
en
we
u
s
e
T
C
PDUMP
to
ex
tr
ac
t
re
lev
an
t
tr
af
f
ic
p
ar
am
eter
s
(
f
o
r
in
s
tan
ce
,
th
e
p
o
r
t
o
f
th
e
s
en
d
er
an
d
th
e
r
ec
eiv
e
r
,
th
e
p
r
o
t
o
co
l
n
u
m
b
er
,
t
h
e
I
P
o
f
th
e
s
en
d
er
a
n
d
r
ec
eiv
e
r
,
th
e
p
ac
k
et
s
ize,
an
d
th
e
tr
an
s
m
i
t
tim
e)
.
T
h
is
g
r
an
ts
to
co
llect
d
ata
f
o
r
d
etec
tin
g
an
o
m
alies.
I
n
ad
d
itio
n
,
we
g
et
lo
g
in
f
o
r
m
atio
n
f
r
o
m
th
e
i
n
n
er
r
o
u
ter
s
,
f
ir
ewa
lls
,
s
witch
es,
an
d
s
er
v
er
s
o
f
th
e
g
atew
ay
.
Nex
t,
we
u
s
e
ass
o
ciatio
n
r
u
les
in
an
ef
f
o
r
t
to
r
e
v
er
s
e
en
g
in
ee
r
th
e
m
a
p
p
in
g
r
elatio
n
s
th
at
ex
is
t
b
etwe
en
th
e
lo
g
s
an
d
t
h
e
tr
af
f
ic.
I
n
co
n
clu
s
io
n
,
th
e
r
ec
o
v
er
e
d
co
n
n
ec
tio
n
s
h
av
e
t
h
e
p
o
ten
t
ial
to
b
e
p
u
t
to
u
s
e
in
o
r
d
e
r
to
p
r
o
d
u
ce
t
h
e
tim
e
s
tam
p
s
o
f
lo
g
en
tr
ies
an
d
r
e
b
u
ild
th
e
d
e
v
elo
p
m
e
n
t
o
f
attac
k
s
.
T
h
is
m
ec
h
an
is
m
co
n
tain
s
3
p
r
o
ce
s
s
es:
p
r
ep
r
o
c
ess
in
g
,
f
ea
tu
r
e
ex
t
r
ac
tio
n
,
a
n
d
AD
.
I
n
itially
,
th
e
l
o
g
c
o
llecto
r
s
p
r
im
ar
ily
g
ath
er
t
h
e
tr
af
f
ic
an
d
lo
g
s
c
o
n
ce
iv
e
d
as
th
e
in
p
u
t
d
ata.
T
h
is
m
ec
h
an
is
m
u
s
es
a
GA
alg
o
r
ith
m
f
o
r
t
h
e
f
ilter
in
g
p
r
o
ce
s
s
th
at
f
ilter
s
th
e
u
n
r
elate
d
d
ata.
T
h
e
f
ea
tu
r
e
s
elec
tio
n
co
n
tain
s
f
iv
e
co
m
p
o
n
en
ts
:
co
r
r
elat
io
n
o
f
tr
af
f
ic
th
at
ex
am
in
es
th
e
m
alicio
u
s
tr
a
f
f
ic
p
ac
k
et
s
,
co
r
r
elatio
n
o
f
te
m
p
o
r
al
to
g
et
th
e
tim
e
f
ea
t
u
r
es
o
f
m
alicio
u
s
,
co
r
r
elatio
n
o
f
c
o
m
b
in
atio
n
t
o
f
o
r
m
th
e
s
tr
o
n
g
s
ig
n
if
ica
n
ce
o
f
m
alicio
u
s
tr
af
f
ic,
T
C
P
f
lag
to
r
ec
o
r
d
th
e
f
o
r
war
d
in
g
an
d
r
e
p
ly
in
g
tr
af
f
i
c
d
ata,
an
d
attac
k
r
e
-
en
ac
tm
e
n
t.
Nex
t
,
we
ap
p
ly
a
n
SVM
t
o
d
etec
t
th
e
an
o
m
aly
ef
f
ec
tiv
ely
.
Pre
p
r
o
ce
s
s
in
g
:
t
h
e
p
r
ep
r
o
ce
s
s
in
g
p
r
o
ce
d
u
r
e
is
em
p
lo
y
ed
to
t
h
e
d
ata
ea
r
lier
th
an
an
y
an
aly
s
is
to
b
u
ild
th
e
f
ile
r
ea
d
y
f
o
r
u
tili
za
tio
n
b
ased
o
n
tr
ain
i
n
g
a
n
d
test
in
g
.
T
h
e
m
ain
o
b
jectiv
e
o
f
th
is
p
r
o
c
ess
is
d
ata
lo
ad
in
g
,
f
ilter
in
g
,
m
an
i
p
u
latin
g
,
an
d
ex
ch
an
g
in
g
th
e
d
ata.
T
h
e
l
o
g
d
ataset
co
n
s
is
ts
o
f
n
u
m
er
o
u
s
d
ata
en
tr
ies
in
th
at
th
e
d
en
y
ac
tio
n
p
r
esen
ts
2
.
6
8
%
a
n
d
Allo
w
d
en
o
tes
8
8
.
2
3
%
o
f
th
e
d
ata.
T
h
is
in
d
icate
s
th
at
th
e
d
ata
lo
s
t
f
r
o
m
in
eq
u
ality
wh
en
eq
u
ated
to
o
t
h
e
r
class
lab
els
.
2
.
1
.
GA
-
ba
s
ed
f
ea
t
ure
s
elec
t
io
n
Fre
q
u
en
tly
,
s
ev
er
al
cy
b
e
r
attac
k
s
,
f
o
r
ex
am
p
le,
p
h
is
h
in
g
em
ails
an
d
b
o
tn
ets,
n
ec
ess
itate
r
ep
ea
ted
ly
f
o
r
war
d
in
g
th
e
co
m
m
a
n
d
s
th
r
o
u
g
h
p
r
o
g
r
am
s
.
I
n
m
o
s
t
ca
s
es,
th
e
wr
ap
p
er
tech
n
iq
u
e
is
s
u
p
er
io
r
th
a
n
a
f
ilter
ap
p
r
o
ac
h
b
ec
a
u
s
e
th
e
p
r
o
ce
s
s
o
f
f
ea
tu
r
e
s
elec
tio
n
is
o
p
tim
ized
.
T
h
e
tr
ad
itio
n
al
wr
ap
p
er
m
eth
o
d
is
ac
co
m
p
lis
h
ed
in
two
s
tag
es:
s
ea
r
ch
in
g
f
o
r
a
s
u
b
s
et
o
f
ch
ar
ac
ter
is
tics
,
an
d
ev
alu
atin
g
th
e
f
ea
tu
r
es
th
at
wer
e
ch
o
s
en
.
I
t
co
n
tin
u
es
to
cy
cle
th
r
o
u
g
h
th
e
f
ir
s
t
s
ta
g
e
an
d
s
ec
o
n
d
s
tag
e
u
n
til
eith
er
th
e
tar
g
et
lev
el
o
f
lear
n
in
g
p
er
f
o
r
m
an
ce
is
r
ea
ch
e
d
o
r
s
p
ec
if
ied
h
altin
g
c
o
n
d
itio
n
s
ar
e
m
et.
Ho
wev
er
,
d
u
e
to
th
e
f
a
ct
th
at
th
e
wr
ap
p
er
tech
n
iq
u
e
is
ex
ce
s
s
iv
ely
co
s
tly
,
it n
ee
d
s
ad
d
itio
n
al
tim
e
an
d
r
aises
co
m
p
u
tatio
n
al
co
m
p
l
ex
ity
.
GA
is
an
o
p
tim
al
s
ea
r
ch
alg
o
r
ith
m
th
at
is
ab
le
to
b
e
ex
p
ed
itio
u
s
ly
u
s
ed
in
f
ea
tu
r
e
s
elec
ti
o
n
.
A
GA
co
n
tain
s
th
r
ee
f
u
n
ctio
n
s
lik
e
r
ep
r
o
d
u
ctio
n
,
cr
o
s
s
o
v
er
,
an
d
m
u
tatio
n
.
R
ep
r
o
d
u
ctio
n
p
i
ck
s
o
u
t
th
e
s
tr
o
n
g
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:
2502
-
4
7
5
2
S
V
M a
lg
o
r
ith
m
-
b
a
s
ed
a
n
o
m
a
ly
d
etec
tio
n
in
n
etw
o
r
k
lo
g
s
a
n
d
fir
ewa
ll lo
g
s
(
Jo
h
n
B
en
ito
J
esu
d
a
s
a
n
P
eter
)
1645
s
tr
in
g
s
;
cr
o
s
s
o
v
er
ag
g
r
e
g
ates
g
o
o
d
s
tr
in
g
s
th
at
d
ev
elo
p
b
ett
er
o
f
f
s
p
r
in
g
;
an
d
m
u
tatio
n
alte
r
s
a
s
tr
in
g
lo
ca
lly
to
p
r
o
v
id
e
a
b
etter
s
tr
in
g
.
First,
th
e
GA
g
en
e
r
ates
a
p
o
p
u
latio
n
o
f
p
o
s
s
ib
le
s
o
lu
tio
n
s
b
y
s
ee
d
in
g
it
with
r
a
n
d
o
m
in
f
o
r
m
atio
n
.
A
b
it
m
ay
b
e
u
s
ed
to
r
ep
r
esen
t
ea
ch
c
h
r
o
m
o
s
o
m
al
g
en
e
in
a
d
ataset,
d
ep
en
d
in
g
o
n
t
h
e
ch
ar
ac
ter
is
tics
o
f
th
e
d
ataset
as
a
wh
o
le.
Af
te
r
th
at,
in
d
iv
i
d
u
als
ar
e
g
r
ad
e
d
ac
co
r
d
in
g
to
th
e
f
itn
ess
f
u
n
ctio
n
th
ey
p
o
s
s
ess
.
2
.
2
.
SVM
-
ba
s
ed
a
no
m
a
ly
de
t
ec
t
io
n
I
d
en
tify
in
g
an
ac
tio
n
in
to
n
o
r
m
al
an
d
an
o
m
aly
ca
teg
o
r
i
es
b
ased
o
n
th
e
SVM
alg
o
r
i
th
m
.
SVM
tech
n
o
lo
g
y
is
u
s
ed
to
ca
te
g
o
r
ize
d
is
s
im
ilar
ty
p
es
o
f
d
ata
c
o
m
in
g
f
r
o
m
a
wid
e
v
a
r
iety
o
f
f
ield
s
.
T
h
ese
h
av
e
b
ee
n
u
s
ed
f
o
r
c
h
allen
g
es
in
v
o
lv
in
g
th
e
class
if
icatio
n
o
f
two
class
es
o
f
d
ata,
an
d
th
ey
ar
e
r
elev
an
t
to
lin
ea
r
a
s
well
as
n
o
n
-
lin
ea
r
d
ata
class
if
icatio
n
en
d
ea
v
o
r
s
.
T
h
e
SVM
[
3
1
]
c
o
n
s
tr
u
cts
a
h
y
p
er
p
la
n
e
th
at
d
iv
id
es
d
ata
in
to
v
ar
io
u
s
class
es.
W
h
en
f
o
r
ec
asti
n
g
th
e
m
ar
g
in
s
,
a
n
o
n
-
lin
ea
r
class
if
ier
will
u
tili
ze
a
s
elec
tio
n
o
f
k
er
n
el
f
u
n
ctio
n
s
.
E
x
p
lo
itin
g
th
e
m
a
r
g
in
s
am
o
n
g
h
y
p
e
r
p
lan
es
is
th
e
p
r
im
a
r
y
g
o
al
o
f
th
e
k
er
n
el
f
u
n
ctio
n
s
,
all
o
f
wh
ic
h
ar
e
d
escr
ib
ed
ab
o
v
e.
T
h
e
SVM
h
as
s
ee
n
ex
ten
s
iv
e
u
s
e
in
im
ag
e
p
r
o
ce
s
s
in
g
a
n
d
p
atter
n
id
en
tific
atio
n
.
Fig
u
r
e
1
ex
p
lain
s
SVM
alg
o
r
ith
m
-
b
ased
AD.
T
h
e
r
ad
ial
b
asis
f
u
n
ctio
n
(
R
B
F)
k
er
n
el
is
th
e
k
ey
co
m
p
o
n
e
n
t
o
f
th
e
s
u
g
g
ested
s
y
s
tem
f
o
r
ca
r
r
y
in
g
o
u
t
th
e
ex
ec
u
tio
n
o
f
th
e
SVM.
T
h
e
k
er
n
el
o
p
er
atio
n
co
n
s
titu
tes
an
ad
v
an
tag
e
th
at
tr
an
s
lates
in
p
u
t
d
ata
to
a
h
ig
h
d
im
en
s
io
n
al
s
p
ac
e
to
b
est s
eg
r
eg
ate
th
e
p
r
o
v
id
ed
d
ata
i
n
to
th
e
attac
k
class
e
s
to
wh
ich
th
ey
b
elo
n
g
.
T
h
u
s
,
th
e
k
er
n
el
R
B
F
i
s
a
u
s
ef
u
l
m
eth
o
d
f
o
r
d
is
tin
g
u
is
h
in
g
b
e
twee
n
d
ata
g
r
o
u
p
s
th
at
h
av
e
s
h
ar
ed
co
m
p
licated
b
o
r
d
er
s
.
T
h
e
SVM
-
b
ased
d
ete
ctio
n
m
o
d
u
le
is
g
iv
en
tr
ain
in
g
u
s
in
g
t
h
e
s
tatis
tics
o
f
b
o
th
th
e
n
o
r
m
al
d
ata
an
d
th
e
s
u
s
p
icio
u
s
d
ata.
T
h
e
a
v
er
ag
e
o
f
o
n
e
-
s
ec
o
n
d
in
ter
v
als
is
u
s
ed
to
g
e
n
er
ate
th
e
s
tatis
tics
p
er
tain
in
g
t
o
ea
ch
ch
ar
ac
ter
is
tic.
T
h
e
d
ata
ar
e
th
en
n
o
r
m
alize
d
a
n
d
u
s
ed
as
in
p
u
t
in
to
th
e
s
y
s
tem
af
ter
b
ein
g
s
ep
ar
ated
in
to
a
tr
ain
in
g
s
eg
m
en
t
an
d
a
t
est
s
e
ctio
n
.
B
ec
au
s
e
th
e
d
etec
tio
n
m
o
d
u
le
is
tr
ain
ed
to
u
n
d
e
r
s
tan
d
th
e
b
eh
av
io
r
o
f
th
e
n
etwo
r
k
u
n
d
er
b
o
th
u
s
u
al
an
d
attac
k
s
ettin
g
s
,
th
e
d
etec
tio
n
m
o
d
u
le
lab
els
as
in
tr
u
s
io
n
a
n
y
lar
g
e
d
ev
iatio
n
s
f
r
o
m
th
e
ty
p
ical
b
e
h
av
io
r
o
f
t
h
e
n
etwo
r
k
.
Fo
r
th
is
AD
,
th
e
SV
M
class
if
ier
alg
o
r
ith
m
ef
f
icien
tly
d
is
tin
g
u
is
h
es
th
e
an
o
m
alies
.
Fig
u
r
e
1
.
SVM
alg
o
r
ith
m
-
b
ase
d
AD
3.
E
XP
E
R
I
M
E
N
T
A
L
RE
SUL
T
S
T
h
is
m
ec
h
an
is
m
ex
am
i
n
es
f
o
u
r
ty
p
es
o
f
attac
k
s
,
s
u
c
h
as
b
o
tn
ets
o
f
HT
T
P,
P2
P,
XSS,
a
n
d
p
h
is
h
in
g
cy
b
er
attac
k
s
,
wh
ich
ar
e
in
te
r
p
o
s
ed
i
n
to
th
e
n
o
r
m
al
ef
f
ec
ts
.
B
o
th
th
e
n
etwo
r
k
an
d
th
e
f
i
r
ewa
ll
lo
g
d
ata
ar
e
g
ath
er
ed
f
r
o
m
u
n
iv
er
s
ity
s
er
v
er
s
.
T
o
au
th
o
r
ize
th
e
f
u
n
ctio
n
o
f
in
co
r
p
o
r
atin
g
tr
af
f
ics
with
lo
g
s
f
o
r
id
e
n
tify
in
g
an
o
m
aly
,
we
ac
c
o
m
p
lis
h
co
m
p
ar
is
o
n
ex
p
e
r
im
en
tatio
n
s
b
y
l
ev
er
ag
in
g
th
e
tr
af
f
ic
d
ata.
T
h
e
n
etwo
r
k
tr
a
f
f
ic
n
o
r
lo
g
s
s
ep
ar
ately
attain
attr
ac
ti
v
e
r
esu
lts
in
id
en
tify
in
g
cy
b
er
attac
k
s
.
I
n
th
is
m
ec
h
an
is
m
,
we
an
aly
ze
th
e
class
if
icatio
n
p
er
f
o
r
m
an
ce
u
ti
lized
b
y
ca
lcu
latin
g
th
eir
p
r
e
cisi
o
n
,
ca
teg
o
r
izatio
n
ac
cu
r
ac
y
,
r
ec
all,
r
eq
u
ir
e
d
tim
e,
a
s
well
as
F
-
m
ea
s
u
r
e
.
T
h
e
a
cc
u
r
ac
y
(
AC
U
)
d
is
p
lay
s
wh
at
p
er
ce
n
tag
e
o
f
th
e
t
o
tal
n
u
m
b
er
o
f
r
i
g
h
t
f
o
r
ec
asti
n
g
s
h
av
e
b
ee
n
m
ad
e,
wh
ich
is
th
e
v
alu
e
o
f
ca
s
es
th
at
h
av
e
b
ee
n
co
r
r
ec
tl
y
ca
teg
o
r
ized
.
T
h
e
ac
cu
r
ac
y
o
f
th
e
class
if
icatio
n
m
ay
b
e
d
eter
m
in
e
d
b
y
u
s
in
g
(
1
)
an
d
d
i
v
id
in
g
th
e
to
tal
n
u
m
b
er
o
f
f
o
r
ec
asti
n
g
s
th
r
o
u
g
h
th
e
to
tal
n
u
m
b
er
o
f
f
o
r
ec
asts
.
=
+
+
+
+
(
1
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
5
2
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
38
,
No
.
3
,
J
u
n
e
20
25
:
1
6
4
2
-
1
6
5
1
1646
C
o
n
ce
r
n
in
g
t
h
e
co
n
f
u
s
io
n
m
a
tr
ix
co
n
tain
s
f
o
u
r
v
ar
iab
les:
t
r
u
e
p
o
s
itiv
e
(
T
P
os
)
,
tr
u
e
n
e
g
ativ
e
(
T
N
eg
)
,
f
alse
p
o
s
itiv
e
(
F
P
os
)
,
as
well
as
f
alse
n
eg
ativ
e
(
F
Neg
)
.
Fo
r
in
s
tan
ce
,
T
P
os
p
r
o
p
o
s
es
th
at
th
e
e
x
is
ten
ce
o
f
d
a
n
g
er
s
was
ex
ac
tly
d
is
tin
g
u
is
h
ed
.
R
ath
er
th
an
,
T
N
eg
r
ep
r
esen
ts
th
at
th
e
d
ef
icien
cy
o
f
th
r
e
ats
was
ac
cu
r
ately
f
o
r
ec
asted
,
th
at
is
a
p
o
s
itiv
e
ex
p
lo
itatio
n
.
T
h
e
in
f
o
r
m
atio
n
th
at
th
e
m
eth
o
d
wr
o
n
g
ly
f
o
r
ec
asted
th
at
th
er
e
was
n
o
d
an
g
er
F
Neg
an
d
f
ailed
to
d
is
tin
g
u
is
h
th
e
ex
is
ten
ce
o
f
th
e
attac
k
F
P
os
p
r
o
p
o
s
es
th
at
th
e
m
ec
h
an
is
m
d
id
n
o
t
s
u
f
f
icien
tly
n
o
tice
th
e
ex
is
tan
ce
o
f
th
e
attac
k
.
=
+
(
2
)
Ad
d
itio
n
ally
,
th
e
ac
cu
r
ac
y
,
R
ec
all
(
R
c
a
)
,
an
d
F
-
m
ea
s
u
r
e
(
F
-
M)
ar
e
all
d
er
iv
ed
u
s
in
g
th
ese
f
o
u
r
v
ar
iab
les.
W
h
en
(
2
)
is
u
s
ed
to
d
eter
m
in
e
p
r
ec
is
io
n
(
Pre
c
)
,
th
e
n
u
m
b
er
o
f
‘
An
o
m
alo
u
s
’
o
cc
u
r
r
en
ce
s
th
at
ar
e
r
ea
lly
p
o
s
itiv
e
is
q
u
an
tifie
d
as
a
p
er
ce
n
tag
e
o
f
th
e
to
tal
n
u
m
b
er
o
f
in
s
tan
ce
s
th
at
ar
e
p
r
o
jec
ted
to
b
e
‘
An
o
m
alo
u
s
’
.
=
+
(
3
)
R
eg
ar
d
in
g
th
e
r
ec
all,
it
lik
ew
is
e
f
o
r
ec
asts
th
e
p
o
s
itiv
e
clas
s
f
o
r
ec
asti
n
g
,
b
u
t,
as
s
h
o
wn
i
n
(
3
)
,
it
is
co
m
p
u
ted
o
v
er
th
e
wh
o
le
a
n
o
m
alo
u
s
co
u
n
t
o
cc
u
r
r
e
n
ce
s
in
clu
d
ed
in
s
id
e
th
e
d
ataset
its
elf
,
r
eg
ar
d
less
o
f
wh
eth
er
o
r
n
o
t
s
u
ch
in
s
tan
ce
s
wer
e
s
u
cc
ess
f
u
lly
f
o
r
ec
asted
.
T
o
co
n
clu
d
e,
th
e
F
-
m
ea
s
u
r
e
is
a
s
in
g
le
co
u
n
t
th
at
ag
g
r
eg
ates
th
e
v
alu
es
o
f
th
e
p
r
ec
is
io
n
as
well
as
r
ec
all
in
to
o
n
e
n
u
m
b
e
r
.
Fig
u
r
e
s
2
to
4
d
is
p
lay
s
th
e
p
r
ec
is
io
n
,
r
ec
all
an
d
F
-
m
ea
s
u
r
e
(F
-
M)
o
f
SVMA
an
d
HADA
m
ec
h
an
is
m
s
.
I
t is co
m
p
u
ted
b
y
a
p
p
ly
in
g
f
o
r
m
u
la
(
4
)
.
−
=
×
+
(
4
)
Fig
u
r
e
2
.
Pre
cisi
o
n
r
ati
o
o
f
SV
MA
an
d
HADA
Fig
u
r
e
3
.
R
ec
all
r
atio
o
f
SVMA
an
d
HADA
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:
2502
-
4
7
5
2
S
V
M a
lg
o
r
ith
m
-
b
a
s
ed
a
n
o
m
a
ly
d
etec
tio
n
in
n
etw
o
r
k
lo
g
s
a
n
d
fir
ewa
ll lo
g
s
(
Jo
h
n
B
en
ito
J
esu
d
a
s
a
n
P
eter
)
1647
Fig
u
r
e
4
.
F
-
Me
asu
r
e
o
f
SVMA
an
d
HADA
Fro
m
th
ese
f
ig
u
r
es,
th
e
p
r
o
p
o
s
ed
SVMA
m
ec
h
an
is
m
h
as
a
0
.
9
9
1
5
an
d
HADA
m
ec
h
an
is
m
h
as
a
0
.
9
3
4
2
p
r
ec
is
io
n
r
atio
.
T
h
e
r
atio
o
f
r
ec
all
v
alu
e
f
o
r
SVMA
m
ec
h
an
is
m
p
r
esen
ts
0
.
9
9
6
,
an
d
HADA
m
ec
h
an
is
m
h
as
a
0
.
9
2
7
.
Fu
r
t
h
er
m
o
r
e
,
t
h
e
th
e
p
r
o
p
o
s
ed
S
VM
A
m
ec
h
an
is
m
p
r
esen
ts
th
e
r
atio
v
alu
e
o
f
F
-
m
ea
s
u
r
e
is
0
.
9
3
3
an
d
th
e
HADA
m
ec
h
an
is
m
p
r
esen
ts
t
h
e
F
-
m
ea
s
u
r
e
r
atio
v
alu
e
is
0
.
9
4
.
T
h
e
SVMA
m
ec
h
an
is
m
co
m
p
ar
e
d
to
th
e
HADA
m
ec
h
an
is
m
s
,
th
e
S
VM
A
m
ec
h
an
is
m
p
r
o
v
id
e
b
etter
r
esu
lts
in
th
e
n
etwo
r
k
.
A
s
th
e
f
ig
u
r
es
ex
p
o
s
e,
th
e
SVMA
r
ea
ch
ed
th
e
g
r
ea
test
o
p
er
atio
n
ab
o
v
e
0
.
9
9
th
an
th
e
ex
is
tin
g
HADA
m
ec
h
an
is
m
.
Fig
u
r
e
5
ex
p
lain
s
th
e
p
r
o
p
o
s
ed
SVM
A
m
ec
h
an
is
m
p
r
ec
is
io
n
p
er
c
en
tag
e
o
f
s
elec
ted
f
ea
tu
r
es with
wh
o
le
f
ea
tu
r
es.
Fro
m
F
ig
u
r
e
5
,
t
h
e
wh
o
le
f
ea
t
u
r
e
p
r
ec
is
io
n
r
ate
is
lo
w
lev
el
,
b
u
t
th
e
s
elec
ted
f
ea
tu
r
es
p
r
e
cisi
o
n
r
ate
is
h
ig
h
d
u
e
to
th
e
SVMA
m
ec
h
an
is
m
u
s
in
g
t
h
e
GA
alg
o
r
ith
m
to
s
elec
t
th
e
f
ea
tu
r
e
as
well.
T
h
e
s
elec
ted
f
ea
tu
r
es
p
r
ec
is
io
n
p
er
ce
n
tag
e
is
9
8
.
5
an
d
th
e
wh
o
le
f
ea
t
u
r
es
p
r
ec
is
i
o
n
p
er
ce
n
ta
g
e
is
9
3
.
5
.
T
h
e
wh
o
le
f
ea
tu
es
cr
ea
tes
ad
d
itio
n
al
d
elay
an
d
o
v
er
h
ea
d
.
Fig
u
r
e
6
ex
p
lai
n
s
th
e
R
eq
u
ir
ed
tim
e
f
o
r
s
elec
ted
f
ea
tu
r
es
with
wh
o
le
f
ea
tu
r
es
.
Fig
u
r
e
5
.
Pre
cisi
o
n
p
er
ce
n
tag
e
o
f
s
elec
ted
f
ea
tu
r
es with
wh
o
le
f
ea
tu
r
es
Fig
u
r
e
6
.
R
eq
u
i
r
e
d
tim
e
f
o
r
s
e
lecte
d
f
ea
tu
r
es with
wh
o
le
f
ea
t
u
r
es
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
5
2
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
38
,
No
.
3
,
J
u
n
e
20
25
:
1
6
4
2
-
1
6
5
1
1648
Fro
m
Fig
u
r
e
6
,
th
e
s
elec
ted
f
ea
tu
r
es
tak
e
a
less
er
r
e
q
u
ir
ed
tim
e
wh
ic
h
r
ep
r
esen
t
s
th
e
b
est
p
er
f
o
r
m
an
ce
ac
c
u
r
ac
y
.
H
o
wev
er
,
th
e
wh
o
le
f
ea
tu
r
e
tak
es
m
o
r
e
r
e
q
u
ir
ed
tim
e
s
in
ce
its
elf
h
av
e
n
o
is
e
an
d
u
n
wan
ted
an
d
r
e
p
ea
ted
d
ata.
T
h
e
SVMA
m
ec
h
a
n
is
m
ap
p
lies
a
GA
to
p
ick
o
u
t
th
e
b
ette
r
f
ea
tu
r
es.
Fig
u
r
e
7
d
is
p
lay
s
an
AD
ac
cu
r
ac
y
o
f
S
VM
A
an
d
HADA
m
ec
h
an
is
m
s
b
ased
o
n
E
x
p
er
im
en
ts
.
Fig
u
r
e
7
clea
r
l
y
s
ay
s
th
at
th
e
p
r
o
p
o
s
ed
SVMA
m
ec
h
an
is
m
r
aises
th
e
AD
ac
cu
r
ac
y
th
a
n
t
h
e
HADA
m
ec
h
an
is
m
.
T
h
e
SVMA
m
ec
h
an
is
m
u
tili
ze
s
th
e
S
VM
alg
o
r
ith
m
to
s
ep
ar
ate
th
e
an
o
m
a
ly
in
n
etwo
r
k
an
d
f
ir
ewa
ll
lo
g
s
ag
ain
s
t
cy
b
er
att
ac
k
s
ef
f
icien
tly
.
Fu
r
th
e
r
m
o
r
e,
th
e
SVMA
m
ec
h
an
is
m
ap
p
li
es
th
e
GA
to
s
elec
t
th
e
b
est f
ea
tu
r
es; h
en
ce
,
it r
aises
th
e
AD
ac
cu
r
ac
y
.
Fig
u
r
e
7
.
AD
a
cc
u
r
ac
y
b
ased
o
n
n
u
m
b
er
o
f
ex
p
er
im
en
ts
4.
CO
NCLU
SI
O
N
Sp
ec
if
ically
,
a
Fire
wall
i
s
a
s
ig
n
if
ican
t
co
m
p
o
n
en
t
o
f
n
etw
o
r
k
s
ec
u
r
ity
,
an
d
it
ca
n
d
ef
en
d
th
e
d
ata
f
r
o
m
ex
te
r
n
al
an
d
in
ter
n
al
at
tack
s
.
T
h
is
ar
ticle
p
r
esen
ts
Su
p
p
o
r
t
Vec
to
r
Ma
ch
in
e
alg
o
r
ith
m
-
b
ased
AD
in
Netwo
r
k
L
o
g
s
an
d
Fire
wall
lo
g
s
.
I
t
in
tr
o
d
u
ce
d
to
i
n
co
r
p
o
r
ate
tr
af
f
ic
with
n
etwo
r
k
lo
g
s
f
o
r
n
o
ticin
g
cy
b
er
attac
k
s
.
T
h
e
p
r
ep
r
o
ce
s
s
in
g
co
n
ce
p
t
to
f
ilter
t
h
e
u
n
wa
n
ted
d
ata
f
r
o
m
th
e
in
p
u
t.
T
h
i
s
m
ec
h
an
is
m
u
s
es
a
GA
alg
o
r
ith
m
f
o
r
s
elec
tin
g
th
e
f
ea
tu
r
e
well.
GA
alg
o
r
it
h
m
tak
es
m
in
im
u
m
tim
e
f
o
r
f
ea
tu
r
e
s
elec
tio
n
,
en
h
an
cin
g
th
e
f
ea
tu
r
e
s
elec
tio
n
p
r
ec
is
io
n
.
T
h
e
SVM
alg
o
r
it
h
m
g
iv
es
t
h
e
in
p
u
t
o
f
th
e
d
ata
;
th
en
,
it
ca
te
g
o
r
izes
th
e
in
tr
u
s
io
n
ef
f
icien
tl
y
.
T
h
e
ex
p
er
im
en
tal
r
esu
lts
p
r
o
v
e
th
a
t
th
e
p
r
o
p
o
s
ed
SVMA
m
ec
h
a
n
is
m
ca
n
e
f
f
icien
tl
y
d
etec
t
an
o
m
aly
an
d
in
cr
ea
s
es
th
e
p
r
ec
is
io
n
,
ac
cu
r
ac
y
,
an
d
r
ec
all
r
atio
.
T
h
e
SVMA
m
ec
h
an
is
m
p
r
esen
ts
9
9
%
ef
f
icien
cy
th
a
n
a
co
n
v
en
tio
n
a
l
m
ec
h
an
is
m
.
T
h
is
m
ec
h
an
is
m
u
tili
ze
s
in
ar
m
y
a
n
d
f
i
r
e
d
e
tectio
n
ap
p
licatio
n
s
.
I
n
f
u
tu
r
e
,
we
ap
p
ly
in
g
ar
tifi
cial
in
tellig
en
ce
an
d
ML
alg
o
r
ith
m
to
im
p
r
o
v
e
th
e
s
ec
u
r
i
t
y
an
d
ev
ad
es
th
e
n
etwo
r
k
tr
af
f
ic
in
th
e
n
etwo
r
k
.
F
UNDING
I
NF
O
R
M
A
T
I
O
N
Fu
n
d
in
g
in
f
o
r
m
ati
o
n
is
n
o
t a
v
ailab
le.
AUTHO
R
CO
NT
RI
B
UT
I
O
NS ST
A
T
E
M
E
N
T
T
h
is
jo
u
r
n
al
u
s
es
th
e
C
o
n
tr
ib
u
to
r
R
o
les
T
ax
o
n
o
m
y
(
C
R
ed
iT)
to
r
ec
o
g
n
ize
in
d
iv
id
u
al
au
th
o
r
co
n
tr
ib
u
tio
n
s
,
r
ed
u
ce
a
u
th
o
r
s
h
ip
d
is
p
u
tes,
an
d
f
ac
ilit
ate
co
llab
o
r
atio
n
Na
m
e
o
f
Aut
ho
r
C
M
So
Va
Fo
I
R
D
O
E
Vi
Su
P
Fu
J
o
h
n
B
en
ito
J
esu
d
asan
Peter
✓
✓
✓
✓
✓
✓
✓
✓
✓
Nitin
R
ak
esh
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
Pu
ttas
wam
y
R
ek
h
a
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
T
am
m
in
en
i
Sre
elath
a
✓
✓
✓
✓
✓
✓
✓
✓
✓
Velu
s
am
y
Su
jath
a
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
Su
r
u
liv
elu
Mu
th
u
m
ar
ilak
s
h
m
i
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
Sh
an
m
u
g
am
Su
jath
a
✓
✓
✓
✓
✓
✓
✓
✓
✓
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:
2502
-
4
7
5
2
S
V
M a
lg
o
r
ith
m
-
b
a
s
ed
a
n
o
m
a
ly
d
etec
tio
n
in
n
etw
o
r
k
lo
g
s
a
n
d
fir
ewa
ll lo
g
s
(
Jo
h
n
B
en
ito
J
esu
d
a
s
a
n
P
eter
)
1649
C
:
C
o
n
c
e
p
t
u
a
l
i
z
a
t
i
o
n
M
:
M
e
t
h
o
d
o
l
o
g
y
So
:
So
f
t
w
a
r
e
Va
:
Va
l
i
d
a
t
i
o
n
Fo
:
Fo
r
mal
a
n
a
l
y
s
i
s
I
:
I
n
v
e
s
t
i
g
a
t
i
o
n
R
:
R
e
so
u
r
c
e
s
D
:
D
a
t
a
C
u
r
a
t
i
o
n
O
:
W
r
i
t
i
n
g
-
O
r
i
g
i
n
a
l
D
r
a
f
t
E
:
W
r
i
t
i
n
g
-
R
e
v
i
e
w
&
E
d
i
t
i
n
g
Vi
:
Vi
su
a
l
i
z
a
t
i
o
n
Su
:
Su
p
e
r
v
i
s
i
o
n
P
:
P
r
o
j
e
c
t
a
d
mi
n
i
st
r
a
t
i
o
n
Fu
:
Fu
n
d
i
n
g
a
c
q
u
i
si
t
i
o
n
CO
NF
L
I
C
T
O
F
I
N
T
E
R
E
S
T
ST
A
T
E
M
E
NT
T
h
e
au
th
o
r
s
h
av
e
n
o
co
n
f
lict o
f
in
ter
est r
elev
an
t to
t
h
is
p
ap
er
.
DATA AV
AI
L
AB
I
L
I
T
Y
T
h
e
d
ata
th
at
s
u
p
p
o
r
t
th
e
f
in
d
in
g
s
o
f
th
is
s
tu
d
y
ar
e
av
aila
b
le
o
n
r
eq
u
est
f
r
o
m
th
e
co
r
r
esp
o
n
d
in
g
au
th
o
r
,
[
N.
R
]
.
T
h
e
d
ata,
wh
ich
co
n
tain
i
n
f
o
r
m
atio
n
th
at
co
u
ld
c
o
m
p
r
o
m
is
e
th
e
p
r
iv
ac
y
o
f
r
esear
ch
p
ar
ticip
an
ts
,
ar
e
n
o
t p
u
b
licly
a
v
ailab
le
d
u
e
to
ce
r
tain
r
estrictio
n
s
.
RE
F
E
R
E
NC
E
S
[
1
]
J.
C
.
Li
u
,
C
.
T.
Y
a
n
g
,
Y
.
W
.
C
h
a
n
,
E
.
K
r
i
st
i
a
n
i
,
a
n
d
W
.
J
.
Ji
a
n
g
,
“
C
y
b
e
r
a
t
t
a
c
k
d
e
t
e
c
t
i
o
n
mo
d
e
l
u
si
n
g
d
e
e
p
l
e
a
r
n
i
n
g
i
n
a
n
e
t
w
o
r
k
l
o
g
s
y
s
t
e
m
w
i
t
h
d
a
t
a
v
i
su
a
l
i
z
a
t
i
o
n
,
”
J
o
u
r
n
a
l
o
f
S
u
p
e
r
c
o
m
p
u
t
i
n
g
,
v
o
l
.
7
7
,
n
o
.
1
0
,
p
p
.
1
0
9
8
4
–
1
1
0
0
3
,
2
0
2
1
,
d
o
i
:
1
0
.
1
0
0
7
/
s
1
1
2
2
7
-
021
-
0
3
7
1
5
-
6.
[
2
]
K
.
M
.
S
u
d
a
r
,
P
.
D
e
e
p
a
l
a
k
sh
mi
,
P
.
N
a
g
a
r
a
j
,
a
n
d
V
.
M
u
n
e
e
sw
a
r
a
n
,
“
A
n
a
l
y
si
s
o
f
c
y
b
e
r
a
t
t
a
c
k
s
a
n
d
i
t
s
d
e
t
e
c
t
i
o
n
me
c
h
a
n
i
sms
,
”
Pro
c
e
e
d
i
n
g
s
-
2
0
2
0
5
t
h
I
n
t
e
r
n
a
t
i
o
n
a
l
C
o
n
f
e
r
e
n
c
e
o
n
R
e
se
a
rc
h
i
n
C
o
m
p
u
t
a
t
i
o
n
a
l
I
n
t
e
l
l
i
g
e
n
c
e
a
n
d
C
o
m
m
u
n
i
c
a
t
i
o
n
N
e
t
w
o
r
k
s,
I
C
RC
I
C
N
2
0
2
0
,
p
p
.
1
2
–
1
6
,
2
0
2
0
,
d
o
i
:
1
0
.
1
1
0
9
/
I
C
R
C
I
C
N
5
0
9
3
3
.
2
0
2
0
.
9
2
9
6
1
7
8
.
[
3
]
M
.
K
a
r
a
n
f
i
l
e
t
a
l
.
,
“
D
e
t
e
c
t
i
o
n
o
f
m
i
c
r
o
g
r
i
d
c
y
b
e
r
a
t
t
a
c
k
s
u
s
i
n
g
n
e
t
w
o
r
k
a
n
d
s
y
st
e
m
m
a
n
a
g
e
me
n
t
,
”
I
EEE
T
ra
n
s
a
c
t
i
o
n
s
o
n
S
m
a
r
t
G
ri
d
,
v
o
l
.
1
4
,
n
o
.
3
,
p
p
.
2
3
9
0
–
2
4
0
5
,
2
0
2
3
,
d
o
i
:
1
0
.
1
1
0
9
/
TSG
.
2
0
2
2
.
3
2
1
8
9
3
4
.
[
4
]
L.
A
y
a
l
a
,
“
D
e
t
e
c
t
i
o
n
o
f
C
y
b
e
r
-
A
t
t
a
c
k
s,”
i
n
C
y
b
e
r
sec
u
r
i
t
y
f
o
r
H
o
s
p
i
t
a
l
s
a
n
d
H
e
a
l
t
h
c
a
r
e
F
a
c
i
l
i
t
i
e
s
,
B
e
r
k
e
l
e
y
,
C
A
:
A
p
r
e
ss,
2
0
1
6
,
p
p
.
5
3
–
6
0
.
[
5
]
S
.
S
h
i
r
a
l
i
-
S
h
a
h
r
e
z
a
a
n
d
Y
.
G
a
n
j
a
l
i
,
“
P
r
o
t
e
c
t
i
n
g
h
o
m
e
u
s
e
r
d
e
v
i
c
e
s
w
i
t
h
a
n
S
D
N
-
b
a
se
d
f
i
r
e
w
a
l
l
,
”
I
EEE
T
r
a
n
sa
c
t
i
o
n
s
o
n
C
o
n
s
u
m
e
r
El
e
c
t
r
o
n
i
c
s
,
v
o
l
.
6
4
,
n
o
.
1
,
p
p
.
9
2
–
1
0
0
,
F
e
b
.
2
0
1
8
,
d
o
i
:
1
0
.
1
1
0
9
/
T
C
E.
2
0
1
8
.
2
8
1
1
2
6
1
.
[
6
]
A
.
A
.
A
l
i
,
S
.
M
.
D
a
r
w
i
sh
,
a
n
d
S
.
K
.
G
u
i
r
g
u
i
s,
“
A
n
a
p
p
r
o
a
c
h
f
o
r
i
m
p
r
o
v
i
n
g
p
e
r
f
o
r
ma
n
c
e
o
f
a
p
a
c
k
e
t
f
i
l
t
e
r
i
n
g
f
i
r
e
w
a
l
l
b
a
s
e
d
o
n
f
u
z
z
y
p
e
t
r
i
n
e
t
,
”
J
o
u
r
n
a
l
o
f
Ad
v
a
n
c
e
s
i
n
C
o
m
p
u
t
e
r
N
e
t
w
o
rks
,
v
o
l
.
3
,
n
o
.
1
,
p
p
.
6
7
–
7
4
,
2
0
1
5
,
d
o
i
:
1
0
.
7
7
6
3
/
JA
C
N
.
2
0
1
5
.
V
3
.
1
4
4
.
[
7
]
A
.
I
.
H
a
j
a
my
d
e
e
n
,
N
.
I
.
U
d
z
i
r
,
R
.
M
a
h
m
o
d
,
a
n
d
A
.
A
.
A
.
G
h
a
n
i
,
“
A
n
u
n
s
u
p
e
r
v
i
se
d
h
e
t
e
r
o
g
e
n
e
o
u
s
l
o
g
-
b
a
s
e
d
f
r
a
mew
o
r
k
f
o
r
a
n
o
ma
l
y
d
e
t
e
c
t
i
o
n
,
”
T
u
rk
i
s
h
J
o
u
rn
a
l
o
f
El
e
c
t
ri
c
a
l
E
n
g
i
n
e
e
ri
n
g
a
n
d
C
o
m
p
u
t
e
r
S
c
i
e
n
c
e
s
,
v
o
l
.
2
4
,
n
o
.
3
,
p
p
.
1
1
1
7
–
1
1
3
4
,
2
0
1
6
,
d
o
i
:
1
0
.
3
9
0
6
/
e
l
k
-
1
3
0
2
-
1
9
.
[
8
]
S
.
R
a
n
g
a
,
N
.
M
.
G
u
p
t
h
a
,
a
n
d
H
.
M
.
S
,
“
A
su
r
v
e
y
o
n
a
u
t
o
ma
t
i
c
a
b
n
o
r
m
a
l
i
t
i
e
s
mo
n
i
t
o
r
i
n
g
s
y
s
t
e
m
f
o
r
l
o
g
f
i
l
e
s
u
si
n
g
mac
h
i
n
e
l
e
a
r
n
i
n
g
,
”
T
u
r
k
i
s
h
O
n
l
i
n
e
J
o
u
r
n
a
l
o
f
Q
u
a
l
i
t
a
t
i
v
e
I
n
q
u
i
ry
(
T
O
J
Q
I
)
,
v
o
l
.
1
2
,
n
o
.
6
,
p
p
.
8
3
8
6
–
8
3
9
3
,
2
0
2
1
.
[
9
]
K
.
F
o
t
i
a
d
o
u
,
T.
-
H
.
V
e
l
i
v
a
ssa
k
i
,
A
.
V
o
u
l
k
i
d
i
s,
D
.
S
k
i
a
s,
S
.
Tse
k
e
r
i
d
o
u
,
a
n
d
T.
Z
a
h
a
r
i
a
d
i
s,
“
N
e
t
w
o
r
k
t
r
a
f
f
i
c
a
n
o
m
a
l
y
d
e
t
e
c
t
i
o
n
v
i
a
d
e
e
p
l
e
a
r
n
i
n
g
,
”
I
n
f
o
rm
a
t
i
o
n
,
v
o
l
.
1
2
,
n
o
.
5
,
p
.
2
1
5
,
M
a
y
2
0
2
1
,
d
o
i
:
1
0
.
3
3
9
0
/
i
n
f
o
1
2
0
5
0
2
1
5
.
[
1
0
]
M
.
Ly
u
,
H
.
H
.
G
h
a
r
a
k
h
e
i
l
i
,
C
.
R
u
ss
e
l
l
,
a
n
d
V
.
S
i
v
a
r
a
ma
n
,
“
H
i
e
r
a
r
c
h
i
c
a
l
a
n
o
m
a
l
y
-
b
a
se
d
d
e
t
e
c
t
i
o
n
o
f
d
i
st
r
i
b
u
t
e
d
D
N
S
a
t
t
a
c
k
s
o
n
e
n
t
e
r
p
r
i
se
n
e
t
w
o
r
k
s,
”
I
E
EE
T
r
a
n
sa
c
t
i
o
n
s
o
n
N
e
t
w
o
r
k
a
n
d
S
e
r
v
i
c
e
M
a
n
a
g
e
m
e
n
t
,
v
o
l
.
1
8
,
n
o
.
1
,
p
p
.
1
0
3
1
–
1
0
4
8
,
M
a
r
.
2
0
2
1
,
d
o
i
:
1
0
.
1
1
0
9
/
TN
S
M
.
2
0
2
1
.
3
0
5
0
0
9
1
.
[
1
1
]
Q
.
M
a
,
C
.
S
u
n
,
a
n
d
B
.
C
u
i
,
“
A
n
o
v
e
l
mo
d
e
l
f
o
r
a
n
o
ma
l
y
d
e
t
e
c
t
i
o
n
i
n
n
e
t
w
o
r
k
t
r
a
f
f
i
c
b
a
s
e
d
o
n
su
p
p
o
r
t
v
e
c
t
o
r
m
a
c
h
i
n
e
a
n
d
c
l
u
st
e
r
i
n
g
,
”
S
e
c
u
r
i
t
y
a
n
d
C
o
m
m
u
n
i
c
a
t
i
o
n
N
e
t
w
o
rks
,
v
o
l
.
2
0
2
1
,
p
p
.
1
–
1
1
,
N
o
v
.
2
0
2
1
,
d
o
i
:
1
0
.
1
1
5
5
/
2
0
2
1
/
2
1
7
0
7
8
8
.
[
1
2
]
A
.
F
.
G
u
l
l
i
v
e
r
a
n
d
T.
A
a
r
o
n
,
“
U
n
s
u
p
e
r
v
i
se
d
l
o
g
mess
a
g
e
a
n
o
ma
l
y
d
e
t
e
c
t
i
o
n
,
”
I
C
T
Ex
p
re
ss
,
v
o
l
.
6
,
n
o
.
3
,
p
p
.
2
2
9
–
2
3
7
,
2
0
2
0
.
[
1
3
]
J.
O
.
O
n
a
h
,
S
.
M
.
A
b
d
u
l
h
a
m
i
d
,
M
.
A
b
d
u
l
l
a
h
i
,
I
.
H
.
H
a
ss
a
n
,
a
n
d
A
.
A
l
-
G
h
u
sh
a
m,
“
G
e
n
e
t
i
c
A
l
g
o
r
i
t
h
m
b
a
s
e
d
f
e
a
t
u
r
e
sel
e
c
t
i
o
n
a
n
d
N
a
ï
v
e
B
a
y
e
s
f
o
r
a
n
o
ma
l
y
d
e
t
e
c
t
i
o
n
i
n
f
o
g
c
o
mp
u
t
i
n
g
e
n
v
i
r
o
n
m
e
n
t
,
”
Ma
c
h
i
n
e
L
e
a
rn
i
n
g
w
i
t
h
A
p
p
l
i
c
a
t
i
o
n
s
,
v
o
l
.
6
,
p
.
1
0
0
1
5
6
,
D
e
c
.
2
0
2
1
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
ml
w
a
.
2
0
2
1
.
1
0
0
1
5
6
.
[
1
4
]
T.
-
B.
-
T
.
N
g
u
y
e
n
,
T.
-
L.
Li
a
o
,
a
n
d
T.
-
A
.
V
u
,
“
A
n
o
m
a
l
y
d
e
t
e
c
t
i
o
n
u
si
n
g
o
n
e
-
c
l
a
s
s
S
V
M
f
o
r
l
o
g
s
o
f
j
u
n
i
p
e
r
r
o
u
t
e
r
d
e
v
i
c
e
s
,
”
i
n
L
e
c
t
u
re
N
o
t
e
s
o
f
t
h
e
I
n
st
i
t
u
t
e
f
o
r C
o
m
p
u
t
e
r
S
c
i
e
n
c
e
s,
S
o
c
i
a
l
-
I
n
f
o
rm
a
t
i
c
s a
n
d
T
e
l
e
c
o
m
m
u
n
i
c
a
t
i
o
n
s E
n
g
i
n
e
e
r
i
n
g
,
L
N
I
C
S
T
,
v
o
l
.
2
9
3
,
2
0
1
9
,
p
p
.
3
0
2
–
3
1
2
.
[
1
5
]
A
.
A
.
T
a
h
i
r
a
n
d
S
.
A
n
g
h
e
l
u
s,
“
I
mp
r
o
v
i
n
g
i
r
i
s
r
e
c
o
g
n
i
t
i
o
n
a
c
c
u
r
a
c
y
u
s
i
n
g
g
a
b
o
r
k
e
r
n
e
l
s
w
i
t
h
n
e
a
r
-
h
o
r
i
z
o
n
t
a
l
o
r
i
e
n
t
a
t
i
o
n
s,
”
I
n
t
e
r
n
a
t
i
o
n
a
l
J
o
u
r
n
a
l
o
f
A
d
v
a
n
c
e
s
i
n
S
i
g
n
a
l
a
n
d
I
m
a
g
e
S
c
i
e
n
c
e
s
,
v
o
l
.
8
,
n
o
.
1
,
p
p
.
2
5
–
3
9
,
2
0
2
2
,
d
o
i
:
1
0
.
2
9
2
8
4
/
i
j
a
si
s.
8
.
1
.
2
0
2
2
.
2
5
-
3
9
.
[
1
6
]
S
.
H
a
n
,
Q
.
W
u
,
a
n
d
Y
.
Y
a
n
g
,
“
M
a
c
h
i
n
e
l
e
a
r
n
i
n
g
f
o
r
I
n
t
e
r
n
e
t
o
f
t
h
i
n
g
s
a
n
o
m
a
l
y
d
e
t
e
c
t
i
o
n
u
n
d
e
r
l
o
w
-
q
u
a
l
i
t
y
d
a
t
a
,
”
I
n
t
e
r
n
a
t
i
o
n
a
l
J
o
u
r
n
a
l
o
f
D
i
s
t
r
i
b
u
t
e
d
S
e
n
s
o
r
N
e
t
w
o
r
k
s
,
v
o
l
.
1
8
,
n
o
.
1
0
,
p
.
1
5
5
0
1
3
2
9
2
2
1
1
3
3
7
,
O
c
t
.
2
0
2
2
,
d
o
i
:
1
0
.
1
1
7
7
/
1
5
5
0
1
3
2
9
2
2
1
1
3
3
7
6
5
.
[
1
7
]
P
.
A
.
M
,
C
.
M
.
R
e
d
d
y
,
A
.
A
n
b
a
r
a
si
,
N
.
M
o
h
a
n
k
u
mar,
I
.
M
.
V
,
a
n
d
S
.
M
u
r
u
g
a
n
,
“
C
l
o
u
d
-
B
a
se
d
r
o
a
d
s
a
f
e
t
y
f
o
r
r
e
a
l
-
t
i
m
e
v
e
h
i
c
l
e
r
a
sh
d
r
i
v
i
n
g
a
l
e
r
t
s
w
i
t
h
r
a
n
d
o
m
f
o
r
e
st
a
l
g
o
r
i
t
h
m
,
”
i
n
2
0
2
4
3
rd
I
n
t
e
r
n
a
t
i
o
n
a
l
C
o
n
f
e
r
e
n
c
e
f
o
r
I
n
n
o
v
a
t
i
o
n
i
n
T
e
c
h
n
o
l
o
g
y
(
I
N
O
C
O
N
)
,
M
a
r
.
2
0
2
4
,
p
p
.
1
–
6
,
d
o
i
:
1
0
.
1
1
0
9
/
I
N
O
C
O
N
6
0
7
5
4
.
2
0
2
4
.
1
0
5
1
1
3
1
6
.
[
1
8
]
S
.
H
o
mm
e
s
,
R
.
S
t
a
t
e
,
a
n
d
T.
E
n
g
e
l
,
“
A
d
i
st
a
n
c
e
-
b
a
s
e
d
me
t
h
o
d
t
o
d
e
t
e
c
t
a
n
o
ma
l
o
u
s
a
t
t
r
i
b
u
t
e
s
i
n
l
o
g
f
i
l
e
s
,
”
i
n
2
0
1
2
I
EE
E
N
e
t
w
o
r
k
O
p
e
r
a
t
i
o
n
s
a
n
d
M
a
n
a
g
e
m
e
n
t
S
y
m
p
o
s
i
u
m
,
A
p
r
.
2
0
1
2
,
p
p
.
4
9
8
–
5
0
1
,
d
o
i
:
1
0
.
1
1
0
9
/
N
O
M
S
.
2
0
1
2
.
6
2
1
1
9
4
0
.
[
1
9
]
B
.
J
.
G
a
n
e
s
h
,
P
.
V
i
j
a
y
a
n
,
V
.
V
a
i
d
e
h
i
,
S
.
M
u
r
u
g
a
n
,
R
.
M
e
e
n
a
k
s
h
i
,
a
n
d
M
.
R
a
j
m
o
h
a
n
,
“
S
V
M
-
b
a
s
e
d
p
r
e
d
i
c
t
i
v
e
m
o
d
e
l
i
n
g
o
f
d
r
o
w
s
i
n
e
ss
i
n
h
o
sp
i
t
a
l
st
a
f
f
f
o
r
o
c
c
u
p
a
t
i
o
n
a
l
saf
e
t
y
so
l
u
t
i
o
n
v
i
a
I
o
T
i
n
f
r
a
st
r
u
c
t
u
r
e
,
”
i
n
2
0
2
4
2
n
d
I
n
t
e
r
n
a
t
i
o
n
a
l
C
o
n
f
e
re
n
c
e
o
n
C
o
m
p
u
t
e
r
,
C
o
m
m
u
n
i
c
a
t
i
o
n
a
n
d
C
o
n
t
r
o
l
(
I
C
4
)
,
F
e
b
.
2
0
2
4
,
p
p
.
1
–
5
,
d
o
i
:
1
0
.
1
1
0
9
/
I
C
4
5
7
4
3
4
.
2
0
2
4
.
1
0
4
8
6
4
2
9
.
[
2
0
]
B
.
M
e
e
n
a
k
s
h
i
,
A
.
V
a
n
a
t
h
i
,
B
.
G
o
p
i
,
S
.
S
a
n
g
e
e
t
h
a
,
L.
R
a
ma
l
i
n
g
a
m
,
a
n
d
S
.
M
u
r
u
g
a
n
,
“
W
i
r
e
l
e
ss
s
e
n
s
o
r
n
e
t
w
o
r
k
s
f
o
r
d
i
sas
t
e
r
man
a
g
e
me
n
t
a
n
d
e
mer
g
e
n
c
y
r
e
s
p
o
n
s
e
u
si
n
g
S
V
M
c
l
a
ssi
f
i
e
r
,
”
i
n
2
0
2
3
S
e
c
o
n
d
I
n
t
e
r
n
a
t
i
o
n
a
l
C
o
n
f
e
r
e
n
c
e
O
n
S
m
a
rt
T
e
c
h
n
o
l
o
g
i
e
s
Fo
r
S
m
a
rt
N
a
t
i
o
n
(
S
m
a
r
t
T
e
c
h
C
o
n
)
,
A
u
g
.
2
0
2
3
,
p
p
.
6
4
7
–
6
5
1
,
d
o
i
:
1
0
.
1
1
0
9
/
S
martT
e
c
h
C
o
n
5
7
5
2
6
.
2
0
2
3
.
1
0
3
9
1
4
3
5
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
5
2
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
38
,
No
.
3
,
J
u
n
e
20
25
:
1
6
4
2
-
1
6
5
1
1650
[
2
1
]
S
.
S
r
i
n
i
v
a
s
a
n
,
P
.
V
e
d
a
,
P
.
A
s
h
a
,
C
.
S
r
i
n
i
v
a
sa
n
,
S
.
M
u
r
u
g
a
n
,
a
n
d
S
.
S
u
j
a
t
h
a
,
“
S
V
M
C
l
a
s
si
f
i
e
r
i
n
I
o
T
-
C
o
n
n
e
c
t
e
d
D
o
o
r
w
a
y
T
h
e
r
ma
l
S
c
a
n
n
i
n
g
f
o
r
P
r
e
v
e
n
t
i
v
e
H
e
a
l
t
h
C
h
e
c
k
S
u
r
v
e
i
l
l
a
n
c
e
,
”
i
n
2
0
2
4
1
s
t
I
n
t
e
rn
a
t
i
o
n
a
l
C
o
n
f
e
re
n
c
e
o
n
I
n
n
o
v
a
t
i
v
e
S
u
st
a
i
n
a
b
l
e
T
e
c
h
n
o
l
o
g
i
e
s
f
o
r
E
n
e
rg
y
,
M
e
c
h
a
t
r
o
n
i
c
s,
a
n
d
S
m
a
rt
S
y
st
e
m
s
(
I
S
T
E
MS
)
,
A
p
r
.
2
0
2
4
,
p
p
.
1
–
6
,
d
o
i
:
1
0
.
1
1
0
9
/
I
S
TEM
S
6
0
1
8
1
.
2
0
2
4
.
1
0
5
6
0
2
3
1
.
[
2
2
]
P
.
M
a
h
e
sw
a
r
i
,
S
.
G
o
w
r
i
sw
a
r
i
,
S
.
B
a
l
a
su
b
r
a
m
a
n
i
,
A
.
R
.
B
a
b
u
,
J.
N
K
,
a
n
d
S
.
M
u
r
u
g
a
n
,
“
I
n
t
e
l
l
i
g
e
n
t
h
e
a
d
l
i
g
h
t
s
f
o
r
a
d
a
p
t
i
n
g
b
e
a
m
p
a
t
t
e
r
n
s
w
i
t
h
r
a
s
p
b
e
r
r
y
p
i
a
n
d
c
o
n
v
o
l
u
t
i
o
n
a
l
n
e
u
r
a
l
n
e
t
w
o
r
k
s,
”
i
n
2
0
2
4
2
n
d
I
n
t
e
r
n
a
t
i
o
n
a
l
C
o
n
f
e
r
e
n
c
e
o
n
D
e
v
i
c
e
I
n
t
e
l
l
i
g
e
n
c
e
,
C
o
m
p
u
t
i
n
g
a
n
d
C
o
m
m
u
n
i
c
a
t
i
o
n
T
e
c
h
n
o
l
o
g
i
e
s
(
D
I
C
C
T
)
,
M
a
r
.
2
0
2
4
,
p
p
.
1
8
2
–
1
8
7
,
d
o
i
:
1
0
.
1
1
0
9
/
D
I
C
C
T
6
1
0
3
8
.
2
0
2
4
.
1
0
5
3
3
1
5
9
.
[
2
3
]
M
.
A
k
p
i
n
a
r
,
M
.
F
.
A
d
a
k
,
a
n
d
G
.
G
u
v
e
n
c
,
“
S
V
M
-
b
a
se
d
a
n
o
m
a
l
y
d
e
t
e
c
t
i
o
n
i
n
r
e
m
o
t
e
w
o
r
k
i
n
g
:
I
n
t
e
l
l
i
g
e
n
t
s
o
f
t
w
a
r
e
S
mart
R
a
d
a
r
,
”
Ap
p
l
i
e
d
S
o
f
t
C
o
m
p
u
t
i
n
g
,
v
o
l
.
1
0
9
,
p
.
1
0
7
4
5
7
,
S
e
p
.
2
0
2
1
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
a
s
o
c
.
2
0
2
1
.
1
0
7
4
5
7
.
[
2
4
]
Z.
L
i
,
J.
S
h
i
,
a
n
d
M
.
V
a
n
L
e
e
u
w
e
n
,
“
G
r
a
p
h
n
e
u
r
a
l
n
e
t
w
o
r
k
s
b
a
s
e
d
l
o
g
a
n
o
mal
y
d
e
t
e
c
t
i
o
n
a
n
d
e
x
p
l
a
n
a
t
i
o
n
,
”
i
n
P
ro
c
e
e
d
i
n
g
s
o
f
t
h
e
2
0
2
4
I
EE
E/
A
C
M
4
6
t
h
I
n
t
e
r
n
a
t
i
o
n
a
l
C
o
n
f
e
r
e
n
c
e
o
n
S
o
f
t
w
a
re
E
n
g
i
n
e
e
r
i
n
g
:
C
o
m
p
a
n
i
o
n
Pro
c
e
e
d
i
n
g
s
,
A
p
r
.
2
0
2
4
,
p
p
.
3
0
6
–
3
0
7
,
d
o
i
:
1
0
.
1
1
4
5
/
3
6
3
9
4
7
8
.
3
6
4
3
0
8
4
.
[
2
5
]
A
.
A
n
d
a
l
i
b
a
n
d
S
.
M
.
B
a
b
a
mi
r
,
“
A
n
o
ma
l
y
d
e
t
e
c
t
i
o
n
o
f
p
o
l
i
c
i
e
s
i
n
d
i
st
r
i
b
u
t
e
d
f
i
r
e
w
a
l
l
s
u
si
n
g
d
a
t
a
l
o
g
a
n
a
l
y
s
i
s
,
”
T
h
e
J
o
u
rn
a
l
o
f
S
u
p
e
r
c
o
m
p
u
t
i
n
g
,
v
o
l
.
7
9
,
n
o
.
1
7
,
p
p
.
1
9
4
7
3
–
1
9
5
1
4
,
N
o
v
.
2
0
2
3
,
d
o
i
:
1
0
.
1
0
0
7
/
s1
1
2
2
7
-
023
-
0
5
4
1
7
-
7.
[
2
6
]
J.
Je
g
a
n
,
M
.
R
.
S
u
g
u
n
a
,
M
.
S
h
o
b
a
n
a
,
H
.
A
z
a
t
h
,
S
.
M
u
r
u
g
a
n
,
a
n
d
M
.
R
a
j
mo
h
a
n
,
“
I
o
T
-
En
a
b
l
e
d
B
l
a
c
k
B
o
x
f
o
r
D
r
i
v
e
r
B
e
h
a
v
i
o
r
A
n
a
l
y
s
i
s
U
si
n
g
C
l
o
u
d
C
o
mp
u
t
i
n
g
,
”
i
n
2
0
2
4
I
n
t
e
r
n
a
t
i
o
n
a
l
C
o
n
f
e
r
e
n
c
e
o
n
A
d
v
a
n
c
e
s
i
n
D
a
t
a
En
g
i
n
e
e
ri
n
g
a
n
d
I
n
t
e
l
l
i
g
e
n
t
C
o
m
p
u
t
i
n
g
S
y
s
t
e
m
s (AD
I
C
S
)
,
A
p
r
.
2
0
2
4
,
p
p
.
1
–
6
,
d
o
i
:
1
0
.
1
1
0
9
/
A
D
I
C
S
5
8
4
4
8
.
2
0
2
4
.
1
0
5
3
3
4
7
1
.
[
2
7
]
G
.
T
h
a
h
n
i
y
a
t
h
e
t
a
l
.
,
“
C
l
o
u
d
b
a
s
e
d
p
r
e
d
i
c
t
i
o
n
o
f
e
p
i
l
e
p
t
i
c
s
e
i
z
u
r
e
s
u
s
i
n
g
r
e
a
l
-
t
i
me
e
l
e
c
t
r
o
e
n
c
e
p
h
a
l
o
g
r
a
ms
a
n
a
l
y
s
i
s,”
I
n
t
e
r
n
a
t
i
o
n
a
l
J
o
u
rn
a
l
o
f
E
l
e
c
t
r
i
c
a
l
a
n
d
C
o
m
p
u
t
e
r
En
g
i
n
e
e
ri
n
g
(
I
J
E
C
E)
,
v
o
l
.
1
4
,
n
o
.
5
,
p
.
6
0
4
7
,
O
c
t
.
2
0
2
4
,
d
o
i
:
1
0
.
1
1
5
9
1
/
i
j
e
c
e
.
v
1
4
i
5
.
p
p
6
0
4
7
-
6
0
5
6
.
[
2
8
]
J.
R
a
m
a
sam
y
,
E
.
S
r
i
v
i
d
h
y
a
,
V
.
V
a
i
d
e
h
i
,
S
.
V
i
ma
l
a
d
e
v
i
,
N
.
M
o
h
a
n
k
u
mar
,
a
n
d
S
.
M
u
r
u
g
a
n
,
“
C
l
o
u
d
-
e
n
a
b
l
e
d
i
s
o
l
a
t
i
o
n
f
o
r
e
s
t
f
o
r
a
n
o
ma
l
y
d
e
t
e
c
t
i
o
n
i
n
U
A
V
-
b
a
s
e
d
p
o
w
e
r
l
i
n
e
i
n
s
p
e
c
t
i
o
n
,
”
i
n
2
0
2
4
2
n
d
I
n
t
e
r
n
a
t
i
o
n
a
l
C
o
n
f
e
re
n
c
e
o
n
N
e
t
w
o
rki
n
g
a
n
d
C
o
m
m
u
n
i
c
a
t
i
o
n
s (I
C
N
WC
)
,
A
p
r
.
2
0
2
4
,
p
p
.
1
–
6
,
d
o
i
:
1
0
.
1
1
0
9
/
I
C
N
W
C
6
0
7
7
1
.
2
0
2
4
.
1
0
5
3
7
4
0
7
.
[
2
9
]
S
.
S
r
i
n
i
v
a
s
a
n
,
R
.
R
a
j
a
,
C
.
Je
h
a
n
,
S
.
M
u
r
u
g
a
n
,
C
.
S
r
i
n
i
v
a
sa
n
,
a
n
d
M
.
M
u
t
h
u
l
e
k
s
h
m
i
,
“
I
o
T
-
e
n
a
b
l
e
d
f
a
c
i
a
l
r
e
c
o
g
n
i
t
i
o
n
f
o
r
sm
a
r
t
h
o
s
p
i
t
a
l
i
t
y
f
o
r
c
o
n
t
a
c
t
l
e
ss
g
u
e
st
serv
i
c
e
s
a
n
d
i
d
e
n
t
i
t
y
v
e
r
i
f
i
c
a
t
i
o
n
,
”
i
n
2
0
2
4
1
1
t
h
I
n
t
e
r
n
a
t
i
o
n
a
l
C
o
n
f
e
r
e
n
c
e
o
n
R
e
l
i
a
b
i
l
i
t
y
,
I
n
f
o
c
o
m
T
e
c
h
n
o
l
o
g
i
e
s
a
n
d
O
p
t
i
m
i
z
a
t
i
o
n
(
T
ren
d
s
a
n
d
F
u
t
u
r
e
D
i
r
e
c
t
i
o
n
s)
(
I
C
RI
T
O
)
,
M
a
r
.
2
0
2
4
,
p
p
.
1
–
6
,
d
o
i
:
1
0
.
1
1
0
9
/
I
C
R
I
TO
6
1
5
2
3
.
2
0
2
4
.
1
0
5
2
2
3
6
3
.
[
3
0
]
V
.
V
.
B
a
s
k
a
r
,
S
.
S
e
k
a
r
,
K
.
S
.
R
a
j
e
s
h
,
N
.
C
.
S
e
n
d
h
i
l
k
u
m
a
r
,
T.
R
,
a
n
d
S
.
M
u
r
u
g
a
n
,
“
C
l
o
u
d
-
b
a
s
e
d
d
e
c
i
si
o
n
s
u
p
p
o
r
t
sy
st
e
ms
f
o
r
sec
u
r
i
n
g
f
a
r
m
-
to
-
t
a
b
l
e
t
r
a
c
e
a
b
i
l
i
t
y
u
s
i
n
g
I
o
T
a
n
d
K
N
N
A
l
g
o
r
i
t
h
m,
”
i
n
2
0
2
4
S
e
c
o
n
d
I
n
t
e
r
n
a
t
i
o
n
a
l
C
o
n
f
e
r
e
n
c
e
o
n
I
n
t
e
l
l
i
g
e
n
t
C
y
b
e
r
Ph
y
s
i
c
a
l
S
y
st
e
m
s
a
n
d
I
n
t
e
rn
e
t
o
f
T
h
i
n
g
s (I
C
o
I
C
I
)
,
A
u
g
.
2
0
2
4
,
p
p
.
4
4
3
–
4
4
8
,
d
o
i
:
1
0
.
1
1
0
9
/
I
C
o
I
C
I
6
2
5
0
3
.
2
0
2
4
.
1
0
6
9
6
6
5
9
.
[
3
1
]
S
.
S
e
k
a
r
e
t
a
l
.
,
“
I
n
t
r
u
si
o
n
d
e
t
e
c
t
i
o
n
a
n
d
p
r
e
v
e
n
t
i
o
n
u
s
i
n
g
B
a
y
e
si
a
n
d
e
c
i
s
i
o
n
w
i
t
h
f
u
z
z
y
l
o
g
i
c
sy
s
t
e
m
,
”
I
n
t
e
r
n
a
t
i
o
n
a
l
J
o
u
r
n
a
l
o
f
El
e
c
t
r
i
c
a
l
a
n
d
C
o
m
p
u
t
e
r
En
g
i
n
e
e
r
i
n
g
(
I
J
EC
E)
,
v
o
l
.
1
5
,
n
o
.
1
,
p
.
1
2
0
0
,
F
e
b
.
2
0
2
5
,
d
o
i
:
1
0
.
1
1
5
9
1
/
i
j
e
c
e
.
v
1
5
i
1
.
p
p
1
2
0
0
-
1
2
0
8
.
BIO
G
RA
PH
IES
O
F
AU
TH
O
R
S
J
o
h
n
Be
n
ito
J
e
sud
a
s
a
n
Pete
r
is
a
se
a
so
n
e
d
S
e
n
i
o
r
M
a
n
a
g
e
r
in
Da
ta
S
c
ien
c
e
a
n
d
En
g
in
e
e
ri
n
g
wit
h
a
trac
k
re
c
o
rd
o
f
o
v
e
r
1
7
y
e
a
rs
in
tec
h
n
o
l
o
g
y
lea
d
e
rsh
ip
i
n
t
h
e
Co
n
g
n
iza
n
t.
He
’
s
a
p
a
ss
io
n
a
te
a
n
d
in
n
o
v
a
ti
v
e
lea
d
e
r
wh
o
th
r
iv
e
s
o
n
d
e
li
v
e
ri
n
g
v
a
l
u
e
th
r
o
u
g
h
p
e
o
p
le,
p
ro
c
e
ss
e
s,
a
n
d
tec
h
n
o
lo
g
y
.
Cu
rre
n
t
ly
a
t
th
e
h
e
lm
o
f
Da
ta
S
c
ien
c
e
a
n
d
En
g
in
e
e
rin
g
a
t
Ca
p
it
a
lOn
e
,
Jo
h
n
h
o
ld
s
a
M
a
st
e
rs
in
Da
ta
S
c
ien
c
e
a
n
d
E
n
g
in
e
e
rin
g
fro
m
BIT
S
P
i
lan
i
,
u
n
d
e
rsc
o
ri
n
g
h
is
c
o
m
m
it
m
e
n
t
t
o
c
o
n
ti
n
u
o
u
s
lea
rn
in
g
a
n
d
p
r
o
f
e
ss
io
n
a
l
g
ro
wth
.
J
o
h
n
is
a
m
a
e
stro
in
d
e
fi
n
in
g
a
n
d
p
lan
n
i
n
g
M
L
Op
e
ra
ti
o
n
s
(M
LOp
s)
a
n
d
t
h
e
d
a
ta
e
n
g
i
n
e
e
ri
n
g
jo
u
rn
e
y
fo
r
o
r
g
a
n
iza
ti
o
n
s.
He
’
s
a
d
e
p
t
a
t
b
u
il
d
i
n
g
AI
-
b
a
se
d
a
n
o
m
a
ly
d
e
tec
ti
o
n
m
o
d
e
ls,
wit
h
a
fo
c
u
s
o
n
se
c
u
rit
y
,
a
n
d
a
rc
h
i
tec
ti
n
g
a
n
d
im
p
lem
e
n
ti
n
g
b
i
g
d
a
ta
p
i
p
e
li
n
e
s
a
n
d
d
a
ta
lak
e
s
in
AWS
.
Jo
h
n
’
s
e
x
ten
siv
e
li
st
o
f
c
e
rti
fica
ti
o
n
s
i
n
c
lu
d
e
s
IIM
Ba
n
g
a
l
o
re
’
s
P
e
o
p
le
M
a
n
a
g
e
m
e
n
t
c
e
rti
fica
ti
o
n
,
AWS
Ce
rti
fied
M
L
S
p
e
c
ialty
,
Da
tab
rick
s
Ce
rti
fied
Da
ta
E
n
g
in
e
e
r
As
so
c
iate
,
a
n
d
G
o
o
g
le
An
a
ly
ti
c
s
Ce
rti
fied
.
Wi
t
h
a
so
li
d
e
d
u
c
a
ti
o
n
a
l
b
a
c
k
g
r
o
u
n
d
,
i
n
c
lu
d
in
g
a
p
o
stg
ra
d
u
a
te
d
e
g
re
e
i
n
Da
ta S
c
ien
c
e
a
n
d
En
g
in
e
e
rin
g
a
n
d
a
n
o
th
e
r
i
n
Artifi
c
ial
I
n
telli
g
e
n
c
e
,
Jo
h
n
is we
ll
-
e
q
u
ip
p
e
d
fo
r
th
e
c
h
a
ll
e
n
g
e
s
o
f
t
h
e
in
d
u
str
y
.
H
e
’
s
a
lso
a
ffil
iate
d
with
Tex
a
s
M
c
Co
m
b
s,
th
e
U
n
iv
e
rsit
y
o
f
Tex
a
s a
t
Au
stin
.
He
c
a
n
b
e
c
o
tac
ted
a
t:
jo
h
n
b
e
n
it
o
a
u
t
h
o
r@
g
m
a
il
.
c
o
m
.
Dr
.
Nitin
Ra
k
e
sh
is
a
re
c
ip
ie
n
t
o
f
IBM
Dr
o
n
a
Aw
a
rd
a
n
d
To
p
1
0
S
tate
Aw
a
rd
Wi
n
n
e
r.
He
is
a
c
ti
v
e
m
e
m
b
e
r
o
f
p
ro
fe
ss
io
n
a
l
so
c
iety
li
k
e
S
e
n
i
o
r
M
e
m
b
e
r
IEE
E
(USA),
ACM,
S
IAM
(USA),
Li
fe
M
e
m
b
e
r
o
f
C
S
I
a
n
d
o
t
h
e
r
p
r
o
fe
ss
io
n
a
l
so
c
ieties
.
He
is
re
v
iew
e
r
o
f
se
v
e
ra
l
p
re
stig
io
u
s
Jo
u
rn
a
ls
/T
ra
n
sa
c
ti
o
n
s
li
k
e
IEE
E
Tran
sa
c
ti
o
n
s
o
n
Ve
h
icu
lar
Tec
h
n
o
l
o
g
y
,
Th
e
Co
m
p
u
ter
Jo
u
rn
a
l,
Ox
f
o
rd
P
re
ss
,
m
a
n
y
S
P
RING
ER/
o
th
e
r
S
c
o
p
u
s
in
d
e
x
e
d
I
n
tern
a
ti
o
n
a
l
Jo
u
rn
a
ls.
His
re
se
a
rc
h
o
u
tl
i
n
e
s
e
m
p
h
a
sis
o
n
Ne
two
r
k
Co
d
i
n
g
,
In
t
e
rc
o
n
n
e
c
ti
o
n
Ne
two
r
k
s
a
n
d
Arc
h
it
e
c
tu
re
a
n
d
O
n
l
in
e
P
h
a
n
t
o
m
Tran
sa
c
ti
o
n
s.
He
h
a
s
p
u
b
li
sh
e
d
v
a
ri
o
u
s
S
CI/
S
c
o
p
u
s
Jo
u
rn
a
l/
C
o
n
fe
re
n
c
e
p
u
b
li
c
a
ti
o
n
i
n
h
i
g
h
q
u
a
li
ty
jo
u
rn
a
ls
a
n
d
c
o
n
f
e
re
n
c
e
s.
Dr
Niti
n
is
h
a
v
in
g
m
o
re
th
a
n
9
0
p
a
te
n
ts/i
n
n
o
v
a
ti
o
n
s/d
e
sig
n
s
in
h
is
c
re
d
e
n
ti
a
ls.
Dr.
Niti
n
h
a
s
a
c
c
o
rd
e
d
se
v
e
ra
l
o
th
e
r
a
wa
r
d
s
f
o
r
b
e
st
p
a
p
e
r
p
u
b
li
sh
e
d
,
se
ss
io
n
c
h
a
irs,
h
i
g
h
e
st
c
i
ted
a
u
t
h
o
r,
b
e
st
st
u
d
e
n
t’s
th
e
sis g
u
id
e
d
,
a
n
d
m
a
n
y
o
th
e
rs.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
n
it
i
n
.
ra
k
e
sh
@g
m
a
il
.
c
o
m
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:
2502
-
4
7
5
2
S
V
M a
lg
o
r
ith
m
-
b
a
s
ed
a
n
o
m
a
ly
d
etec
tio
n
in
n
etw
o
r
k
lo
g
s
a
n
d
fir
ewa
ll lo
g
s
(
Jo
h
n
B
en
ito
J
esu
d
a
s
a
n
P
eter
)
1651
Pu
tta
sw
a
m
y
Re
k
h
a
is
a
P
ro
fe
ss
o
r
i
n
th
e
De
p
a
rtme
n
t
o
f
El
e
c
tro
n
ics
a
n
d
Co
m
m
u
n
ica
ti
o
n
E
n
g
i
n
e
e
rin
g
a
t
B
NM
In
stit
u
te o
f
Tec
h
n
o
lo
g
y
,
Ba
n
g
a
lo
re
,
a
n
d
Ka
rn
a
tak
a
.
S
h
e
h
a
s
m
o
re
th
a
n
2
0
y
e
a
rs
o
f
tea
c
h
in
g
a
n
d
1
5
y
e
a
rs
o
f
R&
D
e
x
p
e
rien
c
e
.
S
h
e
c
o
m
p
lete
d
h
e
r
P
h
.
D.
a
t
Visv
e
sv
a
ra
y
a
Tec
h
n
o
lo
g
ica
l
Un
iv
e
rsity
,
Be
lag
a
v
i
a
n
d
h
e
r
p
o
stg
ra
d
u
a
te stu
d
i
e
s (2
0
0
2
-
2
0
0
4
)
a
t
U
n
iv
e
rsit
y
Vis
v
e
sv
a
ra
y
a
Co
l
leg
e
o
f
En
g
in
e
e
rin
g
a
n
d
u
n
d
e
rg
ra
d
u
a
te
(
1
9
9
4
-
1
9
9
8
)
stu
d
ies
a
t
R
V
Co
ll
e
g
e
o
f
En
g
in
e
e
rin
g
,
Ba
n
g
a
lo
re
U
n
iv
e
rsit
y
.
He
r
re
se
a
rc
h
in
tere
sts
a
re
in
th
e
a
re
a
o
f
s
e
n
so
rs,
e
m
b
e
d
d
e
d
s
y
st
e
m
s,
Io
T,
n
e
two
r
k
se
c
u
rit
y
,
c
y
b
e
r
se
c
u
rit
y
,
ro
b
o
ti
c
s,
d
a
ta
a
n
a
ly
ti
c
s,
d
i
g
it
a
l
d
e
sig
n
,
a
n
d
m
icro
c
o
n
tr
o
l
e
rs.
Dr.
Re
k
h
a
P
h
a
s
b
e
e
n
p
a
rt
o
f
in
tern
a
ti
o
n
a
l
c
o
n
fe
re
n
c
e
s
in
v
a
rio
u
s
c
a
p
a
c
it
i
e
s.
S
h
e
h
a
s
a
tt
e
n
d
e
d
,
o
rg
a
n
ize
d
a
n
d
c
o
n
d
u
c
ted
v
a
rio
u
s
wo
rk
sh
o
p
s
fo
r
stu
d
e
n
tsa
n
d
fa
c
u
lt
y
d
e
v
e
lo
p
m
e
n
t
p
ro
g
ra
m
s.
Ti
l
l
d
a
te
sh
e
h
a
s
p
u
b
l
ish
e
d
m
o
r
e
th
a
n
2
0
re
se
a
rc
h
p
a
p
e
rs
in
v
a
rio
u
s
Na
ti
o
n
a
l,
In
tern
a
ti
o
n
a
l
Jo
u
rn
a
ls
(S
c
o
p
u
s
in
d
e
x
,
Web
o
f
sc
ien
c
e
,
U
G
C
imp
a
c
t
fa
c
to
rs)
a
n
d
c
o
n
fe
re
n
c
e
s
in
I
n
d
ia.
S
h
e
h
a
s
a
u
th
o
re
d
b
o
o
k
c
h
a
p
ters
wit
h
o
n
e
In
d
ian
D
e
sig
n
p
a
ten
t
g
ra
n
t
.
S
h
e
h
a
s
b
e
e
n
w
o
rk
i
n
g
a
s
a
c
o
n
su
lt
a
n
t
with
Ag
imu
s
Tec
h
n
o
l
o
g
ies
P
v
t.
Lt
d
.
a
n
d
Co
g
n
iv
e
rse
Lab
s
P
v
t
.
Lt
d
.
S
h
e
h
a
s
se
r
v
e
d
a
s
a
re
v
iew
m
e
m
b
e
r
in
In
tern
a
ti
o
n
a
l
Co
n
fe
re
n
c
e
s a
n
d
o
t
h
e
r
Jo
u
rn
a
ls.
S
h
e
h
a
s e
x
e
c
u
ted
fu
n
d
e
d
p
r
o
jec
ts r
e
c
e
iv
e
d
fro
m
AICT
E,
KSCS
T
,
a
n
d
Ne
wG
e
n
IEDC.
S
h
e
h
a
s
re
c
e
iv
e
d
b
e
st
p
a
p
e
r
a
wa
rd
fo
r
h
e
r
w
o
r
k
p
re
se
n
ted
i
n
t
h
e
ICCIIS
2
0
2
4
I
n
tern
a
ti
o
n
a
l
C
o
n
fe
re
n
c
e
.
S
h
e
c
a
n
b
e
c
o
n
tac
ted
a
t
:
re
k
h
a
p
@b
n
m
it
.
i
n
.
Dr
.
Ta
m
m
i
n
e
n
i
S
r
e
e
la
th
a
wo
rk
i
n
g
a
s
a
n
As
sista
n
t
P
ro
fe
ss
o
r
i
n
th
e
El
e
c
tro
n
ics
a
n
d
C
o
m
m
u
n
ica
ti
o
n
E
n
g
i
n
e
e
ri
n
g
De
p
a
rtme
n
t
a
t
t
h
e
Ko
n
e
r
u
Lak
s
h
m
a
iah
Ed
u
c
a
ti
o
n
F
o
u
n
d
a
ti
o
n
l
o
c
a
ted
in
Va
d
d
e
sw
a
ra
m
,
G
u
n
tu
r
District,
A
n
d
h
ra
P
ra
d
e
sh
.
S
h
e
h
a
s
1
3
y
e
a
rs
o
f
tea
c
h
in
g
e
x
p
e
rien
c
e
a
n
d
e
a
rn
e
d
h
e
r
P
h
.
D
.
i
n
El
e
c
tro
n
ics
a
n
d
Co
m
m
u
n
ica
ti
o
n
En
g
i
n
e
e
rin
g
fro
m
Ja
wa
h
a
rlal
Ne
h
ru
Tec
h
n
o
l
o
g
ica
l
Un
i
v
e
rsity
An
a
n
tap
u
r.
S
h
e
i
s
sp
e
c
ialize
d
in
le
v
e
ra
g
in
g
M
L
tec
h
n
i
q
u
e
s
f
o
r
Im
a
g
e
P
ro
c
e
ss
in
g
a
p
p
li
c
a
ti
o
n
s.
T
h
ro
u
g
h
o
u
t
h
e
r
a
c
a
d
e
m
ic
c
a
r
e
e
r,
sh
e
h
a
s
c
o
n
tri
b
u
ted
2
0
p
a
p
e
rs
to
i
n
te
rn
a
ti
o
n
a
l
jo
u
r
n
a
ls
in
h
e
r
fiel
d
o
f
e
x
p
e
rti
se
.
S
h
e
is
a
n
a
c
ti
v
e
li
f
e
m
e
m
b
e
r
o
f
th
e
In
d
ian
S
o
c
iety
f
o
r
Tec
h
n
ica
l
Ed
u
c
a
ti
o
n
(IS
TE
)
a
n
d
h
o
l
d
s m
e
m
b
e
rsh
ip
s in
b
o
t
h
th
e
In
tern
a
ti
o
n
a
l
As
so
c
iatio
n
o
f
En
g
in
e
e
rs
(IAENG
)
a
n
d
th
e
In
tern
a
ti
o
n
a
l
As
so
c
iati
o
n
o
f
Co
m
p
u
ter
S
c
ien
c
e
a
n
d
I
n
fo
rm
a
ti
o
n
Tec
h
n
o
lo
g
y
(IACSIT
).
Ad
d
it
i
o
n
a
ll
y
,
s
h
e
h
a
s
p
a
rti
c
ip
a
ted
in
n
u
m
e
ro
u
s
train
i
n
g
se
ss
io
n
s
a
n
d
c
o
n
fe
re
n
c
e
s
th
a
t
fo
c
u
s
o
n
a
d
v
a
n
c
e
m
e
n
ts
in
sig
n
a
l
a
n
d
ima
g
e
p
ro
c
e
ss
in
g
.
S
h
e
c
a
n
b
e
c
o
n
tac
ted
a
t:
sre
e
lath
a
4
5
7
@
g
m
a
il
.
c
o
m
.
Dr
.
Ve
lu
sa
m
y
S
u
j
a
th
a
re
c
e
i
v
e
d
h
e
r
d
o
c
to
ra
l
d
e
g
re
e
a
n
d
p
o
st
-
g
ra
d
u
a
ti
o
n
fr
o
m
An
n
a
Un
iv
e
rsity
,
C
h
e
n
n
a
i,
a
n
d
h
e
r
u
n
d
e
rg
ra
d
u
a
te
d
e
g
re
e
fro
m
M
a
d
ra
s
Un
i
v
e
rsity
.
He
r
re
se
a
rc
h
fo
c
u
se
s
o
n
d
e
sig
n
in
g
c
h
a
rg
e
p
u
m
p
P
h
a
se
-
Lo
c
k
e
d
Lo
o
p
s,
with
a
k
e
e
n
i
n
tere
st
in
An
a
lo
g
VLS
I
.
S
h
e
b
e
g
a
n
h
e
r
c
a
re
e
r
a
s
a
l
e
c
tu
re
r
in
1
9
9
3
a
n
d
h
a
s
se
rv
e
d
a
s
a
n
a
c
a
d
e
m
icia
n
in
v
a
rio
u
s
re
p
u
ted
in
sti
tu
ti
o
n
s,
h
o
l
d
in
g
se
v
e
ra
l
p
o
siti
o
n
s
a
n
d
g
u
id
i
n
g
n
u
m
e
ro
u
s
UG
a
n
d
P
G
p
ro
jec
ts.
Cu
rre
n
tl
y
,
sh
e
is
a
P
r
o
f
e
ss
o
r
in
th
e
De
p
a
r
tme
n
t
o
f
El
e
c
t
ro
n
ics
a
n
d
Co
m
m
u
n
ica
ti
o
n
En
g
i
n
e
e
rin
g
(EC
E)
a
t
S
h
re
e
S
a
th
y
a
m
Co
ll
e
g
e
o
f
E
n
g
in
e
e
rin
g
a
n
d
Tec
h
n
o
lo
g
y
,
S
a
n
k
a
ri
,
Tam
il
Na
d
u
.
S
h
e
c
a
n
b
e
c
o
n
tac
te
d
a
t:
i
n
fo
@s
h
re
e
sa
th
y
a
m
.
e
d
u
.
i
n
.
Dr
.
S
u
r
u
li
v
e
lu
M
u
th
u
m
a
r
il
a
k
s
h
m
i
is
a
n
As
so
c
iate
P
ro
fe
ss
o
r
in
Co
m
p
u
ter
S
c
ien
c
e
a
n
d
En
g
i
n
e
e
rin
g
a
t
C
h
e
n
n
a
i
I
n
stit
u
te
o
f
Tec
h
n
o
l
o
g
y
,
Ku
n
d
ra
th
u
r
.
Wi
th
o
v
e
r
1
3
y
e
a
r
s
o
f
tea
c
h
i
n
g
e
x
p
e
rien
c
e
,
m
y
p
ri
m
a
ry
fo
c
u
s
re
v
o
l
v
e
s
a
ro
u
n
d
C
o
m
p
u
ter
Ne
two
rk
s.
I
a
m
p
a
rti
c
u
larly
i
n
tere
ste
d
in
in
v
e
stig
a
ti
n
g
n
e
two
r
k
p
r
o
to
c
o
ls,
se
c
u
ri
ty
m
e
a
su
re
s,
a
n
d
wa
y
s
t
o
o
p
ti
m
ize
n
e
tw
o
rk
p
e
rfo
rm
a
n
c
e
.
M
y
p
a
ss
io
n
li
e
s
i
n
re
se
a
rc
h
in
g
a
n
d
p
u
b
li
s
h
in
g
a
rti
c
les
t
h
a
t
d
e
lv
e
in
t
o
t
h
e
se
a
re
a
s,
a
imin
g
to
e
n
h
a
n
c
e
o
u
r
u
n
d
e
rsta
n
d
i
n
g
o
f
r
o
b
u
st
n
e
two
r
k
sy
ste
m
s
a
n
d
c
o
n
tri
b
u
te
v
a
l
u
a
b
le
in
si
g
h
ts
t
o
t
h
e
a
c
a
d
e
m
ic
c
o
m
m
u
n
it
y
.
S
h
e
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
m
u
th
u
3
0
4
1
9
7
4
@g
m
a
il
.
c
o
m
.
S
h
a
n
m
u
g
a
m
S
u
j
a
th
a
is
a
n
a
d
ju
n
c
t
p
r
o
fe
ss
o
r,
S
a
v
e
e
th
a
S
c
h
o
o
l
o
f
E
n
g
i
n
e
e
rin
g
,
S
a
v
e
e
th
a
In
sti
tu
te
o
f
M
e
d
ica
l
a
n
d
Tec
h
n
ica
l
S
c
ien
c
e
s,
C
h
e
n
n
a
i,
Tam
il
n
a
d
u
,
I
n
d
ia.
S
h
e
p
u
b
li
sh
e
d
h
e
r
re
se
a
rc
h
a
rti
c
les
in
m
a
n
y
i
n
tern
a
ti
o
n
a
l
a
n
d
n
a
ti
o
n
a
l
c
o
n
fe
re
n
c
e
s
a
n
d
jo
u
r
n
a
ls.
He
r
re
se
a
rc
h
a
re
a
s
in
c
lu
d
e
n
e
two
rk
se
c
u
rit
y
a
n
d
M
L
.
S
h
e
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
su
jath
a
sm
v
r@g
m
a
il
.
c
o
m
.
Evaluation Warning : The document was created with Spire.PDF for Python.