I
nte
rna
t
io
na
l J
o
ur
na
l o
f
I
nfo
rm
a
t
ics a
nd
Co
m
m
un
ica
t
io
n T
ec
hn
o
lo
g
y
(
I
J
-
I
CT
)
Vo
l.
1
5
,
No
.
1
,
M
ar
ch
20
2
6
,
p
p
.
43
8
~
44
6
I
SS
N:
2252
-
8
7
7
6
,
DOI
:
1
0
.
1
1
5
9
1
/iji
ct
.
v
1
5
i
1
.
pp
43
8
-
44
6
438
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//ij
ict.
ia
esco
r
e.
co
m
Securing
De
fi:
a c
o
mprehens
iv
e re
v
iew of
ML
a
pp
ro
a
ches for
detec
ting sm
a
rt
c
o
ntract vulnera
bi
lities and
threats
Dhiv
y
a
la
k
s
hm
i V
enk
a
t
ra
m
a
n,
M
a
nik
a
nd
a
n K
up
p
us
a
m
y
S
c
h
o
o
l
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
(
S
C
O
P
E)
,
V
e
l
l
o
r
e
I
n
st
i
t
u
t
e
o
f
Te
c
h
n
o
l
o
g
y
(
V
I
T
U
n
i
v
e
r
s
i
t
y
)
,
V
e
l
l
o
r
e
,
T
a
mi
l
N
a
d
u
,
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
J
u
n
2
3
,
2
0
2
4
R
ev
is
ed
Oct
1
9
,
2
0
2
5
Acc
ep
ted
No
v
5
,
2
0
2
5
Th
e
ra
p
i
d
e
v
o
l
u
ti
o
n
o
f
d
e
c
e
n
trali
z
e
d
fi
n
a
n
c
e
(De
F
i)
h
a
s
b
r
o
u
g
h
t
re
v
o
lu
ti
o
n
a
ry
in
n
o
v
a
ti
o
n
s
t
o
g
lo
b
a
l
fi
n
a
n
c
ial
sy
ste
m
s;
h
o
we
v
e
r,
it
h
a
s
a
lso
re
v
e
a
led
so
m
e
m
a
jo
r
se
c
u
rit
y
v
u
ln
e
ra
b
il
it
ies
,
e
sp
e
c
ially
o
f
sm
a
rt
c
o
n
trac
ts.
Trad
it
io
n
a
l
a
u
d
it
i
n
g
m
e
th
o
d
s
a
n
d
sta
ti
c
a
n
a
ly
sis
t
o
o
ls
a
re
p
r
o
n
e
to
fa
il
i
n
id
e
n
ti
f
y
in
g
so
p
h
isti
c
a
ted
th
re
a
ts,
in
c
lu
d
in
g
re
e
n
t
ra
n
c
y
a
tt
a
c
k
s,
fr
o
nt
-
ru
n
n
in
g
,
o
ra
c
le
m
a
n
ip
u
latio
n
,
a
n
d
h
o
n
e
y
p
o
ts.
Th
is
re
v
iew
d
isc
u
ss
e
s
th
e
g
r
o
win
g
r
o
le
o
f
m
a
c
h
in
e
lea
rn
i
n
g
(
M
L)
i
n
e
n
h
a
n
c
in
g
t
h
e
se
c
u
rit
y
o
f
De
F
i
sy
ste
m
s.
It
p
ro
v
id
e
s
a
c
o
m
p
re
h
e
n
siv
e
o
v
e
r
v
iew
o
f
m
o
d
e
rn
M
L
-
b
a
se
d
m
e
th
o
d
s
re
late
d
to
t
h
e
d
e
tec
ti
o
n
o
f
sm
a
rt
c
o
n
tr
a
c
t
v
u
l
n
e
ra
b
il
it
ies
,
tran
sa
c
ti
o
n
-
le
v
e
l
fra
u
d
d
e
tec
ti
o
n
,
a
n
d
o
ra
c
le
tr
u
st
a
ss
e
ss
m
e
n
t.
Th
e
p
a
p
e
r
a
lso
p
r
o
v
i
d
e
s
p
u
b
li
c
l
y
a
v
a
il
a
b
le
d
a
tas
e
ts,
n
e
c
e
ss
a
ry
to
o
lk
it
s
,
a
n
d
a
rc
h
it
e
c
tu
ra
l
d
e
si
g
n
s
u
se
d
fo
r
d
e
v
e
lo
p
in
g
a
n
d
tes
ti
n
g
t
h
e
se
m
o
d
e
ls.
Ad
d
i
ti
o
n
a
ll
y
,
it
p
r
o
v
i
d
e
s
fu
tu
re
d
irec
ti
o
n
s
li
k
e
fe
d
e
ra
ted
lea
rn
i
n
g
,
e
x
p
lain
a
b
le
AI,
re
a
l
-
ti
m
e
m
e
m
p
o
o
l
in
sp
e
c
ti
o
n
,
a
n
d
c
ro
ss
-
c
h
a
in
in
tell
ig
e
n
c
e
sh
a
rin
g
.
W
h
il
e
it
is
fu
ll
o
f
p
ro
m
ise
,
th
e
a
p
p
li
c
a
ti
o
n
o
f
M
L
i
n
De
F
i
se
c
u
rit
y
is
p
lag
u
e
d
b
y
issu
e
s
li
k
e
d
a
ta
sc
a
rc
it
y
,
in
tero
p
e
ra
b
il
it
y
,
a
n
d
e
x
p
lain
a
b
il
it
y
.
T
h
is
p
a
p
e
r
c
o
n
c
lu
d
e
s
b
y
h
ig
h
li
g
h
ti
n
g
th
e
n
e
e
d
fo
r
sta
n
d
a
r
d
ise
d
b
e
n
c
h
m
a
rk
s,
sh
a
re
d
d
a
ta
i
n
it
iati
v
e
s,
a
n
d
t
h
e
i
n
teg
ra
ti
o
n
o
f
M
L
in
t
o
d
e
v
e
l
o
p
m
e
n
t
p
i
p
e
li
n
e
s
t
o
d
e
li
v
e
r
se
c
u
re
,
sc
a
lab
le,
a
n
d
re
li
a
b
le De
F
i
e
c
o
sy
s
tem
s.
K
ey
w
o
r
d
s
:
E
x
p
lain
ab
le
AI
Mach
in
e
l
ea
r
n
in
g
Secu
r
ity
attac
k
s
Sm
ar
t c
o
n
tr
ac
ts
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
:
Ma
n
ik
an
d
an
Ku
p
p
u
s
am
y
Sch
o
o
l o
f
C
o
m
p
u
ter
Scien
ce
a
n
d
E
n
g
in
e
er
in
g
(
SC
OPE)
,
Vello
r
e
I
n
s
titu
te
o
f
T
ec
h
n
o
l
o
g
y
(
VI
T
Un
iv
er
s
ity
)
Vello
r
e,
T
am
il Na
d
u
,
I
n
d
ia
k
m
an
ik
an
d
an
@
v
it.a
c.
in
1.
I
NT
RO
D
UCT
I
O
N
Dec
en
tr
ali
z
ed
f
in
a
n
ce
(
DeFi)
d
is
r
u
p
ts
f
in
an
cial
s
er
v
ices
b
y
f
ac
ilit
atin
g
p
ee
r
-
to
-
p
ee
r
tr
an
s
ac
tio
n
s
u
s
in
g
b
lo
ck
c
h
ain
an
d
s
m
ar
t c
o
n
tr
ac
ts
with
o
u
t le
g
ac
y
in
ter
m
e
d
iar
ies.
Alth
o
u
g
h
DeFi
is
tr
an
s
p
ar
en
t,
ac
ce
s
s
ib
le,
an
d
p
r
o
g
r
am
m
a
b
le,
it
is
al
s
o
in
cr
ea
s
in
g
ly
tar
g
eted
b
y
s
ec
u
r
ity
r
is
k
s
b
ec
au
s
e
it
is
o
p
en
[
1
]
.
Sm
ar
t
co
n
tr
ac
ts
,
alth
o
u
g
h
ca
p
ab
le,
ar
e
s
u
s
ce
p
tib
le
to
co
d
in
g
er
r
o
r
s
an
d
ex
p
lo
itat
io
n
,
ev
id
en
ce
d
b
y
h
ig
h
-
p
r
o
f
ile
h
ac
k
s
s
u
ch
as
th
e
DAO
h
ac
k
an
d
o
r
ac
le
attac
k
s
.
L
eg
ac
y
s
ec
u
r
ity
s
o
lu
tio
n
s
,
alth
o
u
g
h
h
e
lp
f
u
l,
ar
e
n
o
t
s
ca
lab
le
an
d
d
o
n
o
t
k
ee
p
p
ac
e
with
em
er
g
in
g
th
r
ea
ts
[
1
]
-
[
6
]
.
Ma
ch
i
n
e
l
ea
r
n
i
n
g
(
ML
)
,
o
n
th
e
o
th
er
h
an
d
,
o
f
f
er
s
a
p
r
o
m
is
in
g
s
o
l
u
tio
n
b
y
allo
win
g
a
d
ap
tiv
e,
d
ata
-
d
r
iv
en
d
etec
tio
n
o
f
an
o
m
alies
an
d
v
u
ln
er
a
b
ilit
ies
an
d
p
r
o
v
id
in
g
r
ea
l
-
tim
e
DeFi
s
y
s
tem
p
r
o
tectio
n
[
7
]
-
[
11
].
T
h
is
s
tu
d
y
p
r
o
v
id
es
an
in
-
d
ep
th
ex
a
m
in
atio
n
o
f
how
ML
m
eth
o
d
s
ar
e
p
r
esen
tly
b
ein
g
a
p
p
lied
a
n
d
c
an
b
e
f
u
r
th
er
o
p
tim
is
ed
to
im
p
r
o
v
e
DeFi
s
ec
u
r
ity
,
n
a
m
ely
i
n
th
e
ar
ea
s
o
f
s
m
ar
t
co
n
tr
ac
t v
u
l
n
er
ab
ilit
y
s
ca
n
n
i
n
g
,
o
r
ac
le
tr
u
s
two
r
th
in
ess
as
s
es
s
m
en
t,
an
d
tr
a
n
s
ac
tio
n
al
th
r
ea
t
m
itig
atio
n
.
T
h
e
s
co
p
e
of
th
e
en
d
ea
v
o
u
r
is
d
ef
in
ed
by
th
r
ee
u
n
d
er
ly
in
g
g
o
als:
1.
To
im
p
r
o
v
e
f
r
o
n
t
-
r
u
n
n
i
n
g
d
et
ec
tio
n
alg
o
r
ith
m
s
by
co
m
b
in
i
n
g
b
eh
a
v
io
u
r
al,
tr
an
s
ac
tio
n
-
le
v
el,
an
d
to
k
en
-
s
p
ec
if
ic
f
ea
tu
r
es
with
ML
m
o
d
els.
Fro
n
t
-
r
u
n
n
in
g
,
wh
e
r
e
attac
k
er
s
r
eo
r
d
e
r
tr
an
s
ac
tio
n
s
in
th
e
m
em
p
o
o
l
to
ea
r
n
ar
b
itra
g
e
g
ain
s
,
is
o
n
e
o
f
th
e
m
o
s
t
ex
p
lo
itab
le
wea
k
n
ess
es
in
DE
Xs.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
I
SS
N:
2252
-
8
7
7
6
S
ec
u
r
in
g
Defi
:
a
c
o
mp
r
eh
en
s
ive
r
ev
iew
o
f
ML
a
p
p
r
o
a
ch
es fo
r
d
etec
tin
g
…
(
Dh
ivya
l
a
ksh
mi
V
en
ka
tr
a
m
an
)
439
2.
T
o
cr
e
ate
an
ML
-
b
ased
s
y
s
tem
to
esti
m
ate
d
ata
tr
u
s
t
v
alu
e
r
ec
eiv
ed
th
r
o
u
g
h
o
r
ac
les,
th
ir
d
-
p
ar
ty
s
er
v
ices
em
p
lo
y
ed
b
y
s
m
ar
t
co
n
tr
ac
ts
to
r
etr
iev
e
r
ea
l
-
w
o
r
ld
in
f
o
r
m
at
io
n
(
e.
g
.
,
ass
et
p
r
ices)
.
C
o
m
p
r
o
m
is
ed
o
r
ac
les
ca
n
r
esu
lt in
im
p
r
o
p
er
c
o
n
tr
ac
t
ex
ec
u
tio
n
,
li
q
u
id
ati
o
n
o
p
e
r
atio
n
s
,
an
d
m
ar
k
et
m
a
n
ip
u
latio
n
.
3.
Har
n
ess
an
d
ex
p
e
r
im
en
t
with
s
o
p
h
is
ticated
ML
tech
n
iq
u
e
s
,
s
u
ch
as
lo
n
g
-
s
h
o
r
t
-
ter
m
m
em
o
r
y
(
L
STM
)
n
etwo
r
k
s
,
g
r
ap
h
n
eu
r
al
n
etwo
r
k
s
(
GNNs)
,
an
d
tr
an
s
f
o
r
m
er
-
b
ased
ar
ch
itectu
r
es,
to
id
e
n
tif
y
s
tr
u
ctu
r
al
a
n
d
s
em
an
tic
v
u
ln
er
ab
ilit
ies in
s
m
ar
t c
o
n
tr
ac
ts
.
DeFi
is
a
n
ew
f
in
an
cial
s
y
s
tem
b
ased
o
n
p
u
b
lic
b
lo
ck
c
h
ain
s
s
u
ch
as
E
th
er
eu
m
,
i
n
w
h
ich
s
m
ar
t
co
n
tr
ac
ts
ex
ec
u
te
tr
an
s
ac
tio
n
s
in
an
in
ter
m
ed
iar
y
-
f
r
ee
e
n
v
ir
o
n
m
en
t
[
12
]
-
[
15
]
.
C
o
m
p
o
s
e
d
in
p
r
o
g
r
am
m
i
n
g
lan
g
u
a
g
es
s
u
ch
as
So
lid
ity
a
n
d
r
u
n
o
n
t
h
e
E
th
er
eu
m
Vir
tu
al
Ma
ch
in
e,
th
ese
co
n
tr
ac
ts
en
ab
le
ap
p
licatio
n
s
s
u
ch
as
Un
is
wap
,
Aav
e,
an
d
DAI
.
DeFi
p
r
o
v
id
es
tr
an
s
p
ar
en
cy
a
n
d
o
p
en
n
ess
,
b
u
t
co
m
p
o
s
ab
ilit
y
an
d
im
m
u
tab
ilit
y
cr
ea
te
s
o
p
h
is
ticated
r
is
k
s
.
I
n
ter
d
ep
e
n
d
en
t
co
n
tr
ac
ts
an
d
im
m
u
ta
b
le
co
d
e
m
ak
e
s
y
s
tem
s
v
u
ln
er
ab
le
to
i
r
r
em
ed
ia
b
le
wea
k
n
ess
[
16
]
-
[
19
]
.
Sm
ar
t
co
n
t
r
ac
ts
,
wo
n
d
er
f
u
l
as
th
ey
a
r
e,
ar
e
n
o
t
im
m
u
n
e
to
co
d
in
g
b
u
g
s
an
d
d
esig
n
f
laws.
C
er
tain
v
u
ln
er
ab
ilit
ies
h
av
e
l
ed
to
th
e
lo
s
s
o
f
v
ast
am
o
u
n
ts
o
f
m
o
n
ey
in
DeFi
[
2
0
]
-
[
22
]
.
R
ee
n
tr
an
c
y
is
a
s
e
r
io
u
s
s
m
ar
t
co
n
tr
ac
t
v
u
ln
e
r
ab
il
ity
b
ec
au
s
e
a
c
o
n
tr
ac
t
ca
lls
e
x
ter
n
al
co
d
e
b
ef
o
r
e
ch
an
g
in
g
its
s
tate,
leav
in
g
th
e
d
o
o
r
o
p
e
n
f
o
r
an
attac
k
e
r
'
s
ex
ter
n
al
co
n
tr
ac
t
to
r
e
p
ea
ted
ly
ca
ll
th
e
o
r
ig
in
al
f
u
n
ctio
n
a
n
d
d
r
ain
f
u
n
d
s
(
as
i
n
th
e
2
0
1
6
DAO
h
ac
k
th
at
r
e
s
u
lted
i
n
th
ef
t
o
f
m
o
r
e
t
h
an
$
6
0
m
illi
o
n
in
E
th
er
v
alu
e)
[
2
3
]
-
[
2
6
]
.
I
n
teg
er
u
n
d
er
f
lo
w
an
d
o
v
er
f
lo
w
is
an
o
t
h
er
p
r
im
ar
y
v
u
l
n
er
ab
ilit
y
b
ec
au
s
e
ea
r
ly
So
lid
ity
v
er
s
io
n
s
d
id
n
o
t
i
n
clu
d
e
ar
ith
m
etic
ch
ec
k
s
,
wh
ich
allo
wed
th
e
f
law
to
b
e
u
s
ed
b
y
attac
k
er
s
to
wr
ap
v
alu
es
ar
o
u
n
d
(
d
e
c
r
em
en
tin
g
0
,
e.
g
.
,
r
esu
lts
in
2
^2
5
6
–
1
)
t
o
cir
cu
m
v
en
t
b
alan
ce
o
r
lim
it
ch
ec
k
s
,
alth
o
u
g
h
s
in
ce
th
e
n
it
h
as
b
ee
n
m
o
s
tly
er
ad
icate
d
b
y
th
e
in
tr
o
d
u
ctio
n
o
f
Saf
eM
ath
lib
r
ar
ies
an
d
a
b
etter
la
n
g
u
ag
e
[
1
]
.
Acc
ess
co
n
tr
o
l
wea
k
n
ess
es
ar
e
an
o
th
er
r
is
k
,
wh
er
e
b
u
g
g
y
o
r
ex
p
o
s
ed
ac
ce
s
s
m
o
d
if
ier
s
(
s
u
c
h
as
t
h
e
o
n
es
th
at
s
e
cu
r
e
ad
m
in
is
tr
ativ
e
ac
tio
n
s
,
s
u
ch
as
p
au
s
in
g
a
co
n
tr
ac
t
o
r
m
in
tin
g
co
in
s
)
allo
w
an
attac
k
er
to
tak
e
p
r
iv
ileg
ed
ac
tio
n
s
an
d
th
u
s
g
ain
co
n
t
r
o
l
o
f
p
r
o
to
c
o
l
b
e
h
av
io
r
,
m
in
t
u
n
k
n
o
wn
to
k
e
n
s
,
o
r
d
r
ain
r
es
er
v
e
h
o
ld
i
n
g
s
[
1
]
,
[
9
]
.
Or
ac
le
m
an
ip
u
latio
n
in
v
o
lv
es
m
an
ip
u
latin
g
th
e
ex
ter
n
al
d
a
ta
s
o
u
r
ce
s
(
o
r
o
r
ac
les)
o
n
wh
ich
s
m
ar
t
co
n
t
r
ac
ts
d
ep
en
d
f
o
r
th
eir
o
f
f
-
ch
ain
i
n
f
o
r
m
atio
n
(
e.
g
.
,
th
e
p
r
ice
o
f
as
s
ets),
wh
ich
th
e
co
n
tr
ac
ts
th
e
n
ac
t
u
p
o
n
b
ased
o
n
f
alse
o
r
s
tale
in
f
o
r
m
a
tio
n
;
as
an
ex
am
p
le,
in
2
0
2
0
th
e
b
Z
x
p
r
o
to
co
l
was
att
ac
k
ed
th
r
o
u
g
h
t
h
e
m
an
ip
u
latio
n
o
f
th
e
p
r
ice
f
ee
d
s
,
r
esu
ltin
g
in
s
o
m
e
$
1
m
illi
o
n
in
lo
s
s
es b
y
ar
ti
f
icially
m
is
p
r
icin
g
ass
ets [
1
0
]
.
2.
F
RO
NT
-
RUNN
I
NG
DE
T
E
CT
I
O
N
U
SI
NG
B
E
H
AV
I
O
URAL
M
L
F
E
AT
URE
S
Fro
n
t
-
r
u
n
n
in
g
is
o
n
e
o
f
th
e
m
o
s
t
p
er
s
is
ten
t
an
d
d
am
ag
in
g
th
r
ea
ts
in
DeFi,
en
ab
led
b
y
th
e
tr
an
s
p
ar
en
t
n
atu
r
e
o
f
b
lo
c
k
ch
ain
s
y
s
tem
s
[
1
]
,
[
1
2
]
.
Un
lik
e
tr
ad
itio
n
al
f
in
an
cial
m
ar
k
ets,
wh
er
e
in
s
id
er
tr
ad
in
g
is
o
f
ten
h
id
d
en
a
n
d
illeg
al,
b
lo
ck
ch
ai
n
tech
n
o
l
o
g
y
allo
ws
all
p
e
n
d
in
g
tr
an
s
ac
tio
n
s
to
b
e
v
is
ib
le
in
th
e
m
em
p
o
o
l,
a
p
u
b
lic
q
u
e
u
e
o
f
u
n
co
n
f
ir
m
e
d
tr
an
s
ac
tio
n
s
[
5
]
,
[
1
3
]
.
T
h
i
s
g
iv
es
m
alicio
u
s
ac
to
r
s
,
o
f
t
en
u
s
in
g
b
o
ts
,
an
o
p
p
o
r
tu
n
ity
to
m
o
n
ito
r
,
an
aly
s
e,
an
d
m
an
ip
u
late
th
ese
tr
an
s
ac
tio
n
s
f
o
r
f
in
a
n
cial
g
ain
.
M
L
,
with
its
ab
il
ity
to
d
etec
t
b
eh
av
i
o
u
r
al
p
atter
n
s
a
n
d
s
eq
u
e
n
ce
an
o
m
alies,
h
as
em
er
g
e
d
as
a
p
o
wer
f
u
l
to
o
l
in
d
etec
tin
g
an
d
p
o
ten
tially
p
r
ev
en
tin
g
f
r
o
n
t
-
r
u
n
n
in
g
attac
k
s
in
r
ea
l
-
tim
e
[
1
0
]
,
[
1
4
]
.
I
t
is
a
b
lo
c
k
ch
ain
e
x
p
lo
it
m
eth
o
d
i
n
wh
ich
p
r
o
f
itab
le
t
r
an
s
ac
tio
n
s
ar
e
s
ee
n
in
th
e
m
em
p
o
o
l,
an
d
co
m
p
e
titi
v
e
tr
an
s
ac
tio
n
s
with
h
ig
h
er
g
as
f
e
es
ar
e
ad
d
e
d
af
ter
war
d
s
to
s
ec
u
r
e
th
em
t
o
e
x
ec
u
te
f
ir
s
t
[
9
]
.
T
h
is
ex
p
lo
it
is
esp
ec
ially
ef
f
ec
tiv
e
in
au
to
m
a
ted
m
ar
k
et
m
ak
er
s
(
AM
Ms)
s
u
ch
as Un
i
s
wap
,
wh
er
e
ass
et
p
r
ices a
r
e
d
y
n
am
icall
y
d
eter
m
in
ed
b
ased
o
n
ex
ec
u
t
ed
tr
ad
es,
en
ab
lin
g
attac
k
er
s
to
b
en
ef
it
b
y
ex
ec
u
tin
g
in
f
r
o
n
t
o
f
lar
g
e
tr
an
s
ac
tio
n
s
[
1
5
]
.
Dis
p
lace
m
en
t,
wh
er
e
th
e
f
ir
s
t
tr
an
s
ac
tio
n
is
p
u
s
h
ed
o
u
t,
r
esu
ltin
g
in
it
f
ailin
g
,
in
s
er
tio
n
,
wh
er
e
th
e
at
tack
er
ex
ec
u
tes
th
eir
tr
a
n
s
ac
tio
n
im
m
ed
iately
in
f
r
o
n
t
o
f
t
h
e
tar
g
et
tr
an
s
ac
tio
n
to
tak
e
ad
v
an
ta
g
e
o
f
ex
p
e
cted
p
r
ice
m
o
v
em
e
n
t,
an
d
s
an
d
wich
in
g
,
wh
e
r
e
attac
k
er
s
ex
ec
u
te
tr
a
n
s
ac
tio
n
s
in
f
r
o
n
t
o
f
an
d
b
eh
i
n
d
th
e
v
ictim
'
s
tr
an
s
ac
tio
n
to
tak
e
ad
v
an
tag
e
o
f
p
r
ice
m
o
v
em
en
t
f
r
o
m
b
o
th
s
id
es,
ar
e
ty
p
ical
f
r
o
n
t
-
ru
n
n
in
g
e
x
a
m
p
les.
T
h
ese
m
eth
o
d
s
to
g
eth
er
ar
e
well
-
k
n
o
wn
ex
am
p
les o
f
m
a
x
im
al
ex
tr
ac
ta
b
le
v
alu
e
(
ME
V)
ex
p
lo
itatio
n
in
b
lo
ck
c
h
ain
n
etwo
r
k
s
[
9
]
,
[
1
5
]
.
2
.
1
.
T
ra
ditio
na
l
det
ec
t
io
n
met
ho
ds
His
to
r
ically
,
f
r
o
n
t
-
r
u
n
n
in
g
d
et
ec
tio
n
o
r
p
r
ev
e
n
tio
n
h
as
co
n
s
is
ted
o
f
l
o
o
k
i
n
g
f
o
r
th
e
s
am
e
o
r
s
im
ilar
tr
an
s
ac
tio
n
s
s
u
b
m
itted
in
q
u
ick
s
u
cc
ess
io
n
,
th
e
d
etec
tio
n
o
f
in
o
r
d
in
ately
h
i
g
h
g
as f
ee
s
o
r
p
r
ec
ip
ito
u
s
g
as p
r
ice
b
id
s
p
ik
es,
an
d
th
e
e
x
am
in
ati
o
n
o
f
s
wap
s
ize
an
d
s
lip
p
ag
e
p
ar
am
eter
s
o
n
tr
a
d
es
o
n
DE
X
S
[
1
6
]
.
T
h
ese
r
u
le
-
b
ased
an
d
r
e
ac
tiv
e
tech
n
iq
u
es
h
av
e
lim
itatio
n
s
b
ec
au
s
e
th
e
y
ar
e
n
o
t
s
u
cc
ess
f
u
l
ag
ain
s
t
c
lev
er
b
o
ts
th
at
ar
e
ab
le
to
d
y
n
am
ically
m
o
d
if
y
t
h
eir
ac
tio
n
s
.
I
n
ad
d
itio
n
,
th
ese
tech
n
iq
u
es
ar
e
lik
ely
to
h
av
e
h
ig
h
f
alse
p
o
s
itiv
e
r
ates
an
d
ar
e
n
o
t
s
u
ited
to
th
e
d
etec
tio
n
o
f
th
e
f
in
e
-
gr
ain
ed
t
em
p
o
r
al
an
d
b
eh
av
io
u
r
al
ch
ar
ac
ter
is
tics
ty
p
ica
lly
d
is
p
lay
ed
b
y
ME
V
b
o
t a
ctiv
it
y
[
1
1
]
,
[
1
7
]
.
2
.
2
.
M
a
chine
lea
rning
-
ba
s
ed
det
ec
t
io
n
ML
m
o
d
els
ar
e
h
ig
h
ly
a
p
t
t
o
o
b
s
er
v
e
co
m
p
licated
s
eq
u
e
n
ce
s
o
f
t
r
an
s
ac
tio
n
s
,
m
o
v
em
en
ts
o
f
g
as
p
r
ices,
an
d
u
s
er
b
e
h
av
io
u
r
al
p
atter
n
s
an
d
,
th
e
r
ef
o
r
e,
ar
e
p
ar
t
icu
lar
ly
s
u
ited
f
o
r
d
etec
tin
g
f
r
o
n
t
-
r
u
n
n
in
g
attac
k
s
in
b
lo
ck
c
h
ain
s
y
s
tem
s
.
Key
f
ea
tu
r
es
lev
er
ag
ed
in
t
h
e
m
o
d
els
ar
e
g
as
p
r
ice
d
elta,
o
r
th
e
d
if
f
er
e
n
tial
o
f
t
h
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
7
7
6
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
,
Vo
l.
1
5
,
No
.
1
,
Ma
r
ch
20
2
6
:
43
8
-
44
6
440
tr
an
s
ac
tio
n
g
as
f
ee
co
m
p
ar
e
d
with
r
ec
en
t
n
etwo
r
k
av
e
r
a
g
es,
in
ter
v
als
in
tr
an
s
ac
tio
n
tim
es,
s
wap
r
ate
s
,
s
lip
p
ag
e
s
ettin
g
s
cr
ea
ted
to
id
en
tify
s
an
d
wich
attac
k
s
,
u
s
e
r
ad
d
r
ess
b
eh
av
io
r
s
s
h
o
win
g
ty
p
ical
p
atter
n
s
o
f
h
is
to
r
ic
tr
an
s
ac
tio
n
s
,
an
d
i
n
ter
ac
tio
n
f
r
e
q
u
en
c
y
with
s
p
ec
if
ic
DeFi
co
n
tr
ac
ts
[
1
]
,
[
1
4
]
,
[
1
8
]
.
T
y
p
ical
ML
ap
p
r
o
ac
h
es
to
d
etec
tin
g
f
r
o
n
t
-
r
u
n
n
in
g
ar
e
L
STM
n
etwo
r
k
s
,
d
em
o
n
s
tr
atin
g
ca
p
a
b
ilit
y
in
s
eq
u
en
tial
tr
an
s
ac
tio
n
p
atter
n
an
d
tem
p
o
r
al
r
elatio
n
s
h
ip
lear
n
in
g
,
I
s
o
latio
n
Fo
r
est
alg
o
r
ith
m
s
,
an
u
n
s
u
p
er
v
i
s
ed
ap
p
r
o
ac
h
,
t
h
at
d
etec
ts
o
u
tlier
s
b
ased
o
n
th
e
u
tili
z
atio
n
o
f
m
u
ltid
im
e
n
s
io
n
al
f
ea
tu
r
e
s
p
ac
e,
s
u
p
er
v
is
ed
l
ea
r
n
in
g
lik
e
r
an
d
o
m
f
o
r
ests
(
R
F)
an
d
s
u
p
p
o
r
t
v
ec
to
r
m
a
ch
in
e
s
(
SVMs)
th
at
r
ely
o
n
lab
elled
d
ata
s
ets
with
r
ec
o
g
n
ized
in
s
tan
ce
s
o
f
attac
k
; a
n
d
DB
SC
AN
clu
s
ter
i
n
g
to
f
i
n
d
clu
s
ter
s
r
ef
lectin
g
b
o
t
-
d
r
iv
e
n
ac
tio
n
o
r
s
p
ik
es in
tr
an
s
ac
tio
n
s
[
1
9
]
.
2
.
3
.
Cha
lleng
es in t
ra
ditio
na
l m
et
ho
ds
Ov
er
th
e
last
f
ew
y
ea
r
s
,
n
u
m
er
o
u
s
to
o
ls
an
d
f
r
am
ewo
r
k
s
h
av
e
co
m
e
in
to
b
ein
g
to
en
h
an
ce
th
e
s
ec
u
r
ity
an
aly
s
is
o
f
E
th
er
e
u
m
s
m
ar
t
co
n
tr
ac
ts
.
T
h
e
t
o
o
ls
u
tili
ze
a
v
ar
iety
o
f
tech
n
iq
u
es
s
u
ch
as
s
y
m
b
o
lic
ex
ec
u
tio
n
,
s
tatic
an
aly
s
is
,
f
u
zz
in
g
,
f
o
r
m
al
v
er
if
icatio
n
,
an
d
ML
to
id
en
tify
p
o
te
n
tial
v
u
l
n
er
ab
ilit
ies.
E
v
er
y
m
eth
o
d
h
as
s
o
m
e
ad
v
an
tag
es
an
d
d
is
ad
v
a
n
tag
es
in
d
etec
ti
o
n
p
o
ten
tial,
ac
cu
r
ac
y
,
a
n
d
s
ca
lab
ilit
y
.
Sy
m
b
o
lic
ex
ec
u
tio
n
-
b
ased
to
o
ls
lik
e
Oy
en
t
e,
My
th
r
il,
an
d
Ma
ian
an
al
y
ze
E
th
er
eu
m
s
m
ar
t
co
n
tr
ac
t
b
y
teco
d
e
to
d
etec
t
lo
g
ical
er
r
o
r
s
an
d
co
m
m
o
n
p
atter
n
s
o
f
wea
k
n
ess
es.
Oy
en
te
was
am
o
n
g
th
e
ea
r
lies
t
to
o
ls
th
at
an
aly
ze
d
E
th
er
eu
m
b
y
teco
d
e
,
f
o
c
u
s
in
g
o
n
f
r
e
q
u
en
t
p
r
o
b
lem
s
in
clu
d
in
g
tim
estam
p
d
ep
e
n
d
e
n
c
y
an
d
r
ee
n
tr
an
cy
.
My
th
r
il
au
g
m
en
ts
s
y
m
b
o
lic
ex
ec
u
tio
n
with
b
etter
d
etec
tio
n
o
f
a
r
ith
m
etic
an
d
r
ee
n
tr
an
c
y
wea
k
n
ess
es
an
d
Ma
ian
is
f
o
cu
s
ed
o
n
tr
ac
e
wea
k
n
ess
es
lik
e
s
u
icid
al
co
n
tr
ac
ts
an
d
i
n
f
in
ite
E
th
er
leak
s
.
Static
co
d
e
an
aly
s
is
to
o
ls
lik
e
Sli
th
er
,
Secu
r
i
f
y
,
an
d
Sm
ar
t
C
h
ec
k
an
aly
s
e
So
lid
ity
co
d
e
with
o
u
t
t
h
e
r
e
q
u
ir
em
e
n
t
o
f
ex
ec
u
tio
n
.
Sli
th
er
is
u
n
iq
u
e
in
t
h
at
it
is
f
ast
to
p
r
o
ce
s
s
an
d
p
r
o
v
id
es
f
u
ll
co
d
e
m
etr
ics,
th
er
eb
y
s
er
v
in
g
as
a
d
e
v
elo
p
er
r
eso
u
r
ce
in
th
e
d
ev
elo
p
m
en
t
p
h
ase.
Secu
r
if
y
em
p
l
o
y
s
p
atter
n
-
m
atch
i
n
g
tech
n
iq
u
es
to
ad
h
e
r
e
to
s
e
cu
r
ity
p
r
o
to
co
ls
f
o
r
co
n
tr
ac
t
p
r
o
p
e
r
ty
c
h
ec
k
s
,
w
h
ile
Sm
ar
tC
h
ec
k
em
p
l
o
y
s
XM
L
tr
an
s
latio
n
t
o
s
ca
n
f
o
r
v
u
l
n
er
ab
ilit
ies
th
r
o
u
g
h
k
n
o
wn
p
atter
n
s
.
Fu
zz
in
g
-
b
ased
tech
n
i
q
u
es
s
u
ch
as
C
o
n
t
r
ac
tFu
zz
er
an
d
Har
v
ey
o
f
f
er
d
y
n
am
ic
an
al
y
s
is
th
r
o
u
g
h
th
e
cr
ea
tio
n
o
f
m
an
y
in
p
u
ts
to
r
ev
ea
l
r
u
n
tim
e
v
u
l
n
er
ab
ilit
ies.
T
ab
le
1
s
h
o
ws
th
e
k
ey
f
ea
tu
r
es
o
f
d
if
f
er
en
t f
r
a
m
ewo
r
k
s
.
T
h
e
s
ev
er
al
ty
p
es o
f
E
th
er
eu
m
s
m
ar
t c
o
n
tr
ac
t a
n
aly
s
is
to
o
ls
th
at
ar
e
f
r
eq
u
en
tly
u
s
ed
f
o
r
s
ec
u
r
ity
au
d
its
an
d
v
u
ln
er
a
b
ilit
y
d
is
co
v
er
y
a
r
e
d
e
p
i
cted
in
Fi
g
u
r
e
1
.
T
ab
le
1
.
Key
f
r
am
ewo
r
k
an
d
f
ea
tu
r
es
S
.
N
o
To
o
l
/
F
r
a
mew
o
r
k
M
e
t
h
o
d
o
l
o
g
y
K
e
y
f
e
a
t
u
r
e
s
1
O
y
e
n
t
e
S
y
mb
o
l
i
c
e
x
e
c
u
t
i
o
n
A
n
a
l
y
s
e
s E
t
h
e
r
e
u
m
b
y
t
e
c
o
d
e
t
o
d
e
t
e
c
t
st
a
n
d
a
r
d
v
u
l
n
e
r
a
b
i
l
i
t
i
e
s
.
2
M
y
t
h
r
i
l
S
y
mb
o
l
i
c
e
x
e
c
u
t
i
o
n
P
o
p
u
l
a
r
o
p
e
n
-
s
o
u
r
c
e
t
o
o
l
f
o
r
d
e
t
e
c
t
i
n
g
r
e
e
n
t
r
a
n
c
y
a
n
d
a
r
i
t
h
me
t
i
c
b
u
g
s
.
3
M
a
i
a
n
S
y
mb
o
l
i
c
e
x
e
c
u
t
i
o
n
F
o
c
u
ses
o
n
i
d
e
n
t
i
f
y
i
n
g
t
r
a
c
e
v
u
l
n
e
r
a
b
i
l
i
t
i
e
s
(
e
.
g
.
,
s
u
i
c
i
d
a
l
c
o
n
t
r
a
c
t
s)
5
S
e
c
u
r
i
f
y
S
t
a
t
i
c
a
n
a
l
y
s
i
s +
P
a
t
t
e
r
n
s
V
e
r
i
f
i
e
s
c
o
mp
l
i
a
n
c
e
w
i
t
h
p
r
e
d
e
f
i
n
e
d
s
a
f
e
t
y
a
n
d
l
i
v
e
n
e
ss
p
r
o
p
e
r
t
i
e
s
.
6
S
l
i
t
h
e
r
S
t
a
t
i
c
a
n
a
l
y
s
i
s
Ef
f
i
c
i
e
n
t
s
o
l
i
d
i
t
y
c
o
d
e
a
n
a
l
y
z
e
r
;
p
r
o
v
i
d
e
s
sec
u
r
i
t
y
i
n
si
g
h
t
s
a
n
d
me
t
r
i
c
s
.
4
S
mart
c
h
e
c
k
S
t
a
t
i
c
a
n
a
l
y
s
i
s
Tr
a
n
s
l
a
t
e
s
s
o
l
i
d
i
t
y
t
o
X
M
L
f
o
r
p
a
t
t
e
r
n
-
b
a
se
d
v
u
l
n
e
r
a
b
i
l
i
t
y
d
e
t
e
c
t
i
o
n
.
7
C
o
n
t
r
a
c
t
f
u
z
z
e
r
F
u
z
z
i
n
g
G
e
n
e
r
a
t
e
s
r
a
n
d
o
m
i
n
p
u
t
s
t
o
d
e
t
e
c
t
r
u
n
t
i
m
e
v
u
l
n
e
r
a
b
i
l
i
t
i
e
s
.
8
H
a
r
v
e
y
G
r
e
y
b
o
x
f
u
z
z
i
n
g
U
ses
g
e
n
e
t
i
c
a
l
g
o
r
i
t
h
ms
f
o
r
sm
a
r
t
e
r
t
e
st
c
a
se
g
e
n
e
r
a
t
i
o
n
.
9
C
o
n
t
r
a
c
t
w
a
r
d
M
a
c
h
i
n
e
l
e
a
r
n
i
n
g
U
ses
f
e
a
t
u
r
e
l
e
a
r
n
i
n
g
t
o
c
l
a
ssi
f
y
c
o
n
t
r
a
c
t
v
u
l
n
e
r
a
b
i
l
i
t
i
e
s
.
10
ESCO
R
T
M
a
c
h
i
n
e
l
e
a
r
n
i
n
g
En
se
mb
l
e
l
e
a
r
n
i
n
g
f
o
r
m
u
l
t
i
c
l
a
s
s
v
u
l
n
e
r
a
b
i
l
i
t
y
c
l
a
ssi
f
i
c
a
t
i
o
n
.
11
ZEU
S
F
o
r
mal
v
e
r
i
f
i
c
a
t
i
o
n
C
o
m
b
i
n
e
s a
b
s
t
r
a
c
t
i
n
t
e
r
p
r
e
t
a
t
i
o
n
a
n
d
mo
d
e
l
c
h
e
c
k
i
n
g
.
Fig
u
r
e
1
.
C
ateg
o
r
y
-
wis
e
E
th
er
eu
m
s
m
ar
t c
o
n
tr
ac
t a
n
aly
s
is
to
o
ls
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
I
SS
N:
2252
-
8
7
7
6
S
ec
u
r
in
g
Defi
:
a
c
o
mp
r
eh
en
s
ive
r
ev
iew
o
f
ML
a
p
p
r
o
a
ch
es fo
r
d
etec
tin
g
…
(
Dh
ivya
l
a
ksh
mi
V
en
ka
tr
a
m
an
)
441
T
h
o
u
g
h
p
r
o
m
is
in
g
,
ML
-
b
as
ed
f
r
o
n
t
-
r
u
n
n
in
g
d
etec
tio
n
m
o
d
els
ar
e
h
am
p
er
e
d
b
y
s
ig
n
if
ican
t
ch
allen
g
es,
m
ain
ly
d
u
e
to
th
e
lim
ited
av
ailab
ilit
y
o
f
ac
cu
r
ately
lab
elled
d
ata
o
win
g
to
th
e
in
s
u
f
f
icien
t
d
o
cu
m
e
n
tatio
n
o
f
i
d
en
tifie
d
a
ttack
s
,
wh
ich
h
in
d
er
s
s
u
p
er
v
i
s
ed
tr
ain
in
g
.
T
h
e
m
o
d
els
also
s
u
f
f
e
r
f
r
o
m
f
alse
p
o
s
itiv
es
s
in
ce
leg
iti
m
ate
u
s
er
s
ca
n
u
n
k
n
o
win
g
l
y
d
is
p
lay
b
o
t
-
lik
e
b
eh
a
v
io
u
r
,
e.
g
.
,
ch
ar
g
in
g
ex
ce
s
s
iv
e
g
as
f
ee
s
d
u
r
in
g
n
etwo
r
k
co
n
g
esti
o
n
.
T
h
e
r
eq
u
i
r
em
en
t
f
o
r
r
ea
l
-
tim
e
in
f
er
en
ce
also
m
ak
es
th
in
g
s
m
o
r
e
d
if
f
ic
u
lt
s
in
c
e
b
lo
ck
ch
ain
tr
an
s
ac
tio
n
s
m
u
s
t
b
e
p
r
o
ce
s
s
ed
with
in
m
illi
s
ec
o
n
d
s
to
av
o
id
p
o
ten
tial
ex
p
lo
itatio
n
ef
f
ec
tiv
ely
.
Ad
d
itio
n
ally
,
h
ig
h
co
m
p
u
tatio
n
al
co
s
ts
in
v
o
lv
ed
in
r
u
n
n
in
g
ML
m
o
d
els
o
n
-
c
h
ain
m
a
k
e
a
s
o
lu
tio
n
in
v
o
l
v
in
g
o
f
f
-
ch
ai
n
in
f
er
en
ce
co
m
b
in
ed
with
o
n
-
ch
ain
tr
ig
g
er
s
n
ec
ess
ar
y
.
Fu
tu
r
e
wo
r
k
m
ay
in
v
o
lv
e
th
e
ap
p
licatio
n
o
f
ML
co
m
b
in
ed
with
s
tr
ea
m
in
g
d
ata
p
ip
elin
es,
f
u
ll
ad
d
r
ess
-
le
v
el
p
r
o
f
ilin
g
,
an
d
o
r
ac
le
-
b
ase
d
p
r
ice
m
o
n
it
o
r
in
g
to
en
h
an
ce
d
etec
tio
n
.
I
n
teg
r
at
io
n
o
f
R
L
tech
n
iq
u
es
m
ay
als
o
en
ab
le
ad
ap
tiv
e
d
ef
en
ce
m
e
ch
an
is
m
s
,
allo
win
g
p
r
o
to
co
ls
to
d
y
n
a
m
ically
r
eo
r
d
er
,
d
elay
,
o
r
r
ejec
t
tr
an
s
ac
tio
n
s
b
ased
o
n
lear
n
ed
b
eh
av
io
u
r
al
k
n
o
wled
g
e
.
3.
E
VA
L
UA
T
I
NG
O
RAC
L
E
T
RUST
WO
RT
H
I
NE
SS
W
I
T
H
M
ACH
I
N
E
L
E
ARN
I
NG
Or
ac
les
ar
e
k
ey
elem
en
ts
o
f
DeFi
s
y
s
tem
s
,
s
er
v
in
g
as
m
id
d
lem
en
th
at
p
r
o
v
id
e
o
f
f
-
ch
ai
n
d
ata
lik
e
ass
et
p
r
ices,
ex
ch
an
g
e
r
ates,
an
d
in
ter
est
r
ates
to
o
n
-
ch
ain
s
m
ar
t
co
n
tr
ac
ts
.
[
4
.
1
]
w
h
ile
th
ey
ar
e
v
al
u
ab
le,
o
r
ac
le
d
ep
e
n
d
en
c
y
in
tr
o
d
u
ce
s
v
u
ln
er
a
b
ilit
ies,
cr
ea
tin
g
ce
n
tr
alis
ed
p
o
in
ts
o
f
f
ailu
r
e
in
d
ec
en
tr
alis
ed
s
y
s
tem
s
.
Ma
n
ip
u
lated
,
d
elay
e
d
,
o
r
er
r
o
n
eo
u
s
d
ata
f
r
o
m
o
r
ac
les
ca
n
in
itiate
in
co
r
r
ec
t
ex
ec
u
tio
n
s
o
f
s
m
ar
t
co
n
tr
ac
ts
,
with
s
ev
er
e
f
in
an
cial
co
n
s
eq
u
en
ce
s
.
T
o
p
r
ev
en
t
th
ese
r
is
k
s
an
d
e
n
h
an
ce
o
r
ac
le
r
eliab
ilit
y
,
r
esear
ch
e
r
s
h
av
e
s
u
g
g
ested
u
s
in
g
ML
m
eth
o
d
s
to
ev
alu
ate
an
d
au
th
en
ticate
t
h
e
cr
ed
ib
ilit
y
o
f
o
r
ac
le
-
p
r
o
v
id
ed
d
ata
in
r
ea
l
-
tim
e
[
4
.
2
]
.
3.
1
.
T
he
pro
blem
wit
h o
ra
cl
es
Sm
ar
t
co
n
tr
ac
t
s
wo
r
k
d
eter
m
in
is
tically
b
ased
en
tire
ly
o
n
ex
ter
n
al
d
ata
s
u
p
p
lied
b
y
th
ir
d
-
p
ar
ty
s
o
u
r
ce
s
ca
lled
o
r
ac
les,
as
th
e
y
ca
n
n
o
t
ac
ce
s
s
o
f
f
-
ch
ain
d
ata
d
ir
ec
tly
.
Ho
we
v
er
,
o
r
ac
les
ca
n
ad
d
v
u
ln
er
ab
ilit
ies
to
s
m
ar
t
co
n
tr
ac
ts
i
f
p
o
o
r
l
y
d
e
v
elo
p
ed
o
r
co
m
p
r
o
m
is
ed
,
o
r
a
s
a
r
esu
lt
o
f
an
y
co
n
tr
ac
t
b
ein
g
ac
tiv
ely
tam
p
er
ed
with
o
u
ts
id
e
o
f
th
e
s
m
ar
t
c
o
n
t
r
ac
t.
So
m
e
ex
am
p
les
o
f
th
ese
f
laws
ca
n
in
clu
d
e,
b
u
t
s
h
o
u
l
d
n
o
t
b
e
lim
ited
t
o
,
p
r
ice
f
ee
d
m
an
ip
u
latio
n
th
r
o
u
g
h
lo
w
-
liq
u
i
d
ity
p
o
o
ls
,
d
el
ay
v
ia
d
a
ta
u
p
d
ates
f
ee
d
in
g
o
u
td
ated
d
ata
to
co
n
tr
ac
ts
,
o
r
ac
le
ce
n
tr
alis
ed
ab
ilit
y
er
o
d
in
g
ac
ce
s
s
to
ex
t
er
n
al
d
ata
r
esu
ltin
g
in
a
s
in
g
le
p
o
in
t
o
f
f
ailu
r
e,
in
clu
d
in
g
allo
ca
te
d
co
llu
s
io
n
o
r
Sy
b
il
attac
k
s
b
y
m
u
ltip
le
o
r
ac
le
n
o
d
es.
A
p
o
ig
n
an
t
ex
am
p
le
o
f
th
ese
r
i
s
k
s
is
th
e
b
Z
x
p
r
o
to
co
l
h
ac
k
o
f
2
0
2
0
,
wh
er
e
attac
k
er
s
in
itially
m
a
n
ip
u
lated
th
e
p
r
ice
o
r
ac
le,
a
n
d
to
o
k
ad
v
a
n
tag
e
o
f
th
at
p
r
ice
o
r
ac
le
b
ased
o
n
th
at
m
an
ip
u
latio
n
to
ex
tr
ac
t
f
u
n
d
s
f
r
o
m
a
co
n
tr
ac
t
b
y
way
o
f
an
u
n
d
er
c
o
llater
alize
d
lo
an
-
s
h
o
wca
s
in
g
th
e
wid
er
o
b
s
er
v
atio
n
o
f
h
o
w
th
e
e
n
tire
DeFi
ec
o
s
y
s
tem
s
u
f
f
er
s
f
r
o
m
th
e
s
am
e
v
u
ln
er
ab
ilit
ies in
th
e
v
u
ln
er
a
b
ilit
ies o
f
len
d
in
g
p
latf
o
r
m
s
,
p
r
e
d
ictio
n
m
ar
k
ets,
a
n
d
s
y
n
th
etic
ass
et
s
er
v
ices.
3
.
2
.
T
ra
ditio
na
l
m
it
ig
a
t
i
o
n t
ec
hn
iq
ues
T
o
d
ef
en
d
a
g
ain
s
t
o
r
ac
le
r
is
k
s
,
s
ev
er
al
DeFi
p
r
o
to
co
ls
p
r
o
ac
tiv
ely
im
p
lem
en
t
v
ar
i
o
u
s
d
ef
en
s
iv
e
m
ea
s
u
r
es.
T
h
ese
r
is
k
-
m
in
im
izin
g
d
ef
en
s
e
s
tr
ateg
ies
ca
n
em
p
lo
y
m
eth
o
d
s
s
u
ch
as
d
ata
ag
g
r
eg
atio
n
,
wh
ich
co
llects
m
u
ltip
le
d
ata
s
o
u
r
ce
s
to
g
eth
er
to
m
itig
ate
th
e
ef
f
ec
t
o
f
a
s
in
g
le
co
m
p
r
o
m
is
ed
in
p
u
t;
tim
e
-
weig
h
te
d
av
er
ag
e
p
r
ices
(
T
W
APs
)
,
wh
i
ch
av
er
ag
e
i
n
co
m
in
g
p
r
ices
o
v
er
a
ce
r
tain
p
er
io
d
t
o
s
m
o
o
th
o
u
t
an
y
s
h
o
r
t
-
ter
m
v
o
latilit
y
;
an
d
d
elay
p
r
o
to
co
ls
,
wh
ich
ca
n
d
elay
ex
ec
u
tin
g
a
n
o
r
d
e
r
u
n
til
a
p
r
eset
tim
e
h
a
s
p
ass
ed
,
allo
win
g
ac
to
r
s
o
n
th
e
p
r
o
to
c
o
l
to
m
an
u
all
y
in
ter
ac
t
wh
en
th
e
r
e
p
o
r
te
d
p
r
ices
h
av
e
d
r
am
atica
lly
ch
an
g
ed
.
E
v
e
n
if
th
ese
m
ea
s
u
r
es
co
n
tr
i
b
u
te
t
o
in
c
r
ea
s
in
g
p
r
o
to
co
l
r
esil
ien
ce
,
th
e
y
ar
e
p
ass
iv
e
m
ea
s
u
r
es
th
at
d
o
n
o
t
cr
ea
tiv
el
y
s
ee
k
o
u
t
n
ew,
o
r
less
o
b
v
io
u
s
,
n
eu
t
r
al
th
r
ea
ts
to
th
e
p
r
o
to
co
l.
T
h
e
s
e
m
ea
s
u
r
es
als
o
ca
n
n
o
t
a
d
ju
s
t
d
y
n
am
ically
to
tr
y
t
o
ev
alu
ate
th
e
tr
u
s
two
r
th
in
es
s
o
f
m
u
ltip
le
o
r
ac
les
in
th
e
m
ar
k
etp
lace
,
an
d
th
ey
p
r
o
b
a
b
ly
ca
n
n
o
t
r
esp
o
n
d
to
m
o
r
e
s
u
b
tle
d
e
v
iatio
n
s
in
h
o
w
th
e
d
ata
f
lo
w
h
as p
atter
n
s
h
if
t
ed
o
v
er
tim
e.
3
.
3
.
ML
-
ba
s
ed
t
rus
t
ev
a
lua
t
io
n m
o
del
s
ML
is
p
ar
ticu
lar
ly
ad
v
an
tag
e
o
u
s
f
o
r
im
p
r
o
v
i
n
g
o
r
ac
le
s
ec
u
r
ity
,
b
y
h
av
in
g
r
ea
l
-
tim
e
m
o
n
ito
r
in
g
o
f
th
e
o
r
ac
le
d
ata
f
ee
d
an
d
id
e
n
tify
in
g
a
p
atter
n
th
at
is
n
o
t
t
y
p
ical.
I
f
we
th
in
k
a
b
o
u
t
o
r
a
cle
o
u
tp
u
ts
as
d
ata
s
tr
ea
m
s
,
we
ca
n
u
s
e
ML
m
o
d
e
ls
to
id
en
tify
a
n
o
m
al
ies,
ass
ess
r
eliab
ilit
y
,
esti
m
ate
li
k
ely
f
u
tu
r
e
b
eh
a
v
io
u
r
,
an
d
co
m
p
ar
e
it
all
ac
r
o
s
s
m
a
n
y
o
r
ac
les.
So
m
e
o
f
th
e
r
elev
a
n
t
f
ea
tu
r
es
th
ese
m
o
d
els
will
ty
p
ically
u
s
e
ar
e:
d
ev
iatio
n
s
f
r
o
m
ex
p
ec
te
d
p
r
ic
es,
h
o
w
o
f
ten
an
d
h
o
w
q
u
ick
l
y
o
r
ac
les
r
ef
r
esh
th
eir
d
a
ta;
m
ea
s
u
r
es
o
f
v
o
latilit
y
;
an
y
co
r
r
elatio
n
s
tr
u
ctu
r
es
ac
r
o
s
s
th
e
o
r
ac
les;
an
d
s
o
m
e
h
is
to
r
ical
ac
cu
r
ac
y
m
etr
ics.
So
m
e
o
f
th
e
ML
tech
n
iq
u
es
we
wo
u
ld
co
n
s
id
er
in
clu
d
e:
L
STM
n
etwo
r
k
s
to
id
en
tify
an
o
m
alo
u
s
co
n
d
itio
n
s
an
d
f
u
tu
r
e
p
r
ice
esti
m
ates
f
r
o
m
tim
e
-
s
er
ies
d
ata;
au
to
en
c
o
d
er
s
as
u
n
s
u
p
er
v
is
ed
m
o
d
els
wh
ich
co
u
ld
id
en
tify
p
o
ten
tial
m
an
ip
u
latio
n
s
,
th
r
o
u
g
h
r
ec
o
n
s
tr
u
ctio
n
er
r
o
r
o
f
u
n
u
s
u
ally
h
ig
h
v
al
u
es;
an
o
m
aly
d
etec
tio
n
m
o
d
els,
s
u
ch
as
is
o
latio
n
f
o
r
est
an
d
DB
S
C
A
N,
to
id
en
tify
u
n
u
s
u
al
d
is
tr
ib
u
tio
n
s
o
f
d
ata
an
d
er
r
atic
b
e
h
av
io
u
r
p
atter
n
s
;
an
d
r
ein
f
o
r
ce
m
e
n
t
lear
n
i
n
g
(
R
L
)
wh
ich
r
ef
lects
a
tr
u
s
t
s
co
r
e
b
ased
r
ewa
r
d
,
d
e
d
u
ctin
g
o
r
ad
d
in
g
tr
u
s
t
s
co
r
es
d
y
n
am
ically
to
o
r
ac
les
with
ea
ch
tr
an
s
ac
tio
n
,
lettin
g
p
er
f
o
r
m
an
ce
g
iv
e
r
ewa
r
d
o
r
p
e
n
alty
f
e
ed
b
ac
k
s
ig
n
als
f
o
r
f
u
tu
r
e
lear
n
i
ng.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
7
7
6
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
,
Vo
l.
1
5
,
No
.
1
,
Ma
r
ch
20
2
6
:
43
8
-
44
6
442
4.
I
NT
E
G
RA
T
E
D
M
L
-
B
AS
E
D
DE
F
I
S
E
CUR
I
T
Y
F
R
AM
E
WO
RK
T
h
e
ev
e
r
-
m
u
tatin
g
a
n
d
co
m
p
lex
r
is
k
s
th
at
ar
e
to
b
e
m
iti
g
ated
in
DeFi
d
em
a
n
d
a
c
o
h
esiv
e
an
d
in
tellig
en
t
s
ec
u
r
ity
en
v
ir
o
n
m
en
t
th
at
o
b
s
er
v
es
n
o
t
o
n
ly
s
m
ar
t
co
n
tr
ac
t
b
eh
a
v
io
u
r
b
u
t
also
tr
an
s
ac
tio
n
d
y
n
am
ics,
with
o
f
f
-
ch
a
in
d
at
a
in
p
u
ts
b
ei
n
g
e
v
alu
ated
as
well.
T
h
is
s
ec
tio
n
d
escr
ib
es
t
h
e
lin
in
g
o
f
a
n
o
v
el
ML
-
d
r
iv
en
f
r
am
ewo
r
k
to
wea
v
e
to
g
eth
e
r
th
r
ee
c
o
r
e
la
y
er
s
o
f
DeFi
s
ec
u
r
ity
:
f
r
o
n
t
-
r
u
n
n
in
g
attac
k
d
etec
tio
n
,
o
r
ac
le
-
m
ar
k
et
ass
ess
m
en
t,
an
d
s
m
ar
t
co
n
tr
ac
t
v
u
ln
er
a
b
ilit
y
e
v
alu
a
tio
n
s
.
E
ac
h
lay
er
,
u
s
in
g
ML
m
o
d
els
t
r
ain
ed
f
o
r
its
o
wn
s
et
o
f
in
p
u
ts
lik
e
u
s
er
tr
an
s
ac
tio
n
p
atter
n
s
,
o
r
a
cle
d
ata
s
tr
ea
m
s
,
o
r
s
m
ar
t
co
n
tr
ac
t
o
p
co
d
es
an
d
lo
g
ic
s
tr
u
ctu
r
es,
aim
s
at
an
o
th
er
ty
p
e
o
f
attac
k
th
at
is
cr
u
cial
.
B
ein
g
m
o
d
u
lar
an
d
s
ca
lab
le,
th
e
f
r
am
ew
o
r
k
p
r
o
p
o
s
ed
h
e
r
e
en
s
u
r
es
th
at
ea
c
h
s
ec
u
r
ity
lay
er
ca
n
o
p
er
ate
in
d
ep
en
d
en
tly
wh
ile
f
ee
d
in
g
i
n
to
a
co
n
s
o
lid
ate
d
f
r
a
m
ewo
r
k
f
o
r
d
ec
is
io
n
-
m
a
k
in
g
.
Su
ch
a
c
o
n
s
o
lid
ated
ar
ch
itectu
r
e
en
a
b
les
p
r
o
ac
tiv
e
th
r
ea
t
d
etec
tio
n
,
r
ea
l
-
tim
e
r
i
s
k
aler
ts
,
an
d
in
tellig
en
t
r
is
k
s
co
r
in
g
ac
r
o
s
s
th
e
en
tire
p
ip
elin
e
o
f
DeFi
tr
an
s
ac
tio
n
s
.
T
h
e
s
y
s
tem
co
n
s
o
lid
ates
th
e
co
r
r
elatio
n
o
f
in
s
ig
h
ts
f
r
o
m
m
an
y
s
o
u
r
ce
s
,
wh
ich
en
h
a
n
ce
s
ea
ch
d
etec
ti
o
n
m
ec
h
an
is
m
wh
ile
r
ed
u
cin
g
f
alse
alar
m
s
a
n
d
p
r
o
v
id
in
g
a
m
o
r
e
c
o
m
p
lete
p
ictu
r
e
o
f
p
o
ten
tial th
r
ea
ts
.
F
u
r
th
er
m
o
r
e
,
it o
f
f
er
s
g
r
ea
t in
te
g
r
atio
n
p
o
s
s
ib
ilit
ies with
an
y
DeFi
p
latf
o
r
m
d
u
e
to
th
e
co
m
m
o
n
a
r
ch
itectu
r
al
b
ase,
s
u
p
p
o
r
tin
g
ad
ap
tiv
e
r
esp
o
n
s
es
s
u
ch
as
tr
an
s
ac
tio
n
d
ela
y
s
,
ac
ce
s
s
r
estrictio
n
s
,
o
r
au
t
o
m
ated
au
d
its
f
r
o
m
th
e
s
ec
u
r
ity
r
atin
g
in
r
ea
l
-
ti
m
e.
T
h
i
s
co
m
p
lete,
ML
-
aid
ed
ap
p
r
o
ac
h
will
n
o
t
o
n
l
y
f
ill
th
e
g
ap
s
p
o
s
ed
b
y
way
o
f
is
o
lated
d
etec
tio
n
m
ec
h
an
is
m
s
b
u
t
wo
u
ld
also
ev
o
lv
e
in
to
a
n
in
tellig
en
t,
co
n
tex
t
-
awa
r
e
d
ef
en
ce
m
ec
h
an
is
m
th
at
ad
ap
ts
p
r
o
m
p
tly
to
th
e
d
y
n
am
ic
n
atu
r
e
o
f
th
e
DeFi
wo
r
ld
.
T
h
r
o
u
g
h
th
ese
i
n
ter
wo
v
en
,
m
u
lti
-
lay
er
ed
M
L
tech
n
iq
u
es,
th
e
f
r
am
ewo
r
k
wo
u
ld
s
ec
u
r
e
d
ec
en
t
r
alis
ed
f
in
an
cial
s
y
s
tem
s
b
etter
,
g
iv
in
g
th
em
a
d
u
r
a
b
le,
tr
u
s
two
r
th
y
f
o
u
n
d
atio
n
.
4
.
1
.
Arc
hite
ct
ure
o
v
er
v
iew
T
h
e
f
r
am
ewo
r
k
p
r
o
p
o
s
ed
is
ca
p
ab
le
o
f
th
r
ee
s
ep
ar
ate
an
d
y
et
co
m
p
le
m
en
tar
y
ML
-
b
ase
d
s
ec
u
r
ity
lay
er
s
,
with
ea
ch
s
er
v
in
g
to
a
d
d
r
ess
a
co
r
e
asp
ec
t
o
f
DeFi
p
r
o
to
co
l
p
r
o
tectio
n
.
T
h
e
f
r
o
n
t
-
r
u
n
n
i
n
g
d
etec
tio
n
lay
er
ex
is
ts
to
m
o
n
ito
r
m
em
p
o
o
l
ac
tiv
ity
an
d
tr
an
s
ac
tio
n
f
l
o
w
b
eh
av
io
u
r
s
in
r
ea
l
tim
e.
Usi
n
g
ML
tech
n
iq
u
es,
th
is
lay
er
id
e
n
tifie
s
p
atter
n
s
wh
er
e
f
r
o
n
t
-
r
u
n
n
in
g
attac
k
s
m
ay
o
cc
u
r
.
B
y
m
o
n
ito
r
i
n
g
g
as
p
r
ice
an
o
m
alies,
tr
an
s
ac
tio
n
s
eq
u
en
cin
g
,
a
n
d
b
eh
av
io
u
r
,
th
is
lay
er
in
ten
d
s
to
f
lag
s
u
s
p
ec
t
tr
an
s
ac
tio
n
s
ev
en
b
ef
o
r
e
th
e
y
m
ak
e
th
eir
way
o
n
to
th
e
c
h
ain
.
T
h
e
s
ec
o
n
d
lay
er
is
ca
lled
th
e
o
r
ac
le
tr
u
s
t
ev
alu
atio
n
lay
er
.
I
t
ass
ess
es
th
e
tr
u
s
two
r
th
in
ess
o
f
o
f
f
-
ch
ain
d
ata
s
o
u
r
ce
s
th
at
s
u
p
p
l
y
v
ital
in
f
o
r
m
atio
n
s
u
ch
as
to
k
en
p
r
ices
an
d
ex
ch
a
n
g
e
r
ates
in
to
s
m
ar
t
co
n
tr
ac
ts
.
On
th
e
o
th
er
h
a
n
d
,
th
is
lay
er
ev
alu
ates
an
d
s
co
r
es
o
r
a
cle
tr
u
s
two
r
th
in
ess
d
y
n
am
ic
ally
with
an
o
m
aly
d
e
tectio
n
alg
o
r
ith
m
s
,
tim
e
-
s
er
ies
an
aly
s
is
,
RL
,
an
d
m
o
r
e
in
o
r
d
er
to
d
is
cr
ed
it
o
r
p
r
ev
en
t
ex
p
lo
its
ca
u
s
ed
b
y
l
ate,
m
an
ip
u
lated
,
o
r
i
n
co
r
r
ec
t
d
ata.
T
h
ir
d
ly
,
th
e
s
m
ar
t
co
n
tr
ac
t
v
u
l
n
er
ab
ilit
y
d
etec
tio
n
lay
er
u
s
es
g
r
ap
h
-
b
a
s
ed
m
o
d
els
s
u
ch
as
G
NN
s
an
d
s
em
an
tic
an
aly
s
is
m
o
d
els
s
u
c
h
as
tr
an
s
f
o
r
m
er
s
f
o
r
t
h
e
v
er
if
icatio
n
o
f
s
m
ar
t
c
o
n
tr
ac
t
c
o
d
es
ag
ai
n
s
t
lo
g
ic
er
r
o
r
s
,
u
n
s
af
e
f
u
n
ctio
n
in
ter
ac
tio
n
s
,
an
d
o
th
er
well
-
k
n
o
wn
v
u
ln
e
r
ab
ilit
y
p
atter
n
s
s
u
ch
as
r
ee
n
tr
a
n
cy
o
r
ac
ce
s
s
co
n
tr
o
l
f
laws.
ML
d
r
iv
e
n
s
ec
u
r
it
y
f
r
a
m
ewo
r
k
is
s
h
o
wn
in
Fig
u
r
e
2
.
Fig
u
r
e
2
.
ML
-
d
r
iv
e
n
s
ec
u
r
ity
f
r
am
ewo
r
k
f
o
r
DeFi
On
e
g
o
o
d
p
r
o
p
er
ty
o
f
a
s
ec
u
r
it
y
f
r
am
ewo
r
k
is
th
at
it a
llo
ws a
ll lay
er
s
to
f
u
n
ctio
n
s
em
i
-
in
d
e
p
en
d
en
tly
s
o
th
at
it su
p
p
o
r
ts
m
o
d
u
lar
tr
ain
in
g
,
test
in
g
,
an
d
d
ep
lo
y
m
en
t.
T
h
e
m
o
d
u
lar
ity
asp
ec
t o
f
th
e
f
r
am
ewo
r
k
g
iv
es it
f
lex
ib
ilit
y
,
wh
er
e
b
y
d
ev
elo
p
e
r
s
ca
n
tak
e
o
n
e
o
r
m
o
r
e
lay
e
r
s
as
th
e
ca
s
e
d
em
a
n
d
s
f
o
r
a
p
ar
ticu
lar
s
ec
u
r
ity
b
r
ea
ch
with
o
u
t
n
ec
ess
itatin
g
an
o
v
er
h
au
l
o
f
ex
is
tin
g
in
f
r
astru
ctu
r
e.
B
u
t
to
g
eth
e
r
,
th
ese
lay
er
s
f
o
r
g
e
a
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
I
SS
N:
2252
-
8
7
7
6
S
ec
u
r
in
g
Defi
:
a
c
o
mp
r
eh
en
s
ive
r
ev
iew
o
f
ML
a
p
p
r
o
a
ch
es fo
r
d
etec
tin
g
…
(
Dh
ivya
l
a
ksh
mi
V
en
ka
tr
a
m
an
)
443
s
ea
m
less
an
d
in
tellig
en
t
s
ec
u
r
ity
ec
o
s
y
s
tem
d
eliv
er
in
g
a
co
m
p
r
eh
en
s
ib
le
-
lev
el
p
r
o
tectio
n
-
al
l
th
e
way
th
r
o
u
g
h
th
e
DeFi
s
tack
.
Me
a
n
wh
ile,
th
is
s
h
ar
ed
a
r
ch
itectu
r
e
en
ab
les
co
m
m
u
n
icatio
n
a
n
d
d
ec
is
io
n
-
m
ak
in
g
ac
r
o
s
s
lay
er
s
,
th
u
s
b
etter
co
r
r
elatin
g
th
r
ea
ts
an
d
r
e
ac
tin
g
ad
a
p
tiv
ely
.
As
it
in
teg
r
ates
b
eh
av
i
o
r
al
an
aly
s
is
,
d
ata
in
teg
r
ity
ass
ess
m
en
t,
an
d
co
d
e
an
aly
s
is
,
th
is
f
r
am
ewo
r
k
will
p
r
o
v
id
e
a
r
o
b
u
s
t a
n
d
s
ca
lab
le
s
o
lu
tio
n
to
s
o
m
e
o
f
th
e
v
ar
i
o
u
s
s
ec
u
r
ity
p
r
o
b
lem
s
af
f
ec
tin
g
DeFi
.
T
h
u
s
,
th
e
f
r
a
m
ewo
r
k
eq
u
ip
s
DeF
i
p
r
o
to
co
l
s
with
th
e
ab
ilit
y
to
p
r
o
ac
tiv
ely
d
ef
e
n
d
ag
ain
s
t
s
o
p
h
is
ticated
attac
k
s
wh
ile
en
s
u
r
in
g
th
e
tr
an
s
p
ar
en
c
y
,
co
m
p
o
s
ab
ilit
y
,
an
d
d
ec
en
tr
aliza
tio
n
th
at
c
h
ar
ac
ter
ize
th
e
en
tire
ec
o
s
y
s
tem
.
T
ab
l
e
2
s
h
o
ws th
e
in
teg
r
ate
d
f
r
am
e
wo
r
k
.
T
ab
le
2
.
I
n
teg
r
ated
f
r
am
ewo
r
k
L
a
y
e
r
P
r
i
mary
f
u
n
c
t
i
o
n
M
L
m
o
d
e
l
s
u
se
d
I
n
p
u
t
d
a
t
a
s
o
u
r
c
e
s
Ex
p
e
c
t
e
d
o
u
t
p
u
t
F
r
o
n
t
-
r
u
n
n
i
n
g
d
e
t
e
c
t
i
o
n
l
a
y
e
r
D
e
t
e
c
t
f
r
o
n
t
-
r
u
n
n
i
n
g
b
y
a
n
a
l
y
si
n
g
t
r
a
n
s
a
c
t
i
o
n
b
e
h
a
v
i
o
r
s
LSTM
,
i
s
o
l
a
t
i
o
n
f
o
r
e
st
M
e
m
p
o
o
l
t
r
a
n
s
a
c
t
i
o
n
d
a
t
a
,
g
a
s
p
r
i
c
e
f
l
u
c
t
u
a
t
i
o
n
s,
sl
i
p
p
a
g
e
r
a
t
e
s
Re
al
-
t
i
me
a
l
e
r
t
s
o
n
su
sp
i
c
i
o
u
s
t
r
a
n
s
a
c
t
i
o
n
p
a
t
t
e
r
n
s
O
r
a
c
l
e
t
r
u
st
e
v
a
l
u
a
t
i
o
n
l
a
y
e
r
Ev
a
l
u
a
t
e
t
r
u
s
t
w
o
r
t
h
i
n
e
ss a
n
d
i
n
t
e
g
r
i
t
y
o
f
o
r
a
c
l
e
d
a
t
a
LSTM
,
R
L
O
r
a
c
l
e
p
r
i
c
e
f
e
e
d
s,
u
p
d
a
t
e
f
r
e
q
u
e
n
c
y
,
v
o
l
a
t
i
l
i
t
y
met
r
i
c
s
D
y
n
a
mi
c
o
r
a
c
l
e
t
r
u
s
t
sco
r
e
s
a
n
d
a
n
o
ma
l
y
d
e
t
e
c
t
i
o
n
S
mar
t
c
o
n
t
r
a
c
t
v
u
l
n
e
r
a
b
i
l
i
t
y
d
e
t
e
c
t
i
o
n
l
a
y
e
r
A
n
a
l
y
z
e
sm
a
r
t
c
o
n
t
r
a
c
t
c
o
d
e
f
o
r
l
o
g
i
c
f
l
a
w
s
a
n
d
k
n
o
w
n
v
u
l
n
e
r
a
b
i
l
i
t
i
e
s
G
N
N
,
C
o
d
e
B
E
R
T
(
t
r
a
n
sf
o
r
mer)
S
mart
c
o
n
t
r
a
c
t
b
y
t
e
c
o
d
e
,
A
b
st
r
a
c
t
S
y
n
t
a
x
Tr
e
e
s
(
A
S
T)
,
S
l
i
t
h
e
r
st
a
t
i
c
a
n
a
l
y
si
s
o
u
t
p
u
t
s
C
l
a
s
si
f
i
c
a
t
i
o
n
o
f
v
u
l
n
e
r
a
b
i
l
i
t
i
e
s a
n
d
l
o
g
i
c
f
l
a
w
i
n
s
i
g
h
t
s
4
.
1
.
1
.
L
a
y
er
1
:
f
ro
nt
-
runn
ing
det
ec
t
io
n
T
h
e
f
r
o
n
t
-
r
u
n
n
in
g
d
etec
tio
n
l
ay
er
aim
s
at
th
e
d
is
ce
r
n
in
g
er
r
an
t
ac
tiv
ities
in
th
e
E
th
er
eu
m
m
em
p
o
o
l
with
atten
tio
n
to
f
r
o
n
t
-
r
u
n
n
in
g
h
eu
r
is
tic
-
b
ased
attac
k
s
.
Featu
r
es
lik
e
g
as
p
r
ice
in
cr
ea
s
e,
s
l
i
p
p
ag
e,
tr
a
n
s
ac
tio
n
tim
in
g
s
,
an
d
o
th
e
r
s
ar
e
an
aly
ze
d
u
s
in
g
L
STM
n
etwo
r
k
s
a
n
d
I
s
o
latio
n
Fo
r
ests
to
f
lag
s
u
s
p
icio
u
s
b
eh
a
v
io
r
.
I
t
s
tu
d
ies
f
lo
w
o
f
tr
an
s
ac
tio
n
s
o
v
er
tim
e
to
i
d
en
tify
ce
r
tain
p
atter
n
s
,
allo
win
g
f
o
r
d
etec
tio
n
o
f
ME
V
ex
p
l
o
its
.
I
t is aim
ed
a
t in
iti
atin
g
m
itig
atio
n
s
tr
ate
g
ies wh
ich
will m
in
im
ize
d
am
ag
e
b
ef
o
r
e
a
tr
an
s
ac
tio
n
is
co
m
m
itted
t
o
th
e
b
lo
ck
c
h
ain
.
4
.
1
.
2
.
L
a
y
er
2
:
o
ra
cle
t
rus
t
ev
a
lua
t
io
n
T
h
e
o
r
ac
le
tr
u
s
t
ev
alu
atio
n
la
y
er
s
ca
n
s
o
f
f
-
ch
ai
n
d
ata
s
o
u
r
c
es
th
at
ar
e
p
er
tin
en
t
f
o
r
th
e
o
p
er
atio
n
o
f
DeFi
p
r
o
to
co
ls
.
As
DeFi
p
r
o
t
o
co
ls
in
teg
r
ate
o
r
ac
les
to
f
etch
ass
et
p
r
icin
g
d
ata,
an
y
o
b
s
tr
u
ctio
n
o
r
alter
ca
tio
n
co
u
ld
lead
to
th
e
im
p
r
o
p
er
e
x
e
cu
tio
n
o
f
s
m
ar
t
co
n
tr
ac
ts
.
T
h
is
lay
er
im
p
lem
e
n
ts
tim
e
-
s
er
ies
an
o
m
aly
d
etec
tio
n
u
s
in
g
L
STM
m
o
d
els
an
d
s
co
r
es
o
r
a
cles
b
a
s
ed
o
n
R
L
p
o
licies
o
n
t
h
eir
h
is
to
r
ical
p
er
f
o
r
m
an
ce
.
T
h
is
lay
er
g
u
ar
an
tees th
at
DeFi
s
y
s
tem
s
f
u
n
ctio
n
o
n
r
eliab
le
d
ata
b
y
s
cr
u
tin
izin
g
d
ata
f
e
ed
s
f
o
r
co
n
tr
a
d
ictio
n
s
.
4
.
1
.
3
.
L
a
y
er
3
:
s
m
a
rt
co
ntr
a
ct
v
uln
er
a
bil
it
y
det
ec
t
io
n
T
h
is
lay
er
an
aly
s
es
s
m
ar
t
co
n
tr
ac
t
co
d
e
to
id
en
tify
v
u
ln
e
r
ab
ilit
ies
an
d
lo
g
ic
b
u
g
s
u
s
in
g
th
e
s
m
ar
t
co
n
tr
ac
t
v
u
ln
e
r
ab
ilit
y
d
etec
tio
n
lay
er
.
I
t
ap
p
lies
GNNs
to
an
aly
s
e
co
n
tr
o
l
f
lo
w
an
d
s
em
an
t
ic
s
tr
u
ctu
r
es,
wh
ile
tr
an
s
f
o
r
m
er
m
o
d
els
lik
e
“Co
d
eb
er
t”
in
ter
p
r
et
“th
e
in
ten
t
an
d
b
e
h
av
io
u
r
o
f
th
e
co
d
e
.
”
T
h
is
lay
er
also
in
c
o
r
p
o
r
ates
b
y
teco
d
e
an
d
ASTs
alo
n
g
s
id
e
o
u
tp
u
ts
f
r
o
m
s
tatic
an
aly
s
i
s
to
o
ls
lik
e
Sl
ith
er
,
en
ab
lin
g
th
e
d
etec
tio
n
o
f
s
ec
u
r
ity
v
u
ln
er
ab
ilit
ies
lik
e
r
ee
n
tr
an
c
y
,
ac
c
ess
co
n
tr
o
l
v
u
ln
er
a
b
ilit
ies,
an
d
lo
g
ical
f
laws.
T
h
is
au
to
m
ated
ass
e
s
s
m
en
t
in
cr
ea
s
es th
e
r
eliab
ilit
y
o
f
co
n
tr
ac
ts
b
ef
o
r
e
an
d
af
ter
d
ep
lo
y
m
en
t.
5.
DATAS
E
T
S
AND
T
O
O
L
S F
O
R
M
ACH
I
N
E
L
E
ARN
I
N
G
I
N
DE
F
I
S
E
CU
RI
T
Y
T
h
e
m
o
s
t
wid
ely
u
s
ed
is
Sm
a
r
tB
u
g
s
,
a
b
en
ch
m
ar
k
in
g
d
ataset
o
f
h
u
n
d
r
ed
s
o
f
h
an
d
-
lab
ele
d
co
n
tr
ac
ts
f
o
r
s
o
m
e
class
es
o
f
v
u
ln
er
ab
ili
ti
es,
b
est
u
tili
ze
d
f
o
r
c
o
m
p
ar
at
iv
e
ass
ess
m
en
t
o
f
ML
m
o
d
els an
d
s
tatic
an
aly
s
is
to
o
ls
.
E
th
er
n
a
u
t,
d
e
v
elo
p
ed
b
y
Op
en
Z
e
p
p
elin
,
p
r
o
v
id
es
a
g
am
if
ied
f
r
am
ewo
r
k
with
v
u
ln
er
ab
le
co
n
tr
ac
ts
,
h
en
ce
v
er
y
well
-
s
u
ited
f
o
r
b
eh
av
io
r
al
m
o
d
elin
g
an
d
s
im
u
latio
n
o
f
ex
p
l
o
itativ
e
attac
k
s
.
T
h
e
Ho
n
e
y
B
ad
g
er
d
ataset
is
in
ten
d
ed
f
o
r
h
o
n
e
y
p
o
t
co
n
tr
ac
ts
—
s
u
s
p
icio
u
s
s
m
ar
t
co
n
tr
ac
ts
in
ten
d
ed
to
c
atch
attac
k
er
s
—
s
o
m
o
d
els
lear
n
ab
o
u
t
a
d
v
er
s
ar
ial
b
eh
a
v
io
r
an
d
ca
tch
s
ca
m
s
.
R
E
KT
.
New
s
also
p
r
o
v
id
es
d
etai
led
p
o
s
tm
o
r
tem
s
o
f
ac
tu
al
DeFi
atta
ck
s
;
wh
ile
n
o
t
an
o
f
f
icial
d
at
aset
lab
el,
th
ese
ca
n
b
e
co
n
v
er
ted
to
lab
eled
s
a
m
p
les
f
o
r
tr
ain
i
n
g
.
Alto
g
eth
er
,
th
ese
r
eso
u
r
ce
s
aid
th
e
c
o
n
s
tr
u
ctio
n
o
f
ML
m
o
d
els
f
o
r
im
p
r
o
v
in
g
s
m
ar
t
co
n
tr
ac
t
s
ec
u
r
ity
.
T
ab
le
3
-
5
s
h
o
ws
th
e
v
ar
io
u
s
b
en
ch
m
ar
k
d
atasets
.
T
h
er
e
a
r
e
n
'
t
m
an
y
p
u
b
lic
d
ata
s
ets
th
at
m
er
ely
r
ec
o
r
d
h
o
w
well
o
r
ac
les
d
o
.
T
h
er
e
ar
e
s
o
m
e
p
latf
o
r
m
s
,
th
o
u
g
h
,
t
h
at
r
ev
ea
l
p
ast
f
ee
d
d
ata
th
at
ca
n
b
e
u
s
ed
to
tr
ain
m
o
d
els
to
r
ec
o
g
n
ize
p
r
o
b
lem
s
o
r
a
n
o
m
alies.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
7
7
6
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
,
Vo
l.
1
5
,
No
.
1
,
Ma
r
ch
20
2
6
:
43
8
-
44
6
444
T
ab
le
3
.
Sm
ar
t
c
o
n
tr
ac
t secu
r
i
ty
d
atasets
an
d
r
eso
u
r
ce
s
D
a
t
a
s
e
t
/
To
o
l
D
e
scri
p
t
i
o
n
A
c
c
e
ss
l
i
n
k
(
s)
S
martB
u
g
s
d
a
t
a
se
t
B
e
n
c
h
mar
k
s
u
i
t
e
o
f
S
o
l
i
d
i
t
y
c
o
n
t
r
a
c
t
s
w
i
t
h
l
a
b
e
l
e
d
v
u
l
n
e
r
a
b
i
l
i
t
i
e
s
h
t
t
p
s
:
/
/
g
i
t
h
u
b
.
c
o
m/
s
mart
b
u
g
s
/
smar
t
b
u
g
s
Et
h
e
r
n
a
u
t
G
a
me
-
b
a
se
d
p
l
a
t
f
o
r
m
b
y
O
p
e
n
Z
e
p
p
e
l
i
n
w
i
t
h
i
n
t
e
n
t
i
o
n
a
l
l
y
v
u
l
n
e
r
a
b
l
e
c
o
n
t
r
a
c
t
s
h
t
t
p
s
:
/
/
e
t
h
e
r
n
a
u
t
.
o
p
e
n
z
e
p
p
e
l
i
n
.
c
o
m
/
h
t
t
p
s
:
/
/
g
i
t
h
u
b
.
c
o
m/
O
p
e
n
Z
e
p
p
e
l
i
n
/
e
t
h
e
r
n
a
u
t
H
o
n
e
y
B
a
d
g
e
r
d
at
a
se
t
C
o
l
l
e
c
t
i
o
n
o
f
h
o
n
e
y
p
o
t
smar
t
c
o
n
t
r
a
c
t
s u
s
e
d
t
o
d
e
t
e
c
t
a
n
d
s
t
u
d
y
s
c
a
m
b
e
h
a
v
i
o
r
h
t
t
p
s
:
/
/
g
i
t
h
u
b
.
c
o
m/
c
h
r
i
st
o
f
t
o
r
r
e
s/
H
o
n
e
y
B
a
d
g
e
r
R
E
K
T.
N
e
w
s
R
e
p
o
s
i
t
o
r
y
o
f
r
e
a
l
-
w
o
r
l
d
D
e
F
i
e
x
p
l
o
i
t
p
o
s
t
m
o
r
t
e
m
s
w
i
t
h
a
n
a
l
y
t
i
c
a
l
i
n
s
i
g
h
t
s
h
t
t
p
s
:
/
/
r
e
k
t
.
n
e
w
s/
T
ab
le
4
.
Data
s
ets f
o
r
f
r
o
n
t
-
r
u
n
n
in
g
an
d
tr
an
s
ac
tio
n
al
th
r
ea
t d
etec
tio
n
S
o
u
r
c
e
/
To
o
l
D
e
scri
p
t
i
o
n
A
c
c
e
ss
l
i
n
k
(
s
)
Et
h
e
r
sca
n
P
r
o
v
i
d
e
s
h
i
s
t
o
r
i
c
a
l
t
r
a
n
sac
t
i
o
n
d
a
t
a
,
c
o
n
t
r
a
c
t
ca
l
l
s,
a
n
d
g
a
s
u
sa
g
e
.
h
t
t
p
s
:
/
/
e
t
h
e
r
sca
n
.
i
o
/
h
t
t
p
s
:
/
/
d
o
c
s.e
t
h
e
r
sc
a
n
.
i
o
F
l
a
s
h
b
o
t
s
/
M
EV
-
e
x
p
l
o
r
e
O
f
f
e
r
s i
n
s
i
g
h
t
s i
n
t
o
r
e
a
l
-
w
o
r
l
d
M
EV
b
e
h
a
v
i
o
u
r
l
i
k
e
s
a
n
d
w
i
c
h
a
t
t
a
c
k
s
a
n
d
a
r
b
i
t
r
a
g
e
.
h
t
t
p
s
:
/
/
d
o
c
s.fl
a
sh
b
o
t
s.
n
e
t
/
h
t
t
p
s
:
/
/
e
x
p
l
o
r
e
.
f
l
a
s
h
b
o
t
s
.
n
e
t
/
h
t
t
p
s
:
/
/
g
i
t
h
u
b
.
c
o
m/
f
l
a
s
h
b
o
t
s/
m
e
v
-
i
n
s
p
e
c
t
-
py
S
i
mu
l
a
t
e
d
m
e
mp
o
o
l
d
a
t
a
se
t
s
U
sed
f
o
r
g
e
n
e
r
a
t
i
n
g
s
y
n
t
h
e
t
i
c
t
r
a
n
sa
c
t
i
o
n
d
a
t
a
i
n
c
o
n
t
r
o
l
l
e
d
e
n
v
i
r
o
n
me
n
t
s fo
r
f
r
o
n
t
-
r
u
n
n
i
n
g
a
n
a
l
y
si
s
.
Et
h
e
r
e
u
m T
e
st
n
e
t
s (S
e
p
o
l
i
a
,
G
o
e
r
l
i
)
h
t
t
p
s
:
/
/
t
r
u
f
f
l
e
s
u
i
t
e
.
c
o
m/
g
a
n
a
c
h
e
/
h
t
t
p
s
:
/
/
t
e
n
d
e
r
l
y
.
c
o
/
T
ab
le
5
.
Or
ac
le
d
ata
s
o
u
r
ce
s
f
o
r
ML
-
b
ased
tr
u
s
t e
v
alu
atio
n
S
o
u
r
c
e
/
P
l
a
t
f
o
r
m
D
e
scri
p
t
i
o
n
A
c
c
e
ss
l
i
n
k
(
s)
C
h
a
i
n
l
i
n
k
d
a
t
a
f
e
e
d
s
O
f
f
e
r
s t
r
a
n
s
p
a
r
e
n
t
,
o
n
-
c
h
a
i
n
p
r
i
c
e
f
e
e
d
s a
c
r
o
ss
m
u
l
t
i
p
l
e
b
l
o
c
k
c
h
a
i
n
s
.
H
i
st
o
r
i
c
a
l
d
a
t
a
h
e
l
p
s
a
ssess
r
e
l
i
a
b
i
l
i
t
y
,
l
a
t
e
n
c
y
,
a
n
d
p
r
i
c
e
d
e
v
i
a
t
i
o
n
.
h
t
t
p
s
:
/
/
d
a
t
a
.
c
h
a
i
n
.
l
i
n
k
/
U
M
A
o
r
a
c
l
e
d
a
s
h
b
o
a
r
d
P
r
o
v
i
d
e
s a
n
o
p
t
i
mi
s
t
i
c
o
r
a
c
l
e
w
i
t
h
d
i
s
p
u
t
e
w
i
n
d
o
w
s.
Ti
m
e
-
seri
e
s
d
a
t
a
c
a
n
b
e
u
se
d
f
o
r
a
n
o
ma
l
y
d
e
t
e
c
t
i
o
n
a
n
d
f
o
r
e
c
a
st
i
n
g
m
o
d
e
l
s.
h
t
t
p
s
:
/
/
u
ma.
x
y
z
/
6.
CH
AL
L
E
NG
E
S
AND
F
U
T
URE P
O
T
E
NT
I
A
L
ML
m
o
d
els
f
o
r
s
m
ar
t
co
n
tr
ac
t
s
ec
u
r
ity
h
av
e
c
o
m
e
a
lo
n
g
w
ay
b
u
t
s
till
s
u
f
f
er
s
ev
er
al
d
r
a
wb
ac
k
s
th
at
to
d
ay
im
p
e
d
e
th
eir
p
r
ac
tical
d
ep
lo
y
m
en
t
an
d
r
ea
l
-
tim
e
r
eliab
ilit
y
in
a
DeFi
en
v
ir
o
n
m
en
t.
T
h
e
p
r
in
cip
al
d
if
f
icu
lty
t
h
ey
s
u
f
f
e
r
is
o
n
e
o
f
g
e
n
er
aliza
tio
n
w
h
er
ein
m
o
d
els
tr
ain
ed
o
n
clea
n
,
la
b
eled
t
est
co
n
tr
ac
ts
f
in
d
i
t
d
if
f
icu
lt
to
d
etec
t
v
u
ln
er
ab
ilit
ies
in
co
n
tr
ac
t
co
d
es
th
at
ar
e
o
b
f
u
s
ca
ted
,
m
i
n
if
ied
,
o
r
r
ea
l
-
wo
r
ld
co
n
tr
ac
t
-
f
r
ee
co
d
es
th
at
d
ev
iate
f
r
o
m
t
h
e
u
s
u
al
p
atter
n
s
.
T
h
er
e
i
s
also
a
co
n
ce
r
n
ab
o
u
t
in
ter
p
r
etab
ilit
y
,
esp
ec
ially
in
f
in
an
c
e
wh
er
e
tr
an
s
p
ar
en
c
y
an
d
au
d
it
ab
ilit
y
ar
e
cr
u
cial.
Dee
p
lea
r
n
in
g
m
o
d
els
ar
e
p
o
wer
f
u
l,
b
u
t
th
e
m
ec
h
an
is
m
an
d
p
r
o
ce
s
s
o
f
h
o
w
a
ce
r
tain
v
u
l
n
er
a
b
ilit
y
p
r
ed
ictio
n
was
m
a
d
e
b
y
a
s
p
ec
if
ic
to
o
lin
g
r
em
ain
s
u
n
cle
a
r
to
th
e
d
ev
elo
p
er
,
an
d
au
d
ito
r
.
R
ea
l
-
tim
e
tr
ain
in
g
a
n
d
in
f
er
en
ce
o
n
-
ch
ain
ar
e
also
p
r
ev
e
n
ted
b
y
co
m
p
u
tatio
n
al
r
eq
u
ir
em
e
n
ts
an
d
E
th
er
e
u
m
g
as
lim
itatio
n
s
.
Go
in
g
f
o
r
th
,
th
e
r
ef
in
em
en
t
o
f
s
ev
er
al
c
r
itical
ar
ea
s
will
p
u
t
an
en
d
to
t
h
e
ab
o
v
e
r
estrictio
n
s
.
I
n
h
y
b
r
id
s
y
s
tem
s
co
m
b
in
in
g
t
h
e
d
eter
m
in
is
tic
r
eliab
ilit
y
o
f
s
tatic
an
aly
s
is
to
o
ls
with
th
e
ad
ap
tiv
e
lear
n
i
n
g
c
ap
ab
ilit
ies
o
f
ML
m
o
d
els,
o
n
e
ac
h
iev
es
b
etter
ac
cu
r
ac
ie
s
co
u
p
led
with
an
u
n
d
er
s
tan
d
i
n
g
o
f
co
n
tex
t.
XAI
m
eth
o
d
s
ar
e
b
ein
g
s
tu
d
ied
to
allo
w
f
o
r
t
r
an
s
p
a
r
e
n
cy
o
f
th
e
m
o
d
el
s
o
th
at
u
s
er
s
ca
n
f
o
llo
w
th
e
d
ec
is
io
n
p
ath
w
ay
an
d
u
n
d
er
s
tan
d
wh
y
a
co
n
t
r
ac
t
is
m
ar
k
ed
as
r
is
k
y
.
Sem
i
-
s
u
p
er
v
is
ed
an
d
s
elf
-
s
u
p
er
v
is
ed
lear
n
in
g
m
eth
o
d
s
th
at
ca
n
tak
e
ad
v
an
tag
e
o
f
h
u
g
e
r
ep
o
s
ito
r
ies
o
f
u
n
lab
eled
s
m
ar
t
co
n
tr
ac
ts
ar
e
und
e
r
d
ev
e
l
o
p
m
en
t,
in
a
n
attem
p
t
to
m
in
im
ize
d
e
p
en
d
e
n
ce
o
n
ex
p
e
r
t
-
lab
eled
d
ata.
Als
o
,
e
m
b
ed
d
in
g
r
ea
l
-
tim
e
ML
-
b
ased
s
ca
n
n
in
g
t
o
o
ls
with
in
I
DE
s
o
r
o
n
d
e
p
lo
y
m
e
n
t
p
latf
o
r
m
s
wo
u
ld
p
r
o
v
i
d
e
d
ev
el
o
p
er
s
with
s
ec
u
r
ity
war
n
in
g
s
b
e
f
o
r
e
a
d
ep
lo
y
m
en
t
is
ev
en
m
ad
e
o
n
-
ch
ain
.
All
t
h
e
s
e
ef
f
o
r
ts
in
ten
d
to
clo
s
e
th
e
g
ap
b
etwe
en
th
e
th
eo
r
etica
l p
o
wer
o
f
ML
a
n
d
t
h
e
r
ea
l
-
wo
r
ld
n
ee
d
s
o
f
s
m
ar
t c
o
n
tr
ac
t secu
r
ity
in
liv
e
DeFi
ec
o
s
y
s
tem
s
.
7.
CO
NCLU
SI
O
N
DeFi
is
s
til
l
r
ev
o
lu
tio
n
is
in
g
th
e
f
in
a
n
cial
wo
r
ld
,
y
et
its
f
ast
g
r
o
wth
h
as
also
b
r
o
u
g
h
t
with
i
t
tr
em
en
d
o
u
s
s
ec
u
r
ity
is
s
u
es,
esp
ec
ially
r
eg
ar
d
in
g
t
h
r
ea
t
s
to
s
m
ar
t
co
n
tr
ac
ts
.
T
h
is
p
ap
er
h
as
g
iv
e
n
a
co
m
p
r
eh
e
n
s
iv
e
o
v
e
r
v
iew
o
f
em
er
g
in
g
t
r
en
d
s
in
ML
m
et
h
o
d
s
f
o
r
id
en
tif
y
in
g
a
n
d
m
it
ig
atin
g
s
u
ch
r
is
k
s
.
B
y
co
m
p
ar
in
g
v
ar
io
u
s
ML
f
r
a
m
ewo
r
k
s
r
an
g
i
n
g
f
r
o
m
s
u
p
er
v
i
s
ed
an
d
u
n
s
u
p
er
v
is
ed
lear
n
in
g
to
d
ee
p
lear
n
i
n
g
an
d
h
y
b
r
id
m
eth
o
d
s
,
we
em
p
h
asis
e
th
eir
ab
ilit
y
to
id
e
n
tify
c
o
m
p
lex
p
atter
n
s
o
f
attac
k
s
,
in
clu
d
in
g
r
ee
n
tr
a
n
cy
,
f
r
o
n
t
-
r
u
n
n
i
n
g
,
a
n
d
p
h
is
h
in
g
.
W
h
ile
in
im
p
r
o
v
in
g
t
h
e
s
ec
u
r
ity
o
f
DeFi
p
latf
o
r
m
s
,
s
ig
n
if
i
ca
n
t
g
ap
s
s
t
ill
ex
is
t
th
at
n
ee
d
to
b
e
f
illed
.
Av
ailab
ilit
y
o
f
d
ata,
in
ter
p
r
eta
b
ilit
y
o
f
m
o
d
els,
r
ea
l
-
tim
e
d
etec
ti
o
n
,
an
d
ad
v
er
s
ar
ial
r
o
b
u
s
tn
ess
ar
e
s
till
o
p
en
ar
ea
s
o
f
co
n
ce
r
n
.
I
n
ad
d
itio
n
,
c
o
m
b
in
in
g
ML
-
ba
s
ed
m
eth
o
d
s
with
cu
r
r
en
t
f
o
r
m
al
v
er
if
icatio
n
an
d
au
d
it
t
o
o
ls
m
a
y
in
tr
o
d
u
ce
ev
en
m
o
r
e
r
o
b
u
s
t
an
d
d
ep
e
n
d
ab
le
s
ec
u
r
ity
p
ar
ad
ig
m
s
.
Up
co
m
in
g
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
I
SS
N:
2252
-
8
7
7
6
S
ec
u
r
in
g
Defi
:
a
c
o
mp
r
eh
en
s
ive
r
ev
iew
o
f
ML
a
p
p
r
o
a
ch
es fo
r
d
etec
tin
g
…
(
Dh
ivya
l
a
ksh
mi
V
en
ka
tr
a
m
an
)
445
r
esear
ch
s
h
o
u
ld
e
m
p
h
asis
e
th
e
d
ev
elo
p
m
e
n
t
o
f
s
tan
d
ar
d
is
e
d
d
atasets
,
ex
p
lain
ab
le
AI
m
o
d
els
f
o
r
an
al
y
s
in
g
s
m
ar
t
co
n
tr
ac
ts
,
an
d
th
e
d
e
v
elo
p
m
en
t
o
f
r
o
b
u
s
t,
r
ea
l
-
tim
e
d
ef
en
ce
s
y
s
te
m
s
.
C
lo
s
in
g
th
e
g
ap
b
etwe
en
th
eo
r
etica
l
ML
r
esear
ch
an
d
r
ea
l
-
wo
r
ld
DeFi
d
ep
lo
y
m
e
n
t
will
b
e
k
ey
to
th
e
d
ev
elo
p
m
e
n
t
o
f
a
s
ec
u
r
e
an
d
r
eliab
le
d
ec
en
tr
alis
ed
f
in
a
n
cial
f
u
tu
r
e.
ACK
NO
WL
E
DG
M
E
N
T
S
W
e
wo
u
ld
lik
e
to
th
an
k
o
u
r
i
n
s
titu
tio
n
,
Vello
r
e
I
n
s
titu
te
o
f
T
ec
h
n
o
l
o
g
y
,
V
e
llo
r
e
(
VI
T
U
n
iv
er
s
ity
)
,
f
o
r
th
eir
i
n
v
alu
ab
le
s
u
p
p
o
r
t d
u
r
in
g
th
e
wr
itin
g
o
f
t
h
is
ar
ticle.
F
UNDING
I
NF
O
R
M
A
T
I
O
N
T
h
e
au
th
o
r
s
d
ec
lar
e
th
at
n
o
f
u
n
d
in
g
was r
ec
eiv
e
d
f
o
r
th
is
r
e
v
iew
wo
r
k
.
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
l
es
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
au
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
Dh
iv
y
alak
s
h
m
i
Ven
k
atr
am
an
✓
✓
✓
✓
✓
✓
Ma
n
ik
an
d
an
Ku
p
p
u
s
am
y
✓
✓
✓
✓
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
d
ec
lar
e
n
o
co
n
f
licts
o
f
in
ter
est.
DATA AV
AI
L
AB
I
L
I
T
Y
T
h
e
d
ata
u
s
ed
in
th
is
s
tu
d
y
w
er
e
o
b
tain
e
d
f
r
o
m
p
u
b
licly
a
v
ailab
le
s
o
u
r
ce
s
,
in
clu
d
i
n
g
E
th
er
eu
m
o
n
-
ch
ain
d
ata,
Un
is
wap
tr
a
n
s
ac
tio
n
lo
g
s
,
an
d
o
p
en
ME
V
-
r
elate
d
d
atasets
.
An
y
ad
d
itio
n
al
d
atasets
s
u
p
p
o
r
tin
g
th
is
s
tu
d
y
ar
e
av
ailab
le
u
p
o
n
r
ea
s
o
n
ab
le
r
eq
u
est to
th
e
co
r
r
esp
o
n
d
in
g
au
t
h
o
r
.
RE
F
E
R
E
NC
E
S
[
1
]
J.
J.
L
o
h
i
t
h
,
K
.
A
.
M
a
n
o
j
,
P
.
G
.
N
a
n
ma
,
a
n
d
P
.
S
r
i
n
i
v
a
sa
n
,
“
TP
-
D
e
t
e
c
t
:
t
r
i
g
r
a
m
-
p
i
x
e
l
b
a
se
d
v
u
l
n
e
r
a
b
i
l
i
t
y
d
e
t
e
c
t
i
o
n
f
o
r
Et
h
e
r
e
u
m
s
mart
c
o
n
t
r
a
c
t
s,
”
M
u
l
t
i
m
e
d
i
a
T
o
o
l
s
a
n
d
A
p
p
l
i
c
a
t
i
o
n
s
,
v
o
l
.
8
2
,
n
o
.
2
3
,
p
p
.
3
6
3
7
9
–
3
6
3
9
3
,
2
0
2
3
,
d
o
i
:
1
0
.
1
0
0
7
/
s
1
1
0
4
2
-
0
2
3
-
1
5
0
4
2
-
4.
[
2
]
O
.
Z
a
a
z
a
a
a
n
d
H
.
El
B
a
k
k
a
l
i
,
“
U
n
v
e
i
l
i
n
g
t
h
e
l
a
n
d
sc
a
p
e
o
f
smar
t
c
o
n
t
r
a
c
t
v
u
l
n
e
r
a
b
i
l
i
t
i
e
s:
a
d
e
t
a
i
l
e
d
e
x
a
mi
n
a
t
i
o
n
a
n
d
c
o
d
i
f
i
c
a
t
i
o
n
o
f
v
u
l
n
e
r
a
b
i
l
i
t
i
e
s
i
n
p
r
o
m
i
n
e
n
t
b
l
o
c
k
c
h
a
i
n
s,”
I
n
t
e
r
n
a
t
i
o
n
a
l
J
o
u
r
n
a
l
o
f
C
o
m
p
u
t
e
r
N
e
t
w
o
r
k
s
a
n
d
C
o
m
m
u
n
i
c
a
t
i
o
n
s
,
v
o
l
.
1
5
,
n
o
.
6
,
p
p
.
5
5
–
75
,
2
0
2
3
,
d
o
i
:
1
0
.
5
1
2
1
/
i
j
c
n
c
.
2
0
2
3
.
1
5
6
0
3
.
[
3
]
Z.
L
i
e
t
a
l
.
,
“
V
u
l
H
u
n
t
e
r
:
h
u
n
t
i
n
g
v
u
l
n
e
r
a
b
l
e
smar
t
c
o
n
t
r
a
c
t
s
a
t
E
V
M
b
y
t
e
c
o
d
e
-
l
e
v
e
l
v
i
a
m
u
l
t
i
p
l
e
i
n
st
a
n
c
e
l
e
a
r
n
i
n
g
,
”
I
EEE
T
r
a
n
s
a
c
t
i
o
n
s
o
n
S
o
f
t
w
a
r
e
E
n
g
i
n
e
e
ri
n
g
,
v
o
l
.
4
9
,
n
o
.
1
1
,
p
p
.
4
8
8
6
–
4
9
1
6
,
N
o
v
.
2
0
2
3
,
d
o
i
:
1
0
.
1
1
0
9
/
TSE
.
2
0
2
3
.
3
3
1
7
2
0
9
.
[4
]
S
.
S
.
K
u
s
h
w
a
h
a
,
S
.
Jo
s
h
,
D
.
S
i
n
g
h
,
M
.
K
a
u
r
,
a
n
d
H
.
-
N
.
L
e
e
,
“
E
t
h
e
r
e
u
m
smar
t
c
o
n
t
r
a
c
t
a
n
a
l
y
s
i
s
t
o
o
l
s:
a
s
y
st
e
mat
i
c
r
e
v
i
e
w
,
”
I
EEE
A
c
c
e
ss
,
v
o
l
.
1
0
,
p
p
.
5
7
0
3
7
–
5706
2
,
2
0
2
2
.
[
5
]
Z.
W
a
n
g
a
n
d
S
.
G
u
a
n
,
“
A
b
l
o
c
k
c
h
a
i
n
-
b
a
s
e
d
t
r
a
c
e
a
b
l
e
a
n
d
s
e
c
u
r
e
d
a
t
a
-
s
h
a
r
i
n
g
sc
h
e
m
e
,
”
Pe
e
rJ
C
o
m
p
u
t
e
r
S
c
i
e
n
c
e
,
v
o
l
.
9
,
p
.
e
1
3
3
7
,
A
p
r
.
2
0
2
3
,
d
o
i
:
1
0
.
7
7
1
7
/
p
e
e
r
j
-
c
s.
1
3
3
7
.
[
6
]
V
.
M
e
r
l
o
,
G
.
P
i
o
,
F
.
G
i
u
st
o
,
a
n
d
M
.
B
i
l
a
n
c
i
a
,
“
O
n
t
h
e
e
x
p
l
o
i
t
a
t
i
o
n
o
f
t
h
e
b
l
o
c
k
c
h
a
i
n
t
e
c
h
n
o
l
o
g
y
i
n
t
h
e
h
e
a
l
t
h
c
a
r
e
s
e
c
t
o
r
:
a
sy
st
e
mat
i
c
r
e
v
i
e
w
,
”
E
x
p
e
r
t
S
y
s
t
e
m
s
w
i
t
h
Ap
p
l
i
c
a
t
i
o
n
s
,
v
o
l
.
2
1
3
,
p
.
1
1
8
8
9
7
,
M
a
r
.
2
0
2
3
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
e
sw
a
.
2
0
2
2
.
1
1
8
8
9
7
.
[
7
]
S
.
K
.
L
o
e
t
a
l
.
,
“
A
n
a
l
y
s
i
s
o
f
b
l
o
c
k
c
h
a
i
n
s
o
l
u
t
i
o
n
s
f
o
r
I
o
T:
a
sy
st
e
mat
i
c
l
i
t
e
r
a
t
u
r
e
r
e
v
i
e
w
,
”
I
EEE
Ac
c
e
ss
,
v
o
l
.
7
,
p
p
.
5
8
8
2
2
–
5
8
8
3
5
,
2
0
1
9
,
d
o
i
:
1
0
.
1
1
0
9
/
A
C
C
E
S
S
.
2
0
1
9
.
2
9
1
4
6
7
5
.
[
8
]
Y.
X
u
,
G
.
H
u
,
L.
Y
o
u
,
a
n
d
C
.
C
a
o
,
“
A
n
o
v
e
l
ma
c
h
i
n
e
l
e
a
r
n
i
n
g
-
b
a
se
d
a
n
a
l
y
si
s
m
o
d
e
l
f
o
r
sm
a
r
t
c
o
n
t
r
a
c
t
v
u
l
n
e
r
a
b
i
l
i
t
y
,
”
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
r
k
s
,
v
o
l
.
2
0
2
1
,
p
p
.
1
–
1
2
,
A
u
g
.
2
0
2
1
,
d
o
i
:
1
0
.
1
1
5
5
/
2
0
2
1
/
5
7
9
8
0
3
3
.
[
9
]
S
.
A
g
g
a
r
w
a
l
a
n
d
N
.
K
u
m
a
r
,
“
C
r
y
p
t
o
g
r
a
p
h
i
c
c
o
n
se
n
su
s
me
c
h
a
n
i
sms
,
”
i
n
A
d
v
a
n
c
e
s i
n
C
o
m
p
u
t
e
rs
,
v
o
l
.
1
2
1
,
2
0
2
1
,
p
p
.
2
1
1
–
2
2
6
.
[
1
0
]
W
.
W
a
n
g
,
H
.
H
u
a
n
g
,
Z.
Y
i
n
,
T.
R
.
G
a
d
e
k
a
l
l
u
,
M
.
A
l
a
z
a
b
,
a
n
d
C
.
S
u
,
“
S
mart
c
o
n
t
r
a
c
t
t
o
k
e
n
-
b
a
se
d
p
r
i
v
a
c
y
-
p
r
e
ser
v
i
n
g
a
c
c
e
ss
c
o
n
t
r
o
l
s
y
st
e
m
f
o
r
i
n
d
u
st
r
i
a
l
I
n
t
e
r
n
e
t
o
f
T
h
i
n
g
s,
”
D
i
g
i
t
a
l
C
o
m
m
u
n
i
c
a
t
i
o
n
s
a
n
d
N
e
t
w
o
rks
,
v
o
l
.
9
,
n
o
.
2
,
p
p
.
3
3
7
–
3
4
6
,
2
0
2
3
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
d
c
a
n
.
2
0
2
2
.
1
0
.
0
0
5
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
7
7
6
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
,
Vo
l.
1
5
,
No
.
1
,
Ma
r
ch
20
2
6
:
43
8
-
44
6
446
[
1
1
]
B
.
Ji
a
n
g
,
Y
.
L
i
u
,
a
n
d
W
.
K
.
C
h
a
n
,
“
C
o
n
t
r
a
c
t
F
u
z
z
e
r
:
f
u
z
z
i
n
g
sm
a
r
t
c
o
n
t
r
a
c
t
s
f
o
r
v
u
l
n
e
r
a
b
i
l
i
t
y
d
e
t
e
c
t
i
o
n
,
”
i
n
Pr
o
c
e
e
d
i
n
g
s
o
f
t
h
e
3
3
r
d
A
C
M/
I
EE
E
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
u
t
o
m
a
t
e
d
S
o
f
t
w
a
r
e
E
n
g
i
n
e
e
r
i
n
g
,
S
e
p
.
2
0
1
8
,
p
p
.
2
5
9
–
2
6
9
,
d
o
i
:
1
0
.
1
1
4
5
/
3
2
3
8
1
4
7
.
3
2
3
8
1
7
7
.
[
1
2
]
L.
Z
h
a
n
g
e
t
a
l
.
,
“
C
B
G
R
U
:
a
d
e
t
e
c
t
i
o
n
m
e
t
h
o
d
o
f
sm
a
r
t
c
o
n
t
r
a
c
t
v
u
l
n
e
r
a
b
i
l
i
t
y
b
a
se
d
o
n
a
h
y
b
r
i
d
m
o
d
e
l
,
”
S
e
n
so
rs
,
v
o
l
.
2
2
,
n
o
.
9
,
p
.
3
5
7
7
,
M
a
y
2
0
2
2
,
d
o
i
:
1
0
.
3
3
9
0
/
s2
2
0
9
3
5
7
7
.
[
1
3
]
L.
Z
h
a
n
g
e
t
a
l
.
,
“
A
n
o
v
e
l
smar
t
c
o
n
t
r
a
c
t
v
u
l
n
e
r
a
b
i
l
i
t
y
d
e
t
e
c
t
i
o
n
me
t
h
o
d
b
a
s
e
d
o
n
i
n
f
o
r
ma
t
i
o
n
g
r
a
p
h
a
n
d
e
n
s
e
mb
l
e
l
e
a
r
n
i
n
g
,
”
S
e
n
so
rs
,
v
o
l
.
2
2
,
n
o
.
9
,
p
.
3
5
8
1
,
M
a
y
2
0
2
2
,
d
o
i
:
1
0
.
3
3
9
0
/
s2
2
0
9
3
5
8
1
.
[
1
4
]
L.
Zh
a
ng
e
t
a
l.
,
“
S
P
C
B
I
G
-
EC
:
a
r
o
b
u
st
ser
i
a
l
h
y
b
r
i
d
m
o
d
e
l
f
o
r
smar
t
c
o
n
t
r
a
c
t
v
u
l
n
e
r
a
b
i
l
i
t
y
d
e
t
e
c
t
i
o
n
,
”
S
e
n
s
o
rs
,
v
o
l
.
2
2
,
n
o
.
1
2
,
p
.
4
6
2
1
,
Ju
n
.
2
0
2
2
,
d
o
i
:
1
0
.
3
3
9
0
/
s
2
2
1
2
4
6
2
1
.
[
1
5
]
T.
H
.
-
D
.
H
u
a
n
g
,
“
H
u
n
t
i
n
g
t
h
e
Et
h
e
r
e
u
m
sm
a
r
t
c
o
n
t
r
a
c
t
:
c
o
l
o
r
-
i
n
s
p
i
r
e
d
i
n
sp
e
c
t
i
o
n
o
f
p
o
t
e
n
t
i
a
l
a
t
t
a
c
k
s
,
”
,
"
a
r
Xi
v
p
re
p
r
i
n
t
,
a
rXi
v
:
1
8
0
7
.
0
1
8
6
8
,
Ju
l
.
2
0
1
8
,
[
O
n
l
i
n
e
]
.
A
v
a
i
l
a
b
l
e
:
h
t
t
p
:
/
/
a
r
x
i
v
.
o
r
g
/
a
b
s/
1
8
0
7
.
0
1
8
6
8
.
[
1
6
]
J.
-
W
.
Li
a
o
,
T.
-
T.
Ts
a
i
,
C
.
-
K
.
H
e
,
a
n
d
C
.
-
W
.
T
i
e
n
,
“
S
o
l
i
A
u
d
i
t
:
sm
a
r
t
c
o
n
t
r
a
c
t
v
u
l
n
e
r
a
b
i
l
i
t
y
a
ssessm
e
n
t
b
a
s
e
d
o
n
mac
h
i
n
e
l
e
a
r
n
i
n
g
a
n
d
f
u
z
z
t
e
st
i
n
g
,
”
i
n
20
1
9
S
i
x
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
I
n
t
e
r
n
e
t
o
f
T
h
i
n
g
s:
S
y
s
t
e
m
s,
M
a
n
a
g
e
m
e
n
t
a
n
d
S
e
c
u
ri
t
y
(
I
O
T
S
M
S
)
,
O
c
t
.
2
0
1
9
,
p
p
.
4
5
8
–
4
6
5
,
d
o
i
:
1
0
.
1
1
0
9
/
I
O
TSM
S
4
8
1
5
2
.
2
0
1
9
.
8
9
3
9
2
5
6
.
[
1
7
]
X
.
Y
u
,
H
.
Zh
a
o
,
B
.
H
o
u
,
Z.
Y
i
n
g
,
a
n
d
B
.
W
u
,
“
D
e
e
S
C
V
H
u
n
t
e
r
:
a
d
e
e
p
l
e
a
r
n
i
ng
-
b
a
s
e
d
f
r
a
mew
o
r
k
f
o
r
smar
t
c
o
n
t
r
a
c
t
v
u
l
n
e
r
a
b
i
l
i
t
y
d
e
t
e
c
t
i
o
n
,
”
i
n
2
0
2
1
I
n
t
e
r
n
a
t
i
o
n
a
l
J
o
i
n
t
C
o
n
f
e
re
n
c
e
o
n
N
e
u
r
a
l
N
e
t
w
o
rks
(
I
J
C
N
N
)
,
J
u
l
.
2
0
2
1
,
v
o
l
.
2
0
2
1
-
Ju
l
y
,
p
p
.
1
–
8
,
d
o
i
:
1
0
.
1
1
0
9
/
I
JC
N
N
5
2
3
8
7
.
2
0
2
1
.
9
5
3
4
3
2
4
.
[
1
8
]
Z.
F
e
n
g
e
t
a
l
.
,
“
C
o
d
e
B
ER
T:
a
p
r
e
-
t
r
a
i
n
e
d
m
o
d
e
l
f
o
r
p
r
o
g
r
a
mm
i
n
g
a
n
d
n
a
t
u
r
a
l
l
a
n
g
u
a
g
e
s,”
i
n
F
i
n
d
i
n
g
s
o
f
t
h
e
Asso
c
i
a
t
i
o
n
f
o
r
C
o
m
p
u
t
a
t
i
o
n
a
l
L
i
n
g
u
i
s
t
i
c
s:
EM
N
L
P
2
0
2
0
,
2
0
2
0
,
p
p
.
1
5
3
6
–
1
5
4
7
,
d
o
i
:
1
0
.
1
8
6
5
3
/
v
1
/
2
0
2
0
.
f
i
n
d
i
n
g
s
-
e
m
n
l
p
.
1
3
9
.
[
1
9
]
S
.
A
.
S
a
l
l
o
u
m
,
R
.
K
h
a
n
,
a
n
d
K
.
S
h
a
a
l
a
n
,
“
A
s
u
r
v
e
y
o
f
sem
a
n
t
i
c
a
n
a
l
y
s
i
s
a
p
p
r
o
a
c
h
e
s,
”
i
n
A
d
v
a
n
c
e
s
i
n
I
n
t
e
l
l
i
g
e
n
t
S
y
s
t
e
m
s
a
n
d
C
o
m
p
u
t
i
n
g
,
v
o
l
.
1
1
5
3
A
I
S
C
,
2
0
2
0
,
p
p
.
6
1
–
7
0
.
[
2
0
]
A
.
A
l
-
B
o
g
h
d
a
d
y
,
M
.
El
-
R
a
ml
y
,
a
n
d
K
.
W
a
ssi
f
,
“
i
D
e
t
e
c
t
f
o
r
v
u
l
n
e
r
a
b
i
l
i
t
y
d
e
t
e
c
t
i
o
n
i
n
i
n
t
e
r
n
e
t
o
f
t
h
i
n
g
s
o
p
e
r
a
t
i
n
g
s
y
st
e
ms
u
si
n
g
mac
h
i
n
e
l
e
a
r
n
i
n
g
,
”
S
c
i
e
n
t
i
f
i
c
Re
p
o
r
t
s
,
v
o
l
.
1
2
,
n
o
.
1
,
p
.
1
7
0
8
6
,
O
c
t
.
2
0
2
2
,
d
o
i
:
1
0
.
1
0
3
8
/
s4
1
5
9
8
-
0
2
2
-
2
1
3
2
5
-
x.
[
2
1
]
A
.
H
a
d
d
a
d
,
M
.
H
.
H
a
b
a
e
b
i
,
M
.
R
.
I
sl
a
m,
N
.
F
.
H
a
sb
u
l
l
a
h
,
a
n
d
S
.
A
.
Z
a
b
i
d
i
,
“
S
y
st
e
ma
t
i
c
r
e
v
i
e
w
o
n
A
I
-
b
l
o
c
k
c
h
a
i
n
b
a
s
e
d
E
-
h
e
a
l
t
h
c
a
r
e
r
e
c
o
r
d
s
ma
n
a
g
e
m
e
n
t
s
y
s
t
e
ms,”
I
E
EE
A
c
c
e
ss
,
2
0
2
2
.
[
2
2
]
M
.
A
l
a
e
d
d
i
n
i
,
M
.
H
a
j
i
z
a
d
e
h
,
a
n
d
P
.
R
e
a
i
d
y
,
“
A
b
i
b
l
i
o
m
e
t
r
i
c
a
n
a
l
y
si
s
o
f
r
e
sea
r
c
h
o
n
t
h
e
c
o
n
v
e
r
g
e
n
c
e
o
f
a
r
t
i
f
i
c
i
a
l
i
n
t
e
l
l
i
g
e
n
c
e
a
n
d
b
l
o
c
k
c
h
a
i
n
i
n
sm
a
r
t
c
i
t
i
e
s,
”
S
m
a
r
t
C
i
t
i
e
s
,
v
o
l
.
6
,
n
o
.
2
,
p
p
.
7
6
4
–
7
9
5
,
M
a
r
.
2
0
2
3
,
d
o
i
:
1
0
.
3
3
9
0
/
s
mart
c
i
t
i
e
s6
0
2
0
0
3
7
.
[
2
3
]
R
.
K
u
m
a
r
,
A
r
j
u
n
a
d
i
t
y
a
,
D
.
S
i
n
g
h
,
K
.
S
r
i
n
i
v
a
s
a
n
,
a
n
d
Y
.
-
C
.
H
u
,
“
A
I
-
p
o
w
e
r
e
d
b
l
o
c
k
c
h
a
i
n
t
e
c
h
n
o
l
o
g
y
f
o
r
p
u
b
l
i
c
h
e
a
l
t
h
:
a
c
o
n
t
e
mp
o
r
a
r
y
r
e
v
i
e
w
,
o
p
e
n
c
h
a
l
l
e
n
g
e
s,
a
n
d
f
u
t
u
r
e
r
e
sea
r
c
h
d
i
r
e
c
t
i
o
n
s,”
H
e
a
l
t
h
c
a
re
,
v
o
l
.
1
1
,
n
o
.
1
,
p
.
8
1
,
D
e
c
.
2
0
2
2
,
d
o
i
:
1
0
.
3
3
9
0
/
h
e
a
l
t
h
c
a
r
e
1
1
0
1
0
0
8
1
.
[
2
4
]
M
.
S
.
B
.
K
a
s
y
a
p
a
a
n
d
C
.
V
a
n
m
a
t
h
i
,
“
B
l
o
c
k
c
h
a
i
n
i
n
t
e
g
r
a
t
i
o
n
i
n
h
e
a
l
t
h
c
a
r
e
:
a
c
o
m
p
r
e
h
e
n
si
v
e
i
n
v
e
s
t
i
g
a
t
i
o
n
o
f
u
s
e
c
a
s
e
s,
p
e
r
f
o
r
m
a
n
c
e
i
ss
u
e
s
,
a
n
d
m
i
t
i
g
a
t
i
o
n
s
t
r
a
t
e
g
i
e
s,
”
Fr
o
n
t
i
e
rs
i
n
D
i
g
i
t
a
l
H
e
a
l
t
h
,
v
o
l
.
6
,
A
p
r
.
2
0
2
4
,
d
o
i
:
1
0
.
3
3
8
9
/
f
d
g
t
h
.
2
0
2
4
.
1
3
5
9
8
5
8
.
[
2
5
]
M
.
G
u
e
t
a
l
.
,
“
S
o
f
t
w
a
r
e
se
c
u
r
i
t
y
v
u
l
n
e
r
a
b
i
l
i
t
y
m
i
n
i
n
g
b
a
s
e
d
o
n
d
e
e
p
l
e
a
r
n
i
n
g
,
”
J
i
s
u
a
n
j
i
Y
a
n
j
i
u
y
u
Fa
z
h
a
n
/
C
o
m
p
u
t
e
r
Re
s
e
a
rc
h
a
n
d
D
e
v
e
l
o
p
m
e
n
t
,
v
o
l
.
5
8
,
n
o
.
1
0
,
p
p
.
2
1
4
0
–
2
1
6
2
,
2
0
2
1
,
d
o
i
:
1
0
.
7
5
4
4
/
i
ss
n
1
0
0
0
-
1
2
3
9
.
2
0
2
1
.
2
0
2
1
0
6
2
0
.
[
2
6
]
R
.
K
i
a
n
i
a
n
d
V
.
S
.
S
h
e
n
g
,
“
Et
h
e
r
e
u
m
smar
t
c
o
n
t
r
a
c
t
v
u
l
n
e
r
a
b
i
l
i
t
y
d
e
t
e
c
t
i
o
n
a
n
d
m
a
c
h
i
n
e
l
e
a
r
n
i
n
g
-
d
r
i
v
e
n
s
o
l
u
t
i
o
n
s
:
a
s
y
st
e
ma
t
i
c
l
i
t
e
r
a
t
u
r
e
r
e
v
i
e
w
,
”
El
e
c
t
r
o
n
i
c
s (
S
w
i
t
z
e
rl
a
n
d
)
,
v
o
l
.
1
3
,
n
o
.
1
2
,
2
0
2
4
,
d
o
i
:
1
0
.
3
3
9
0
/
e
l
e
c
t
r
o
n
i
c
s1
3
1
2
2
2
9
5
.
B
I
O
G
RAP
H
I
E
S O
F
AUTH
O
RS
Dhi
v
y
a
la
k
s
h
m
i
Ve
n
k
a
tr
a
m
a
n
wa
s
b
o
rn
in
Arn
i,
Ti
ru
v
a
n
n
a
m
a
lai
District,
Tam
il
Na
d
u
,
In
d
ia.
S
h
e
o
b
tain
e
d
h
e
r
m
a
ste
r’s
d
e
g
re
e
in
c
o
m
p
u
ter
a
p
p
li
c
a
ti
o
n
s
fro
m
S
ri
Ba
laji
Ch
o
c
k
a
li
n
g
a
m
E
n
g
in
e
e
rin
g
C
o
ll
e
g
e
,
a
f
fil
iat
e
d
wit
h
A
n
n
a
Un
iv
e
rsit
y
,
C
h
e
n
n
a
i,
Tam
il
Na
d
u
,
i
n
2
0
1
3
.
Cu
rre
n
tl
y
,
sh
e
is
p
u
rs
u
in
g
h
e
r
Ph
.
D
.
in
Co
m
p
u
ter
S
c
ien
c
e
a
t
Ve
ll
o
re
In
stit
u
te
o
f
Tec
h
n
o
l
o
g
y
(VIT)
,
Ve
ll
o
re
,
wh
e
re
sh
e
is
a
c
ti
v
e
l
y
e
n
g
a
g
e
d
in
c
u
tt
in
g
-
e
d
g
e
re
se
a
rc
h
a
t
th
e
in
ters
e
c
ti
o
n
o
f
b
lo
c
k
c
h
a
i
n
tec
h
n
o
lo
g
y
a
n
d
m
a
c
h
in
e
lea
rn
i
n
g
.
Afte
r
c
o
m
p
leti
n
g
h
e
r
p
o
stg
ra
d
u
a
te
stu
d
ies
,
sh
e
b
e
g
a
n
h
e
r
a
c
a
d
e
m
ic
c
a
re
e
r
a
s
a
n
a
ss
istan
t
p
ro
fe
ss
o
r
in
th
e
De
p
a
rtme
n
t
o
f
M
CA
a
t
S
ri
Ba
laji
Ch
o
c
k
a
li
n
g
a
m
En
g
i
n
e
e
rin
g
Co
l
l
e
g
e
.
He
r
re
se
a
rc
h
fo
c
u
se
s
o
n
d
e
v
e
lo
p
in
g
in
telli
g
e
n
t
sy
ste
m
s
t
o
d
e
tec
t
a
n
d
p
re
v
e
n
t
sm
a
rt
c
o
n
trac
t
a
tt
a
c
k
s
in
De
F
i,
wit
h
p
a
rti
c
u
lar
e
m
p
h
a
sis
o
n
fr
o
n
t
-
r
u
n
n
i
n
g
a
n
d
sa
n
d
wic
h
a
tt
a
c
k
s
o
n
th
e
Et
h
e
re
u
m
b
l
o
c
k
c
h
a
in
.
He
r
b
ro
a
d
e
r
i
n
tere
sts
in
c
l
u
d
e
b
lo
c
k
c
h
a
in
se
c
u
rit
y
,
tru
stw
o
rth
y
AI,
a
n
d
a
p
p
l
y
in
g
m
a
c
h
i
n
e
lea
rn
in
g
i
n
h
i
g
h
-
r
isk
d
o
m
a
in
s
su
c
h
a
s
f
in
a
n
c
e
a
n
d
c
ry
p
t
o
c
u
rre
n
c
y
.
S
h
e
is
c
u
rre
n
tl
y
e
x
p
lo
r
in
g
a
d
v
a
n
c
e
d
d
e
tec
ti
o
n
m
e
th
o
d
o
l
o
g
ies
u
sin
g
tran
sa
c
ti
o
n
-
lev
e
l
a
n
d
to
k
e
n
-
lev
e
l
fe
a
tu
re
s,
a
s
we
ll
a
s
d
a
ta
in
te
g
rit
y
fra
m
e
wo
rk
s
f
o
r
De
F
i
o
ra
c
les
.
S
h
e
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
d
h
i
v
y
a
lak
s
h
m
i.
v
2
0
2
3
@
v
it
stu
d
e
n
t
.
a
c
.
in
.
Dr
.
Ma
n
ik
a
n
d
a
n
K
u
p
p
u
s
a
m
y
is
wo
r
k
in
g
a
s
p
r
o
fe
ss
o
r
in
th
e
S
c
h
o
o
l
o
f
Co
m
p
u
ter
S
c
ien
c
e
a
n
d
En
g
i
n
e
e
rin
g
a
t
V
e
ll
o
re
In
stit
u
te
o
f
Tec
h
n
o
l
o
g
y
(VI
T),
Ve
ll
o
re
.
He
re
c
e
iv
e
d
th
e
b
e
st
tec
h
n
o
fa
c
u
lt
y
a
wa
rd
fro
m
I
CTACT,
G
o
v
t
o
f
Tam
il
n
a
d
u
in
2
0
1
4
.
He
g
ra
d
u
a
ted
in
B
.
E
.
(CS
E)
wit
h
first
c
las
s
with
d
ist
in
c
ti
o
n
a
t
Ve
ll
o
re
En
g
in
e
e
rin
g
Co
l
leg
e
(P
re
se
n
tl
y
VIT)
u
n
d
e
r
th
e
a
ffil
iati
o
n
o
f
U
n
i
v
e
rsity
o
f
M
a
d
ra
s
in
2
0
0
4
.
He
se
c
u
re
d
a
M
a
ste
r
o
f
En
g
i
n
e
e
rin
g
(
M
.
E
.
)
i
n
CS
E
a
t
S
o
n
a
Co
ll
e
g
e
o
f
Tec
h
n
o
l
o
g
y
,
S
a
lem
u
n
d
e
r
th
e
a
ffil
iati
o
n
o
f
An
n
a
U
n
iv
e
rsit
y
in
2
0
0
6
with
a
Un
i
v
e
rsity
G
OLD
M
e
d
a
l.
He
Co
m
p
lete
d
a
n
M
BA
(S
y
ste
m
s)
a
t
Ala
g
a
p
p
a
U
n
iv
e
rsit
y
i
n
2
0
0
8
.
He
Co
m
p
lete
d
a
P
h
.
D.
i
n
th
e
field
o
f
wire
les
s
n
e
tw
o
rk
s
a
t
VIT
in
2
0
1
5
.
He
h
a
s
1
8
+
y
e
a
rs o
f
tea
c
h
in
g
a
n
d
re
se
a
rc
h
e
x
p
e
rien
c
e
.
He
h
a
s p
re
se
n
ted
a
n
u
m
b
e
r
o
f
p
a
p
e
rs at t
h
e
Na
ti
o
n
a
l
a
n
d
In
ter
n
a
ti
o
n
a
l
Co
n
fe
re
n
c
e
s
a
n
d
p
u
b
li
sh
e
d
5
0
+
p
a
p
e
rs
in
re
p
u
ted
jo
u
r
n
a
ls.
Hi
s
a
re
a
o
f
in
tere
s
t
in
c
l
u
d
e
s
wire
les
s
n
e
two
r
k
s,
c
lo
u
d
c
o
m
p
u
t
in
g
,
I
o
T,
AI,
a
n
d
da
ta
sc
ien
c
e
.
He
is
a
li
fe
m
e
m
b
e
r
o
f
CS
I
a
n
d
IAEn
g
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
k
m
a
n
ik
a
n
d
a
n
@
v
it
.
a
c
.
i
n
.
Evaluation Warning : The document was created with Spire.PDF for Python.