I
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o
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Appl
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I
J
AP
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)
Vo
l.
1
5
,
No
.
1
,
Ma
r
ch
20
2
6
,
p
p
.
42
1
~
4
2
9
I
SS
N:
2252
-
8
7
9
2
,
DOI
:
1
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.
1
1
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1
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.
v
1
5
.
i
1
.
pp
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429
421
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ttp
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Enha
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a
hybrid blo
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in and
ma
chine learning
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ppro
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a
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As
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rid
s
a
re
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o
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m
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p
lex
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ra
ti
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sm
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e
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c
y
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ly
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q
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e
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b
a
se
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o
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e
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tralize
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a
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it
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re
s
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n
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st
a
ti
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ru
le
-
b
a
se
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e
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lu
a
ti
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ten
d
t
o
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e
in
a
d
e
q
u
a
te
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re
a
l
-
ti
m
e
fa
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lt
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e
tec
ti
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n
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ted
re
sp
o
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se
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a
n
d
c
y
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e
rse
c
u
rit
y
.
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h
is
p
a
p
e
r
su
g
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e
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s
a
h
y
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rid
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rit
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h
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h
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o
imp
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o
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p
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telli
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c
u
rit
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ti
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g
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y
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n
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ly
sis
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F
o
r
th
e
IE
EE
3
0
-
b
u
s
tes
t
c
a
se
,
d
iffere
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li
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tag
e
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n
d
g
e
n
e
ra
to
r
fa
il
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re
c
a
se
s
we
re
sim
u
late
d
.
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re
n
t
m
a
c
h
in
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m
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ls
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h
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o
m
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st
(RF
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su
p
p
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t
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c
to
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m
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h
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(S
V
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),
a
n
d
g
ra
d
i
e
n
t
b
o
o
sti
n
g
(G
B)
,
we
re
train
e
d
t
o
c
las
sify
a
n
d
p
re
d
ict
th
e
se
c
o
n
ti
n
g
e
n
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ies
.
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p
a
ra
ll
e
l,
c
ry
p
to
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ra
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ic
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rimi
ti
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e
n
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ry
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ti
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n
sta
n
d
a
rd
(
AES
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,
Ri
v
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h
a
m
ir
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Ad
lem
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n
(
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S
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,
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n
d
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ll
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ti
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v
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c
ry
p
to
g
ra
p
h
y
(
EC
C
)
we
re
tes
ted
in
a
b
l
o
c
k
c
h
a
i
n
se
tt
in
g
to
p
ro
v
id
e
se
c
u
rit
y
fo
r
e
v
e
n
t
d
a
ta
a
n
d
e
n
a
b
le
a
u
to
m
a
ti
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re
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o
v
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ry
ste
p
s
th
r
o
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g
h
sm
a
rt
c
o
n
trac
ts.
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u
tco
m
e
s
il
l
u
str
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te
th
a
t
th
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sh
o
we
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th
e
m
a
x
i
m
u
m
fa
u
lt
c
las
sifica
ti
o
n
ra
te
(
9
3
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4
%
),
a
n
d
E
CC
e
n
su
re
d
li
g
h
t
y
e
t
r
o
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u
st
d
a
ta
p
ro
tec
ti
o
n
fo
r
b
l
o
c
k
c
h
a
in
a
c
ti
v
it
ies
.
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a
in
st
th
e
c
o
n
v
e
n
ti
o
n
a
l
sy
ste
m
,
th
e
d
e
sig
n
e
d
m
o
d
e
l
e
n
h
a
n
c
e
d
t
h
e
re
sp
o
n
se
ti
m
e
in
c
a
se
o
f
fa
u
lt
s,
a
c
c
u
ra
c
y
,
a
n
d
sy
ste
m
fa
u
lt
to
lera
n
c
e
.
Th
is t
wo
-
lay
e
r
m
e
c
h
a
n
ism
p
re
se
n
ts
a
sc
a
lab
le,
p
r
o
a
c
ti
v
e
,
a
n
d
c
y
b
e
r
-
sa
fe
m
e
c
h
a
n
ism
fo
r
th
e
p
o
we
r
g
rid
i
n
t
h
e
fu
t
u
re
.
K
ey
w
o
r
d
s
:
B
lo
ck
ch
ain
C
o
n
tin
g
en
cy
a
n
aly
s
is
Ma
ch
in
e
l
ea
r
n
in
g
Po
wer
g
r
id
s
ec
u
r
ity
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
:
R
av
i V
.
An
g
ad
i
Dep
ar
tm
en
t o
f
E
lectr
ical
an
d
E
lectr
o
n
ics E
n
g
in
ee
r
i
n
g
,
Pre
s
i
d
en
cy
Un
iv
er
s
ity
B
en
g
alu
r
u
,
Kar
n
atak
a
5
6
0
0
6
4
,
I
n
d
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E
m
ail:
r
av
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g
ad
i
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@
g
m
ail.
co
m
or
r
av
ian
g
ad
i@
p
r
esid
en
cy
u
n
iv
er
s
ity
.
in
1.
I
NT
RO
D
UCT
I
O
N
T
h
e
co
n
tem
p
o
r
a
r
y
p
o
wer
g
r
i
d
s
ar
e
b
ec
o
m
in
g
h
ig
h
l
y
d
ec
e
n
tr
alize
d
an
d
d
ata
-
d
r
iv
en
cy
b
er
-
p
h
y
s
ical
s
y
s
tem
s
.
T
h
e
tr
ad
itio
n
al
ce
n
tr
alize
d
d
esig
n
s
o
f
g
en
er
atio
n
,
tr
an
s
m
is
s
io
n
,
an
d
d
is
tr
ib
u
tio
n
ca
n
n
o
t
co
p
e
with
r
en
ewa
b
le
s
o
u
r
ce
s
,
elec
tr
ic
v
eh
icles,
s
m
ar
t
m
eter
s
,
an
d
d
i
s
tr
ib
u
ted
g
en
e
r
atio
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t
h
at
in
tr
o
d
u
ce
in
ter
m
itten
c
y
,
b
id
ir
ec
tio
n
al
f
lo
ws,
an
d
f
ast
f
lu
ctu
atio
n
s
in
d
em
an
d
v
ar
i
ab
ilit
y
[
1
]
,
[
2
]
.
T
o
o
v
er
co
m
e
th
ese,
p
r
ed
ictiv
e
an
aly
tics
,
r
ea
l
-
tim
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m
o
n
ito
r
in
g
,
an
d
au
to
m
atio
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s
ar
e
p
ar
t
o
f
s
m
ar
t
g
r
id
p
r
o
g
r
am
s
.
Ma
ch
in
g
lear
n
in
g
(
ML
)
in
v
o
lv
es
h
is
to
r
ical
an
d
r
ea
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-
tim
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d
ata,
wh
ic
h
it
an
aly
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s
t
o
d
etec
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alies,
f
o
r
ec
ast
d
e
m
an
d
,
a
n
d
p
r
ed
ict
f
au
lts
[
3
]
.
Similar
ly
,
b
lo
ck
c
h
ain
tech
n
o
lo
g
y
is
ac
tiv
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r
e
s
ea
r
ch
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s
af
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d
ec
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n
tr
alize
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in
ce
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tiv
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o
f
en
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g
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wh
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f
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im
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u
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co
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m
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icatio
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to
m
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tic
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ac
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s
b
y
u
s
in
g
s
m
ar
t
co
n
tr
ac
ts
[
4
]
.
Mo
s
t
r
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en
t
s
tu
d
ies
h
av
e
talk
ed
ab
o
u
t
b
lo
c
k
ch
ain
an
d
m
ac
h
in
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lear
n
in
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to
g
eth
er
n
ess
in
th
e
f
u
tu
r
e
en
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g
y
s
y
s
tem
s
.
Mo
lo
lo
th
et
a
l.
[
5
]
s
h
o
wed
th
at
th
e
y
ca
n
f
ac
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ate
s
af
e
tr
ad
in
g
o
f
en
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g
y
a
s
well
as
s
m
ar
t g
r
id
co
n
tr
o
l,
an
d
Ven
k
atesan
an
d
R
ah
ay
u
[
6
]
in
t
r
o
d
u
ce
d
h
y
b
r
id
co
n
s
en
s
u
s
an
d
ML
m
o
d
els
to
en
h
an
ce
th
e
s
ec
u
r
ity
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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I
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No
.
1
,
Ma
r
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20
2
6
:
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1
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4
2
9
422
o
f
b
lo
c
k
ch
ain
s
.
Nev
e
r
th
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m
o
s
t
m
eth
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v
ice
attac
k
(
D
o
S)
[
7
]
.
Mo
r
eo
v
er
,
ce
n
tr
alize
d
s
y
s
tem
s
ar
e
s
in
g
le
p
o
in
t
o
f
f
ailu
r
e
an
d
ca
n
n
o
t
th
er
ef
o
r
e
b
e
d
ee
m
e
d
r
eliab
le
wh
en
d
ea
lin
g
with
h
ig
h
-
f
r
eq
u
en
cy
an
d
lo
w
-
laten
cy
e
n
v
ir
o
n
m
en
ts
.
T
o
m
itig
ate
s
u
ch
s
h
o
r
tco
m
in
g
s
,
th
is
p
ap
er
o
u
tlin
es
a
h
y
b
r
id
s
y
s
tem
,
wh
ich
is
th
e
in
te
g
r
atio
n
o
f
m
ac
h
in
e
lear
n
i
n
g
-
in
f
u
s
ed
p
r
e
d
ictiv
e
an
aly
tics
an
d
b
lo
ck
c
h
ain
-
en
ab
le
d
au
to
m
atio
n
.
R
a
n
d
o
m
f
o
r
est
(
R
F)
,
s
u
p
p
o
r
t
v
ec
t
o
r
m
ac
h
i
n
e
(
SVM)
,
an
d
g
r
ad
ien
t
b
o
o
s
tin
g
(
G
B
)
m
o
d
els
will
b
e
tr
ain
ed
u
s
in
g
a
g
r
id
p
ar
am
eter
-
b
ased
d
ata
s
et
s
u
ch
as
v
o
ltag
e
s
,
ac
tiv
e/r
ea
ctiv
e
p
o
wer
,
an
d
p
h
ase
an
g
les
th
at
class
if
y
f
au
lt si
tu
atio
n
s
u
s
in
g
th
e
I
E
E
E
3
0
-
b
u
s
s
y
s
tem
.
Me
an
w
h
ile,
cr
y
p
to
g
r
ap
h
ic
alg
o
r
ith
m
s
s
u
ch
as
a
d
v
an
ce
d
e
n
cr
y
p
tio
n
s
tan
d
ar
d
(
AE
S
)
,
R
iv
est
–
Sh
am
ir
–
Ad
lem
an
(
R
SA
)
,
an
d
ellip
tic
cu
r
v
e
cr
y
p
t
o
g
r
ap
h
y
(
E
C
C
)
ar
e
also
b
ein
g
ex
am
in
ed
in
th
e
co
n
tex
t
o
f
p
e
r
m
is
s
io
n
ed
b
lo
ck
ch
ain
to
p
r
o
tect
th
e
d
ata
an
d
in
itiate
th
e
s
m
ar
t
co
n
tr
a
ct
-
b
ased
au
to
m
atic
r
esp
o
n
s
e.
T
h
e
o
r
i
g
in
ality
o
f
t
h
is
wo
r
k
is
th
at
it
h
as
a
two
-
tier
ar
ch
itectu
r
e,
wh
er
e
ML
m
o
d
els
will
p
r
ed
ict
ea
r
ly
f
a
u
lts
,
an
d
b
l
o
ck
ch
ain
w
ill
en
s
u
r
e
t
h
at
m
itig
atio
n
m
ea
s
u
r
es
ar
e
im
p
lem
en
ted
s
af
ely
an
d
a
u
to
n
o
m
o
u
s
ly
.
T
o
g
eth
er
,
th
e
i
n
tellig
en
ce
a
n
d
tr
u
s
t
lay
e
r
ca
n
d
ec
r
ea
s
e
r
esp
o
n
s
e
tim
e,
im
p
r
o
v
e
class
if
icatio
n
ac
cu
r
ac
y
,
a
n
d
im
p
r
o
v
e
r
esil
ien
cy
.
T
h
e
p
ap
e
r
is
s
tr
u
ctu
r
e
d
as
f
o
llo
ws:
s
ec
tio
n
2
will
p
r
esen
t
r
elate
d
wo
r
k
,
s
ec
tio
n
3
will
in
clu
d
e
all
th
e
in
f
o
r
m
atio
n
ab
o
u
t
th
e
m
eth
o
d
o
lo
g
y
o
f
d
ata
s
im
u
latio
n
,
tr
ain
in
g
o
f
th
e
ML
m
o
d
el,
an
d
th
e
u
s
e
o
f
b
l
o
ck
ch
ain
,
s
ec
tio
n
4
will
d
is
cu
s
s
th
e
r
esu
lts
,
an
d
t
h
e
co
n
clu
s
io
n
will
b
e
g
iv
en
in
s
ec
tio
n
5
b
ased
o
n
th
e
m
ain
f
in
d
in
g
s
an
d
s
o
m
e
s
u
g
g
e
s
tio
n
s
o
n
th
e
f
u
r
th
er
d
ir
ec
tio
n
s
.
2.
L
I
T
E
R
AT
U
RE
R
E
VI
E
W
A
ND
P
RO
P
O
SE
D
AP
P
RO
AC
H
T
o
m
an
ag
e
t
h
e
g
r
o
win
g
co
m
p
lex
ity
,
s
m
ar
t
g
r
i
d
s
r
eq
u
ir
e
s
m
ar
t
co
n
tr
o
l,
r
e
al
-
tim
e
an
a
ly
tics
,
an
d
au
to
m
ated
p
r
o
tectio
n
.
Ma
ch
i
n
e
lea
r
n
in
g
(
ML
)
h
as
tu
r
n
e
d
o
u
t
to
b
e
v
alu
ab
le
in
d
etec
tin
g
p
r
ed
ictiv
e
f
au
lts
,
wh
ich
allo
ws
lar
g
e
s
ca
les
o
f
d
ata
an
al
y
s
is
r
eg
ar
d
in
g
o
p
e
r
atio
n
s
.
T
h
e
wea
k
n
ess
es
o
f
co
n
v
en
tio
n
al
f
a
u
lt
an
aly
s
is
wer
e
m
en
tio
n
ed
b
y
L
ian
g
[
1
]
an
d
C
h
an
d
r
an
et
a
l
.
[
2
]
,
wh
o
p
o
in
ted
o
u
t
th
e
p
r
o
b
lem
o
f
r
en
ewa
b
le
in
teg
r
atio
n
an
d
th
e
n
ec
ess
ity
o
f
ad
ap
tiv
e
f
au
lt r
esp
o
n
s
e.
B
lo
ck
ch
ain
h
as
p
r
o
v
ed
t
o
b
e
a
s
o
lu
tio
n
to
s
ec
u
r
e
an
d
d
ec
en
tr
alize
d
en
e
r
g
y
m
an
ag
e
m
en
t.
As
d
em
o
n
s
tr
ated
b
y
s
tu
d
ies
b
y
So
n
g
et
a
l.
[
3
]
an
d
C
h
o
o
b
in
eh
et
a
l
.
[
4
]
,
it
ca
n
im
p
r
o
v
e
t
r
an
s
p
ar
en
c
y
,
au
th
en
ticatio
n
,
a
n
d
r
esis
tan
ce
to
cy
b
er
-
attac
k
s
.
Sm
ar
t
co
n
tr
ac
ts
also
au
to
m
ate
ac
tiv
ities
in
clu
d
in
g
f
a
u
lt
is
o
latio
n
an
d
lo
ad
m
an
ag
e
m
en
t w
ith
o
u
t th
e
n
ee
d
o
f
ce
n
tr
aliz
ed
m
an
ip
u
latio
n
.
T
h
er
e
is
litt
le
liter
atu
r
e
o
n
c
o
m
b
in
in
g
ML
-
b
ased
p
r
ed
icti
o
n
a
n
d
b
lo
ck
c
h
ain
-
s
ec
u
r
e
d
a
u
to
m
atio
n
.
T
h
eo
r
etica
l
ad
v
an
tag
es
o
f
in
t
eg
r
atio
n
wer
e
d
is
cu
s
s
ed
b
y
Mo
lo
lo
th
et
a
l.
[
5
]
,
a
n
d
h
y
b
r
id
co
n
s
en
s
u
s
t
h
at
is
im
p
r
o
v
e
d
with
th
e
h
elp
o
f
ML
is
d
i
s
cu
s
s
ed
b
y
Ven
k
atesan
an
d
R
ah
ay
u
[
6
]
.
Nev
er
th
eless
,
m
o
s
t
m
eth
o
d
o
l
o
g
ies
ad
d
r
ess
an
aly
t
ics
an
d
s
ec
u
r
ity
in
d
ep
en
d
en
tl
y
,
wh
ich
leav
es
a
n
o
p
p
o
r
tu
n
it
y
to
d
ev
el
o
p
s
in
g
le
f
r
am
ewo
r
k
s
with
th
e
ab
ilit
y
t
o
p
r
o
v
id
e
r
ea
l
-
tim
e
f
au
lt
p
r
e
d
ictio
n
an
d
p
r
e
v
en
tiv
e
m
itig
a
tio
n
with
s
u
f
f
icien
t
s
ec
u
r
ity
.
T
h
e
g
iv
en
wo
r
k
f
ills
th
at
g
ap
an
d
p
r
esen
ts
a
h
y
b
r
id
ML
-
b
lo
ck
ch
ai
n
in
f
r
astru
ctu
r
e
im
p
lem
en
ted
o
n
th
e
I
E
E
E
3
0
-
b
u
s
s
y
s
tem
an
d
p
r
o
v
id
in
g
p
r
e
d
ictiv
e
in
tellig
en
ce
an
d
au
to
m
atio
n
b
ased
o
n
cy
b
er
-
s
ec
u
r
it
y
.
T
ab
le
1
ex
p
lain
s
th
e
ass
o
ciate
d
ter
m
s
an
d
ac
r
o
n
y
m
s
.
T
ab
le
1
.
T
er
m
in
o
lo
g
y
an
d
ac
r
o
n
y
m
s
A
b
b
r
e
v
i
a
t
i
o
n
D
e
scri
p
t
i
o
n
A
b
b
r
e
v
i
a
t
i
o
n
D
e
scri
p
t
i
o
n
A
ES
A
d
v
a
n
c
e
d
e
n
c
r
y
p
t
i
o
n
st
a
n
d
a
r
d
(
sy
mm
e
t
r
i
c
e
n
c
r
y
p
t
i
o
n
)
GB
G
r
a
d
i
e
n
t
b
o
o
s
t
i
n
g
I
o
T
I
n
t
e
r
n
e
t
o
f
t
h
i
n
g
s
EC
C
El
l
i
p
t
i
c
c
u
r
v
e
c
r
y
p
t
o
g
r
a
p
h
y
(
l
i
g
h
t
w
e
i
g
h
t
a
s
y
mm
e
t
r
i
c
e
n
c
r
y
p
t
i
o
n
)
RF
R
a
n
d
o
m f
o
r
e
s
t
ML
M
a
c
h
i
n
e
l
e
a
r
n
i
n
g
R
S
A
R
i
v
e
s
t
–
S
h
a
m
i
r
–
A
d
l
e
ma
n
S
V
M
S
u
p
p
o
r
t
v
e
c
t
o
r
ma
c
h
i
n
e
T
h
e
s
u
g
g
ested
ar
c
h
itectu
r
e
c
o
m
b
in
es
th
e
ML
-
b
ased
p
r
e
d
ictio
n
o
f
f
au
lts
with
t
h
e
s
ec
u
r
e
a
u
to
m
atio
n
b
ased
o
n
b
lo
ck
c
h
ain
tech
n
o
lo
g
y
.
L
iv
e
g
r
i
d
m
ea
s
u
r
em
en
ts
o
f
v
o
ltag
es,
p
o
wer
f
lo
ws,
tr
an
s
f
o
r
m
er
/lo
a
d
co
n
d
itio
n
ar
e
in
p
u
t
to
ML
m
o
d
els
tr
ain
ed
o
n
f
a
u
lts
in
th
e
I
E
E
E
3
0
-
b
u
s
co
n
d
itio
n
o
n
r
an
d
o
m
f
o
r
est
,
SVM,
an
d
g
r
ad
ien
t b
o
o
s
ti
n
g
.
A
p
e
r
m
is
s
io
n
ed
b
lo
ck
c
h
ain
g
u
ar
an
tees
th
e
s
ec
u
r
e
tr
an
s
f
er
o
f
d
ata
a
n
d
a
u
to
m
atic
o
p
e
r
atio
n
s
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ap
p
l Po
wer
E
n
g
I
SS
N:
2252
-
8
7
9
2
E
n
h
a
n
ci
n
g
p
o
w
er g
r
id
r
elia
b
ilit
y:
a
h
yb
r
id
b
lo
ck
ch
a
i
n
a
n
d
ma
ch
in
e
lea
r
n
in
g
a
p
p
r
o
a
ch
(
R
a
vi
V
.
A
n
g
a
d
i
)
423
with
s
m
ar
t
co
n
tr
ac
ts
to
is
o
late
f
au
lts
,
s
h
ed
d
in
g
th
e
lo
ad
,
an
d
r
ea
s
s
ig
n
in
g
g
en
e
r
at
o
r
s
.
C
r
y
p
to
g
r
a
p
h
ic
alg
o
r
ith
m
s
(
AE
S,
R
SA,
E
C
C
)
ar
e
co
m
p
ar
ed
b
ased
o
n
co
n
f
id
en
tiality
an
d
in
teg
r
ity
,
an
d
E
C
C
h
as
th
e
b
es
t
p
er
f
o
r
m
an
ce
-
s
ec
u
r
ity
tr
ad
e
-
o
f
f
.
T
h
e
a
r
ch
itectu
r
e
o
f
f
er
s
a
p
r
ed
ictiv
e,
d
ec
en
tr
alize
d
,
an
d
c
y
b
er
-
s
ec
u
r
e
m
eth
o
d
o
f
co
n
tin
g
en
cy
m
an
ag
e
m
en
t t
h
at
o
f
f
er
s
a
b
etter
r
esp
o
n
s
e
tim
e,
ac
cu
r
ac
y
,
an
d
r
esil
ien
ce
.
3.
M
E
T
H
O
D
T
h
e
s
u
g
g
ested
m
eth
o
d
o
lo
g
y
i
s
a
co
m
b
in
atio
n
o
f
m
ac
h
in
e
le
ar
n
in
g
t
o
p
r
ed
ict
f
a
u
lts
with
b
lo
ck
ch
ain
-
b
ased
s
ec
u
r
e
au
to
m
atio
n
:
a
t
h
r
ee
-
tier
s
tr
u
ctu
r
e
is
d
ata
s
im
u
latio
n
,
ML
-
b
ased
p
r
ed
ictio
n
,
a
n
d
b
lo
c
k
ch
ain
with
cr
y
p
to
g
r
ap
h
ic
p
r
o
tectio
n
an
d
s
m
ar
t
co
n
tr
ac
ts
.
T
h
e
g
en
er
a
l
p
r
o
ce
s
s
f
lo
w
,
as
d
ep
icted
i
n
Fig
u
r
e
1
,
en
tails
g
en
er
atio
n
o
f
th
e
d
ataset,
co
n
tin
g
en
c
y
s
im
u
latio
n
,
d
ev
el
o
p
m
en
t
o
f
th
e
ML
,
an
d
in
t
eg
r
atio
n
with
th
e
b
lo
ck
ch
ain
.
I
t
was
test
ed
o
n
t
h
e
I
E
E
E
3
0
-
b
u
s
s
y
s
tem
,
wh
ic
h
co
n
tain
s
3
0
b
u
s
es,
6
g
e
n
er
at
o
r
s
,
an
d
4
1
li
n
es
o
f
tr
an
s
m
is
s
io
n
,
b
ec
au
s
e
it
is
b
alan
ce
d
an
d
ca
n
b
e
u
s
ed
as
a
test
b
ed
in
co
n
tin
g
e
n
cy
s
t
u
d
ies
[
8
]
,
[
9
]
.
T
h
e
MiPo
wer
was
u
s
ed
to
s
im
u
late
f
au
lt
s
ce
n
ar
io
s
s
u
ch
as
tr
an
s
m
is
s
io
n
lin
e
o
u
tag
es,
g
en
er
ato
r
f
ailu
r
es
an
d
tr
an
s
f
o
r
m
er
d
is
tu
r
b
an
ce
s
.
Par
a
m
eter
s
o
f
b
u
s
v
o
ltag
es,
ac
tiv
e/r
ea
ctiv
e
p
o
wer
,
a
n
d
p
h
ase
an
g
les
wer
e
m
ea
s
u
r
ed
d
u
r
in
g
ea
c
h
r
u
n
[
1
0
]
.
E
ac
h
r
o
w
o
f
th
e
d
ataset
h
o
ld
s
a
f
au
lt
t
y
p
e,
lo
ca
tio
n
,
an
d
s
ev
er
it
y
as
a
lab
eled
g
r
id
s
tate.
E
x
tr
ac
tin
g
f
ea
tu
r
es
th
at
wer
e
co
m
p
atib
le
with
th
e
latest
p
o
wer
-
s
y
s
tem
ML
r
esear
ch
[
1
1
]
,
[
1
2
]
,
th
e
f
in
al
tr
ain
in
g
d
ataset
was
b
u
ilt
wit
h
th
em
.
T
h
is
s
y
s
tem
atic
p
ip
ewo
r
k
g
u
ar
an
tees
r
e
p
r
o
d
u
cib
le
an
d
all
-
in
cl
u
s
iv
e
em
u
latio
n
o
f
v
ar
ied
f
au
lt c
o
n
d
itio
n
s
[
1
3
]
.
Fig
u
r
e
1.
Pro
ce
s
s
f
lo
w
f
o
r
b
u
il
d
in
g
d
atasets
an
d
s
im
u
latin
g
f
au
lt scen
ar
io
s
with
th
e
I
E
E
E
3
0
-
b
u
s
p
o
wer
s
y
s
tem
T
h
r
ee
tr
ain
e
d
ML
m
o
d
els,
in
clu
d
in
g
r
an
d
o
m
f
o
r
est
,
SVM,
an
d
g
r
ad
ien
t
b
o
o
s
tin
g
,
wer
e
cr
ea
ted
to
ca
teg
o
r
ize
g
r
id
f
a
u
lts
.
R
F,
wh
ich
is
a
g
r
o
u
p
o
f
d
ec
is
io
n
tr
e
es,
m
in
im
izes
o
v
er
f
itti
n
g
an
d
th
e
r
esu
lts
m
ay
b
e
in
ter
p
r
eted
th
r
o
u
g
h
f
ea
tu
r
e
im
p
o
r
tan
ce
,
m
ak
in
g
it
r
esis
tan
t
to
n
o
is
e
an
d
u
n
b
alan
ce
d
d
a
ta
[
1
4
]
,
[
1
5
]
.
SVM
f
in
d
s
th
e
b
est
s
ep
ar
atin
g
h
y
p
e
r
p
lan
es
an
d
class
if
ies
n
o
n
lin
e
ar
p
atter
n
s
u
s
in
g
R
B
F
k
er
n
els
,
an
d
t
h
u
s
class
if
ies
th
e
co
m
p
lex
g
r
id
b
eh
a
v
io
r
[
1
6
]
.
Gr
ad
ien
t
b
o
o
s
tin
g
,
a
p
r
o
g
r
ess
iv
e
en
s
em
b
le,
co
llab
o
r
ate
s
with
th
e
p
r
ev
io
u
s
lear
n
er
s
an
d
g
r
ab
s
g
r
id
s
ea
r
ch
to
o
p
tim
ize
d
ep
th
an
d
lear
n
in
g
r
ate,
im
p
r
o
v
in
g
t
h
e
r
ec
all
o
f
th
e
m
in
o
r
ity
f
a
u
lt
class
es [
1
7
]
.
Fo
r
m
ally
,
g
iv
en
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(
1
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n
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as in
(
2
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.
:
→
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(2
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T
h
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ec
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r
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R
F
an
d
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e
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e
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o
d
to
o
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in
s
m
ar
t
-
g
r
id
f
a
u
lt
an
aly
tics
[
1
8
]
,
[
1
9
]
.
T
h
e
cr
y
p
to
g
r
ap
h
ic
ev
alu
atio
n
f
r
a
m
ewo
r
k
is
p
r
o
v
id
ed
i
n
Fig
u
r
es
2
(
a
)
an
d
2
(
b
)
,
wh
er
e
t
h
e
AE
S,
R
SA
,
an
d
E
C
C
ar
e
c
o
m
p
ar
ed
i
n
ter
m
s
o
f
r
eso
u
r
ce
ef
f
icien
cy
,
lev
el
o
f
s
ec
u
r
ity
,
an
d
s
p
ee
d
o
f
en
cr
y
p
tio
n
.
Sy
m
m
etr
ic
cip
h
e
r
is
ca
lled
AE
S,
an
d
it
is
f
ast
with
lo
w
laten
cy
b
u
t
h
as
d
if
f
icu
lties
in
k
e
y
m
an
a
g
em
en
t
d
ec
en
tr
aliza
tio
n
[
2
0
]
.
R
SA
is
an
asy
m
m
etr
ic
alg
o
r
ith
m
,
wh
ich
m
ea
n
s
th
at
it
is
v
er
y
s
ec
u
r
e
an
d
r
e
q
u
ir
es
a
h
ig
h
lev
el
o
f
co
m
p
u
tatio
n
o
v
er
h
ea
d
,
wh
ich
r
estricts
its
p
r
ac
tical
u
s
e
in
r
ea
l
-
tim
e
[
2
1
]
.
E
C
C
is
an
asy
m
m
etr
ic
s
ch
em
e,
b
ein
g
lig
h
tw
eig
h
t
an
d
o
f
f
er
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g
th
e
s
am
e
lev
el
o
f
R
SA
s
ec
u
r
ity
u
s
in
g
s
m
aller
k
ey
s
,
h
e
n
ce
it is
s
u
itab
le
in
th
e
ca
s
e
o
f
I
o
T
-
b
as
ed
p
o
wer
s
y
s
tem
s
.
E
C
C
en
cr
y
p
tio
n
as
in
(
4
)
:
=
(
,
+
)
(
4
)
is
ef
f
ec
tiv
e
an
d
s
af
e
b
ec
au
s
e
th
e
ellip
tic
cu
r
v
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is
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ete
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g
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ith
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p
r
o
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lem
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to
u
g
h
[
2
2
]
,
[
2
3
]
.
E
C
C
b
ec
am
e
an
o
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tim
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m
p
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m
is
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etwe
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er
f
o
r
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ce
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d
s
ec
u
r
ity
wh
e
n
it
ca
m
e
to
th
e
b
lo
ck
ch
ain
-
b
a
s
ed
d
ata
ex
ch
an
g
e
.
T
h
e
s
m
ar
t
co
n
tr
ac
t
lay
er
m
ak
es
th
e
o
p
er
atio
n
s
s
ec
u
r
e
an
d
au
d
itab
le
b
ec
au
s
e
it
au
to
m
ates
th
e
o
p
er
atio
n
s
,
in
clu
d
in
g
f
a
u
lt
is
o
latio
n
,
lo
ad
s
h
ed
d
in
g
,
an
d
re
-
d
is
p
atch
in
g
o
f
g
en
er
ato
r
s
o
n
ce
th
e
ML
m
o
d
els
p
r
ed
ict
th
e
f
au
lts
.
E
v
er
y
o
p
er
atio
n
is
s
to
r
ed
o
n
th
e
au
th
o
r
ized
b
lo
c
k
ch
ain
,
wh
ich
r
em
o
v
es
th
e
d
elay
d
u
r
i
n
g
th
e
m
a
n
u
al
p
r
o
ce
s
s
in
g
an
d
in
cr
ea
s
es th
e
r
esil
ien
ce
[
2
4
]
-
[
2
7
]
.
T
h
e
b
lo
c
k
ch
ain
a
n
d
m
ac
h
in
e
l
ea
r
n
in
g
c
o
m
p
o
n
en
ts
o
f
th
e
p
r
o
p
o
s
ed
s
tr
u
ctu
r
e
co
m
p
lem
en
t
ea
ch
o
th
er
as
d
em
o
n
s
tr
ated
in
Fig
u
r
e
3
to
d
ev
elo
p
an
a
u
to
m
ated
,
s
ec
u
r
e,
an
d
d
y
n
am
ic
s
y
s
te
m
o
f
p
o
wer
g
r
id
m
an
ag
em
en
t.
Pre
d
ictiv
e
f
a
u
lt
d
etec
tio
n
:
m
ac
h
in
e
lear
n
in
g
m
o
d
els,
s
p
ec
if
ically
,
th
e
r
a
n
d
o
m
f
o
r
est,
SVM,
an
d
g
r
ad
ien
t b
o
o
s
tin
g
class
if
ier
s
,
p
r
o
ce
s
s
r
ea
l
-
tim
e
g
r
id
d
ata
s
tr
e
am
s
co
n
tin
u
o
u
s
ly
.
T
h
ese
m
o
d
els p
r
ed
ict
p
o
ten
tial
m
alf
u
n
ctio
n
s
s
u
ch
as th
ese
th
r
o
u
g
h
t
h
e
p
r
e
v
io
u
s
s
y
s
tem
tr
en
d
s
an
d
p
atter
n
s
.
L
astl
y
,
th
e
ML
a
n
d
b
lo
ck
c
h
ain
lay
er
s
w
o
r
k
to
g
eth
e
r
t
o
p
r
o
v
id
e
p
r
e
d
ictiv
e
an
d
s
e
cu
r
e
g
r
id
m
an
ag
em
en
t.
ML
m
o
d
els d
o
r
ea
l
-
tim
e
in
f
er
e
n
ce
o
n
s
en
s
o
r
d
ata
co
n
tin
u
o
u
s
ly
an
d
id
en
tif
y
ea
r
ly
war
n
i
n
g
s
ig
n
s
o
f
lin
e
o
u
tag
es,
g
e
n
er
ato
r
f
ail
u
r
es,
an
d
lo
ad
im
b
alan
ce
.
T
h
e
b
lo
ck
ch
ain
will
ac
tiv
ate
s
m
ar
t
co
n
tr
ac
ts
to
tak
e
r
em
ed
ial
m
ea
s
u
r
es
wh
en
o
u
t
s
tan
d
in
g
p
atter
n
s
ar
e
o
b
s
er
v
e
d
.
T
h
is
u
n
if
ied
p
ip
elin
e
ca
n
g
u
ar
an
tee
tam
p
er
-
r
esis
tan
t
lo
g
s
,
s
af
e
o
p
er
atio
n
,
f
u
ll
tr
ac
ea
b
ilit
y
,
an
d
m
alicio
u
s
r
esis
tan
ce
to
r
eo
r
g
an
izatio
n
-
tim
ely
,
co
r
r
ec
t,
an
d
d
ep
en
d
a
b
le
co
n
tin
g
en
c
y
r
es
p
o
n
s
e
th
r
o
u
g
h
th
e
s
y
n
th
esis
o
f
d
ata
-
b
ased
f
o
r
ec
asti
n
g
an
d
d
ec
en
tr
alize
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I
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ased
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2252
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8
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429
B
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RAP
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AUTH
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Dr
.
Ra
v
i
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.
Ang
a
d
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h
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E.
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rin
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VTU,
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lag
a
v
i,
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rn
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tak
a
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d
ia)
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2
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h
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d
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g
re
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o
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a
n
tap
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r
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ia)
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fro
m
P
re
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n
g
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tl
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h
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s
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k
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e
m
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d
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c
a
n
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tac
ted
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m
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y
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s
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re
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o
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k
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s.
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larly
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tac
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