I
nte
rna
t
io
na
l J
o
urna
l o
f
E
lect
rica
l a
nd
Co
m
pu
t
er
E
ng
ineering
(
I
J
E
CE
)
Vo
l.
15
,
No
.
1
,
Feb
r
u
ar
y
20
25
,
p
p
.
1
1
0
9
~
1
1
2
2
I
SS
N:
2088
-
8
7
0
8
,
DOI
: 1
0
.
1
1
5
9
1
/ijece.
v
15
i
1
.
pp
1
1
0
9
-
1
1
2
2
1109
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//ij
ec
e.
ia
esco
r
e.
co
m
Sa
ilfish
-
ca
t
a
lg
o
ri
thm
-
en
ha
nced
g
e
nera
tive a
dv
ersa
ria
l net
wo
rk
for a
tt
a
ck
dete
cti
o
n in
int
ernet
of
t
hing
s
-
F
o
g
net
wo
rk
a
uthent
ica
tion
P
a
lla
v
i K
a
ntha
m
a
ng
a
l
a
N
ira
nja
n
1
,
Ra
v
ik
um
a
r
V
enk
a
t
esh
2
1
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
,
N
M
A
M
I
n
st
i
t
u
t
e
o
f
T
e
c
h
n
o
l
o
g
y
,
N
I
TTE
(
D
e
e
me
d
t
o
b
e
U
n
i
v
e
r
si
t
y
)
,
N
i
t
t
e
,
I
n
d
i
a
2
D
e
p
a
r
t
me
n
t
o
f
I
n
f
o
r
mat
i
o
n
S
c
i
e
n
c
e
a
n
d
E
n
g
i
n
e
e
r
i
n
g
,
V
i
d
y
a
v
a
r
d
h
a
k
a
C
o
l
l
e
g
e
o
f
E
n
g
i
n
e
e
r
i
n
g
,
M
y
s
u
r
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
Ma
y
1
3
,
2
0
2
4
R
ev
is
ed
Au
g
8
,
2
0
2
4
Acc
ep
ted
Sep
3
,
2
0
2
4
Th
e
i
n
tern
e
t
o
f
t
h
in
g
s
(I
o
T)
h
a
s
e
m
e
rg
e
d
a
s
a
p
r
o
m
in
e
n
t
a
n
d
in
flu
e
n
ti
a
l
c
o
n
c
e
p
t
with
in
t
h
e
re
a
lm o
f
c
o
m
p
u
ti
n
g
.
Va
rio
u
s a
tt
a
c
k
d
e
tec
ti
o
n
m
e
th
o
d
s a
re
d
e
v
ise
d
fo
r
d
e
tec
ti
n
g
a
tt
a
c
k
s
in
Io
T
-
F
o
g
e
n
v
iro
n
m
e
n
t.
De
sp
it
e
a
ll
th
e
se
e
ffo
rts,
a
tt
a
c
k
d
e
tec
ti
o
n
stil
l
re
m
a
in
e
d
a
s
a
c
h
a
ll
e
n
g
in
g
tas
k
d
u
e
to
fa
c
to
rs
su
c
h
a
s
lo
w
late
n
c
y
,
re
so
u
rc
e
c
o
n
stra
in
ts
o
f
Io
T
d
e
v
ice
s,
sc
a
lab
il
it
y
issu
e
s,
a
n
d
d
istri
b
u
ti
o
n
c
o
m
p
lex
it
ies
.
All
th
e
se
c
h
a
ll
e
n
g
e
s
a
re
a
d
d
re
ss
e
d
in
t
h
is
p
a
p
e
r
b
y
d
e
si
g
n
i
n
g
a
n
e
fficie
n
t
a
tt
a
c
k
d
e
tec
ti
o
n
tec
h
n
i
q
u
e
n
a
m
e
d
a
s
sa
il
fish
-
c
a
t
o
p
ti
m
iza
ti
o
n
-
b
a
se
d
g
e
n
e
ra
ti
v
e
a
d
v
e
rsa
rial
n
e
two
rk
(S
a
CO
-
b
a
se
d
G
AN
)
tailo
re
d
f
o
r
t
h
e
Io
T
-
F
o
g
fra
m
e
wo
rk
.
Th
is
p
ro
p
o
se
d
a
p
p
r
o
a
c
h
i
n
tr
o
d
u
c
e
s
t
h
e
S
a
CO
-
b
a
se
d
G
AN
fo
r
Io
T
-
F
o
g
a
t
tac
k
d
e
tec
ti
o
n
u
ti
li
z
in
g
d
e
e
p
lea
rn
in
g
a
n
d
fe
a
tu
re
-
b
a
se
d
c
las
sifica
ti
o
n
,
v
a
li
d
a
ted
th
r
o
u
g
h
e
x
p
e
rime
n
ts
sh
o
wi
n
g
su
p
e
rio
r
p
e
rfo
rm
a
n
c
e
m
e
tri
c
s.
No
tab
l
y
,
t
h
e
S
a
CO
o
p
t
imiz
a
ti
o
n
tec
h
n
i
q
u
e
i
s
u
ti
li
z
e
d
to
trai
n
th
e
G
AN
.
Ex
p
e
rime
n
tal
re
su
lt
s
d
e
m
o
n
stra
te
th
e
e
ffica
c
y
o
f
t
h
e
S
a
CO
-
b
a
se
d
G
AN
with
a
m
a
x
imu
m
re
c
a
ll
o
f
9
2
.
1
5
%
,
a
m
a
x
im
u
m
p
re
c
isio
n
o
f
9
1
.
2
1
%
,
a
n
d
a
m
a
x
imu
m
F
-
M
e
a
su
re
o
f
9
2
.
1
6
%
,
o
u
t
p
e
rfo
rm
i
n
g
e
x
isti
n
g
tec
h
n
iq
u
e
s
in
Io
T
-
F
o
g
a
tt
a
c
k
d
e
t
e
c
ti
o
n
.
T
h
e
p
a
p
e
r
re
c
o
m
m
e
n
d
s
e
n
h
a
n
c
i
n
g
sc
a
lab
il
it
y
,
imp
lem
e
n
ti
n
g
re
a
l
-
ti
m
e
d
e
t
e
c
ti
o
n
stra
teg
ies
,
rig
o
r
o
u
sly
tes
ti
n
g
ro
b
u
stn
e
ss
a
g
a
in
st
d
i
v
e
rse
a
tt
a
c
k
sc
e
n
a
rio
s,
a
n
d
in
te
g
ra
ti
n
g
with
e
x
isti
n
g
I
o
T
se
c
u
rit
y
fra
m
e
wo
rk
s fo
r
p
ra
c
ti
c
a
l
d
e
p
lo
y
m
e
n
t.
K
ey
w
o
r
d
s
:
Attack
d
etec
tio
n
Fo
g
co
m
p
u
tin
g
G
e
n
e
r
a
t
i
v
e
a
d
v
e
r
s
a
r
i
al
n
e
t
w
o
r
k
s
I
n
ter
n
et
o
f
t
h
in
g
s
Op
tim
izatio
n
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
:
Pallav
i K
an
th
am
an
g
ala
Nir
an
j
an
Dep
ar
tm
en
t o
f
C
o
m
p
u
ter
Scie
n
ce
an
d
E
n
g
in
ee
r
in
g
,
NM
AM
I
n
s
titu
te
o
f
T
ec
h
n
o
lo
g
y
,
NI
T
T
E
(
Dee
m
ed
to
b
e
Un
iv
er
s
ity
)
Kar
n
atak
a,
Nitte,
5
7
4
1
1
0
,
I
n
d
i
a
E
m
ail:
p
allav
i@
n
itte.e
d
u
.
in
1.
I
NT
RO
D
UCT
I
O
N
T
h
e
a
u
t
h
e
n
t
i
ca
t
e
d
n
et
w
o
r
k
o
f
t
h
e
i
n
t
e
r
n
e
t
o
f
t
h
i
n
g
s
F
o
g
(
I
oT
-
F
o
g
)
e
n
v
i
r
o
n
m
e
n
t
is
a
c
o
m
p
l
e
x
e
c
o
s
y
s
t
e
m
w
h
e
r
e
I
o
T
d
e
v
i
c
e
s
,
F
o
g
n
o
d
e
s
,
a
n
d
c
l
o
u
d
i
n
f
r
a
s
t
r
u
c
t
u
r
e
c
o
l
l
a
b
o
r
a
t
e
t
o
p
r
o
c
e
s
s
a
n
d
e
x
c
h
a
n
g
e
d
a
t
a
[
1
]
,
[
2
]
.
T
h
i
s
e
n
v
i
r
o
n
m
e
n
t
is
c
h
a
r
ac
t
e
r
i
z
e
d
b
y
i
ts
d
is
t
r
i
b
u
te
d
a
r
c
h
it
e
c
t
u
r
e
,
d
i
v
e
r
s
e
c
o
m
m
u
n
i
c
a
t
i
o
n
[
3
]
p
r
o
t
o
c
o
l
s
,
a
n
d
r
e
s
o
u
r
c
e
-
c
o
n
s
t
r
ai
n
e
d
d
e
v
i
c
e
s
.
A
u
t
h
e
n
t
i
c
at
i
o
n
m
e
c
h
a
n
is
m
s
p
la
y
a
v
i
t
a
l
r
o
l
e
i
n
v
e
r
i
f
y
i
n
g
t
h
e
i
d
e
n
t
i
t
y
o
f
e
n
t
it
i
es
w
i
t
h
i
n
t
h
e
n
et
w
o
r
k
a
n
d
e
n
s
u
r
i
n
g
s
e
c
u
r
e
d
a
t
a
t
r
a
n
s
m
i
s
s
i
o
n
as
s
h
o
w
n
i
n
F
i
g
u
r
e
1
.
O
p
ti
m
i
z
at
i
o
n
a
l
g
o
r
i
t
h
m
s
ai
m
t
o
e
n
h
a
n
c
e
t
h
e
e
f
f
i
c
i
e
n
c
y
a
n
d
e
f
f
e
c
t
i
v
e
n
es
s
o
f
v
a
r
i
o
u
s
p
r
o
ce
s
s
e
s
wi
t
h
i
n
t
h
is
e
n
v
i
r
o
n
m
e
n
t
,
i
n
c
l
u
d
i
n
g
a
t
t
ac
k
d
e
t
e
c
t
i
o
n
[
4
]
s
t
r
a
t
e
g
i
es
.
T
h
e
I
o
T
-
Fo
g
en
v
ir
o
n
m
en
t
o
p
er
ates
am
id
s
t
s
ig
n
if
ican
t
s
ca
l
e
an
d
co
m
p
lex
ity
,
en
c
o
m
p
ass
in
g
a
v
ast
ar
r
ay
o
f
in
te
r
co
n
n
ec
ted
d
ev
ic
es,
h
eter
o
g
e
n
eo
u
s
in
th
eir
ca
p
ab
ilit
ies
an
d
co
m
m
u
n
icatio
n
p
r
o
t
o
co
ls
[
5
]
.
T
h
is
d
iv
er
s
ity
p
o
s
es
s
u
b
s
tan
tial
c
h
allen
g
es
to
s
ca
lab
ilit
y
,
as
t
h
e
n
etwo
r
k
m
u
s
t
ef
f
icien
tly
ac
co
m
m
o
d
ate
th
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
15
,
No
.
1
,
Feb
r
u
ar
y
20
25
:
1
1
0
9
-
1
1
2
2
1110
ex
p
an
d
i
n
g
n
u
m
b
er
s
o
f
d
e
v
ice
s
wh
ile
m
ain
tain
in
g
r
o
b
u
s
t
p
e
r
f
o
r
m
a
n
ce
an
d
s
ec
u
r
ity
m
ea
s
u
r
es
[
6
]
.
Ma
n
ag
in
g
th
e
h
eter
o
g
en
eity
o
f
I
o
T
d
ev
ic
es
ad
d
s
an
o
th
er
lay
er
o
f
c
o
m
p
lex
ity
,
n
ec
ess
itatin
g
a
d
ap
tab
le
au
th
e
n
ticatio
n
a
n
d
d
etec
tio
n
s
tr
ateg
ies
tailo
r
ed
to
ea
ch
d
ev
ice'
s
u
n
iq
u
e
ch
ar
ac
ter
is
tics
.
Mo
r
eo
v
er
,
th
e
d
is
tr
ib
u
tio
n
o
f
d
ata
ac
r
o
s
s
Fo
g
n
o
d
es
an
d
clo
u
d
in
f
r
astru
ctu
r
e
in
tr
o
d
u
ce
s
co
m
p
lex
ities
in
m
ain
tain
in
g
d
ata
in
te
g
r
ity
an
d
co
n
f
id
en
tialit
y
[
7
]
,
d
em
an
d
i
n
g
s
o
p
h
is
ticated
s
ec
u
r
ity
p
r
o
to
co
ls
.
T
o
m
itig
ate
r
is
k
s
,
ad
v
an
ce
d
s
ec
u
r
ity
m
ea
s
u
r
es
s
u
ch
as
en
cr
y
p
tio
n
,
ac
ce
s
s
co
n
tr
o
l,
a
n
d
in
tr
u
s
io
n
d
etec
tio
n
s
y
s
te
m
s
ar
e
ess
en
tial
to
s
af
eg
u
ar
d
s
en
s
itiv
e
d
ata
a
n
d
en
s
u
r
e
th
e
r
eliab
ilit
y
o
f
co
m
m
u
n
icatio
n
s
with
in
th
is
in
tr
icate
ec
o
s
y
s
tem
.
Ad
d
r
ess
in
g
th
ese
ch
allen
g
es r
eq
u
ir
es
in
n
o
v
ativ
e
ap
p
r
o
ac
h
es
th
at
ca
n
d
y
n
am
ically
a
d
ju
s
t
to
th
e
ev
o
lv
in
g
lan
d
s
ca
p
e
o
f
I
o
T
-
Fo
g
en
v
ir
o
n
m
en
ts
[
8
]
,
en
s
u
r
in
g
b
o
th
o
p
er
atio
n
al
e
f
f
i
cien
cy
an
d
r
o
b
u
s
t p
r
o
tectio
n
a
g
ain
s
t e
m
er
g
in
g
th
r
ea
ts
.
Fig
u
r
e
1
.
Netwo
r
k
m
o
d
el
o
f
I
o
T
-
Fo
g
c
o
m
p
u
tin
g
T
h
e
f
o
cu
s
o
f
o
p
tim
izatio
n
al
g
o
r
ith
m
s
f
o
r
attac
k
d
etec
tio
n
s
tr
ateg
y
in
au
th
en
ticated
n
e
two
r
k
s
o
f
I
o
T
-
Fo
g
[
9
]
–
[
1
1
]
en
v
ir
o
n
m
e
n
ts
is
to
im
p
r
o
v
e
th
e
ef
f
icien
cy
an
d
ac
cu
r
ac
y
o
f
d
etec
ti
n
g
s
ec
u
r
ity
t
h
r
ea
ts
tar
g
etin
g
th
e
in
ter
co
n
n
ec
ted
d
ev
ices
an
d
in
f
r
astru
ctu
r
e
[
2
]
.
T
h
is
in
v
o
lv
es
lev
er
ag
in
g
o
p
ti
m
izatio
n
tech
n
iq
u
es
s
u
ch
as
g
en
etic
alg
o
r
ith
m
s
,
s
war
m
in
tellig
en
ce
,
an
d
ev
o
lu
t
io
n
ar
y
alg
o
r
ith
m
s
to
o
p
tim
ize
p
ar
am
eter
s
,
f
ea
tu
r
e
s
elec
tio
n
,
an
d
d
ec
is
io
n
-
m
a
k
in
g
p
r
o
ce
s
s
es
with
in
attac
k
d
etec
tio
n
s
y
s
tem
s
.
T
h
e
g
o
al
is
to
en
h
an
ce
t
h
e
d
etec
tio
n
ca
p
ab
ilit
ies
[
1
2
]
,
r
ed
u
ce
f
alse p
o
s
itiv
es,
an
d
m
i
n
im
ize
r
esp
o
n
s
e
tim
es to
s
ec
u
r
ity
in
cid
en
ts
.
Fu
r
th
er
m
o
r
e
,
r
eso
u
r
ce
lim
itat
io
n
s
with
in
I
o
T
d
e
v
ices
an
d
Fo
g
n
o
d
es
p
o
s
e
s
ig
n
if
ican
t
o
b
s
tacle
s
to
im
p
lem
en
tin
g
s
o
p
h
is
ticated
s
ec
u
r
ity
m
ea
s
u
r
es.
T
h
ese
d
ev
i
ce
s
o
f
ten
o
p
er
ate
with
co
n
s
tr
ain
ed
co
m
p
u
tatio
n
al
p
o
wer
an
d
m
em
o
r
y
,
wh
ich
c
an
lim
it
th
e
f
ea
s
ib
ilit
y
o
f
d
e
p
lo
y
in
g
c
o
m
p
u
tatio
n
ally
in
te
n
s
iv
e
o
p
tim
izatio
n
alg
o
r
ith
m
s
f
o
r
r
ea
l
-
tim
e
att
ac
k
d
etec
tio
n
.
Secu
r
ity
an
d
p
r
iv
ac
y
co
n
ce
r
n
s
ar
e
p
ar
a
m
o
u
n
t
in
I
o
T
-
Fo
g
en
v
ir
o
n
m
en
ts
,
wh
er
e
s
en
s
itiv
e
d
ata
is
tr
an
s
m
itted
an
d
p
r
o
ce
s
s
ed
ac
r
o
s
s
m
u
ltip
le
n
o
d
es
an
d
lay
er
s
,
in
cr
ea
s
in
g
v
u
ln
er
ab
ilit
y
to
m
alicio
u
s
attac
k
s
.
E
x
is
tin
g
s
o
lu
tio
n
s
f
o
r
u
tili
zin
g
o
p
tim
izatio
n
al
g
o
r
ith
m
s
in
attac
k
d
etec
tio
n
s
tr
ate
g
y
with
in
au
th
en
ticated
n
etwo
r
k
s
o
f
I
o
T
-
Fo
g
en
v
ir
o
n
m
e
n
ts
in
clu
d
e
Swar
m
I
n
tellig
en
ce
alg
o
r
ith
m
.
Swar
m
in
tellig
en
ce
alg
o
r
ith
m
s
s
u
ch
as
an
t
c
o
lo
n
y
o
p
tim
izatio
n
(
AC
O)
an
d
p
ar
ticle
s
war
m
o
p
tim
izatio
n
(
PS
O)
[
1
3
]
o
p
tim
ize
f
ea
tu
r
e
s
elec
tio
n
[
1
4
]
an
d
m
o
d
el
p
ar
am
eter
s
in
attac
k
d
etec
tio
n
[
1
5
]
,
[
1
6
]
s
y
s
tem
s
,
en
h
an
cin
g
th
eir
p
er
f
o
r
m
an
ce
[
1
7
]
in
id
en
tify
i
n
g
s
ec
u
r
ity
th
r
ea
ts
.
Desp
ite
th
e
p
o
ten
tial
b
en
ef
its
o
f
o
p
tim
iz
atio
n
alg
o
r
ith
m
s
in
attac
k
d
etec
tio
n
s
tr
ateg
ies
f
o
r
au
th
e
n
ticated
n
etwo
r
k
s
o
f
I
o
T
-
Fo
g
en
v
ir
o
n
m
e
n
ts
,
s
ev
er
al
ch
allen
g
es
a
n
d
d
r
awb
ac
k
s
ex
is
t.
T
h
e
y
ar
e:
a.
C
o
m
p
u
tatio
n
al
c
o
m
p
lex
ity
:
Op
tim
izatio
n
alg
o
r
ith
m
s
m
a
y
r
eq
u
ir
e
s
ig
n
if
ican
t
co
m
p
u
tat
io
n
al
r
eso
u
r
ce
s
,
m
ak
in
g
th
e
m
u
n
s
u
itab
le
f
o
r
r
e
s
o
u
r
ce
-
co
n
s
tr
ain
e
d
I
o
T
d
ev
ice
s
an
d
Fo
g
n
o
d
es.
b.
L
ac
k
o
f
s
ca
lab
ilit
y
:
So
m
e
o
p
t
im
izatio
n
alg
o
r
ith
m
s
m
ay
s
tr
u
g
g
le
to
s
ca
le
ef
f
ec
tiv
ely
to
ac
co
m
m
o
d
ate
th
e
in
cr
ea
s
in
g
n
u
m
b
er
o
f
d
e
v
ices a
n
d
d
ata
v
o
lu
m
es with
in
th
e
I
o
T
-
Fo
g
[
1
2
]
,
[
1
8
]
en
v
ir
o
n
m
en
t.
c.
Sen
s
itiv
ity
to
p
ar
am
eter
s
:
T
h
e
p
er
f
o
r
m
an
ce
o
f
o
p
tim
izatio
n
alg
o
r
ith
m
s
ca
n
b
e
s
en
s
itiv
e
to
p
ar
a
m
eter
s
ettin
g
s
an
d
in
itializatio
n
,
r
eq
u
ir
in
g
ca
r
e
f
u
l
tu
n
i
n
g
an
d
o
p
tim
izatio
n
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
S
a
ilfi
s
h
-
ca
t a
lg
o
r
ith
m
-
en
h
a
n
c
ed
g
en
era
tive
a
d
ve
r
s
a
r
ia
l
n
etw
o
r
k
fo
r
…
(
P
a
lla
vi
K
a
n
th
a
m
a
n
g
a
la
N
ir
a
n
ja
n
)
1111
d.
A
d
a
p
t
a
b
i
l
it
y
t
o
d
y
n
a
m
i
c
e
n
v
ir
o
n
m
e
n
t
s
:
O
p
t
i
m
i
za
t
i
o
n
al
g
o
r
i
t
h
m
s
m
a
y
f
a
ce
c
h
a
ll
e
n
g
e
s
i
n
a
d
a
p
t
i
n
g
t
o
t
h
e
d
y
n
a
m
i
c
n
a
t
u
r
e
o
f
I
o
T
-
F
o
g
e
n
v
i
r
o
n
m
e
n
t
s
,
w
h
e
r
e
n
et
w
o
r
k
c
o
n
d
i
t
i
o
n
s
a
n
d
a
tt
a
c
k
p
a
t
te
r
n
s
c
a
n
c
h
a
n
g
e
r
a
p
i
d
l
y
.
I
n
s
u
m
m
ar
y
,
s
ca
lab
ilit
y
,
th
e
h
eter
o
g
en
eity
o
f
I
o
T
d
ev
ices
,
d
ata
d
is
tr
ib
u
tio
n
ch
allen
g
es
,
r
eso
u
r
ce
lim
itatio
n
s
,
an
d
th
e
ev
o
l
v
in
g
lan
d
s
ca
p
e
o
f
s
ec
u
r
ity
th
r
ea
t
s
all
co
n
tr
ib
u
te
to
th
e
co
m
p
l
ex
ities
o
f
I
o
T
-
Fo
g
n
etwo
r
k
s
.
T
h
ese
is
s
u
es
u
n
d
e
r
s
co
r
e
th
e
cr
itical
r
o
le
o
f
o
p
tim
izatio
n
alg
o
r
ith
m
s
.
T
h
ey
ar
e
ess
en
tial
f
o
r
en
h
an
cin
g
th
e
s
ec
u
r
ity
p
o
s
t
u
r
e
o
f
a
u
th
en
ticated
I
o
T
-
Fo
g
n
etwo
r
k
.
T
h
ese
alg
o
r
ith
m
s
aim
to
m
itig
ate
v
u
ln
er
ab
ilit
ies
an
d
im
p
r
o
v
e
th
e
r
esil
ien
ce
o
f
I
o
T
-
Fo
g
en
v
ir
o
n
m
en
t
ag
ain
s
t
em
er
g
in
g
s
ec
u
r
ity
ch
allen
g
es,
th
er
eb
y
s
af
eg
u
ar
d
i
n
g
s
en
s
itiv
e
d
ata
an
d
m
ain
tain
in
g
o
p
er
atio
n
al
in
teg
r
ity
.
E
f
f
ec
tiv
e
attac
k
d
etec
tio
n
s
wif
tly
id
en
tifie
s
a
n
d
m
itig
ates
p
o
ten
tial
th
r
ea
ts
to
en
s
u
r
e
s
y
s
tem
s
ec
u
r
ity
an
d
in
teg
r
ity
.
I
n
t
h
e
co
n
te
x
t o
f
attac
k
d
etec
tio
n
Dir
o
a
n
d
C
h
i
lam
k
u
r
ti
[
1
9
]
r
ec
o
m
m
en
d
s
lev
er
ag
in
g
lo
n
g
s
h
o
r
t
-
ter
m
m
em
o
r
y
(
L
STM
)
n
etw
o
r
k
s
f
o
r
attac
k
d
etec
tio
n
in
Fo
g
-
to
-
th
in
g
s
co
m
m
u
n
icatio
n
s
.
T
h
ey
h
ig
h
lig
h
t
L
STM
s
'
ca
p
ab
ilit
y
to
ca
p
tu
r
e
lo
n
g
-
ter
m
d
ep
e
n
d
en
cies
in
s
eq
u
en
tial
d
ata,
wh
ich
im
p
r
o
v
es
th
e
ac
cu
r
ac
y
o
f
attac
k
d
etec
tio
n
b
y
an
al
y
zin
g
h
is
to
r
ical
n
etwo
r
k
tr
af
f
ic
d
ata
.
Ad
d
itio
n
ally
,
th
ey
a
d
d
r
ess
p
o
ten
tial
ch
allen
g
es
s
u
ch
as c
o
m
p
u
tatio
n
al
co
m
p
le
x
ity
an
d
d
ataset
r
eq
u
ir
em
en
ts
.
E
th
ala
an
d
Ku
m
ar
ap
p
an
[
1
3
]
in
tr
o
d
u
ce
s
a
h
y
b
r
id
in
tr
u
s
io
n
d
etec
tio
n
m
et
h
o
d
f
o
r
I
o
T
en
v
ir
o
n
m
en
t,
co
m
b
i
n
in
g
s
p
id
e
r
m
o
n
k
e
y
o
p
tim
iz
atio
n
(
SMO)
a
n
d
h
ier
ar
ch
ical
p
ar
ticle
s
war
m
o
p
tim
i
za
tio
n
(
HPSO).
T
h
ese
al
g
o
r
ith
m
s
,
i
n
s
p
ir
ed
b
y
n
atu
r
al
b
eh
av
io
r
s
o
f
s
p
id
er
m
o
n
k
e
y
s
an
d
h
ier
a
r
ch
ical
s
tr
u
ctu
r
es.
T
h
ey
ad
d
r
ess
th
e
r
is
in
g
cy
b
er
t
h
r
ea
ts
in
I
o
T
n
etwo
r
k
s
b
y
im
p
r
o
v
in
g
t
h
e
d
etec
tio
n
ac
cu
r
ac
y
a
n
d
ef
f
icien
cy
th
r
o
u
g
h
th
e
i
n
teg
r
ated
s
tr
e
n
g
th
s
o
f
SMO
an
d
HPSO.
T
h
e
ap
p
r
o
ac
h
in
clu
d
es
p
r
ep
r
o
ce
s
s
in
g
I
o
T
d
ata,
o
p
tim
izin
g
in
tr
u
s
io
n
d
etec
tio
n
p
a
r
a
m
eter
s
,
an
d
im
p
lem
en
tin
g
th
e
m
o
d
el
f
o
r
r
ea
l
-
tim
e
n
etwo
r
k
p
r
o
tectio
n
.
Saee
d
an
d
J
am
ee
l
[
1
4
]
p
r
o
p
o
s
e
a
m
eth
o
d
co
m
b
in
in
g
p
ar
ticle
s
war
m
o
p
tim
izatio
n
(
PS
O)
an
d
a
d
ec
is
io
n
t
r
e
e
(
DT
)
clas
s
if
ier
f
o
r
in
tellig
en
t
f
ea
t
u
r
e
s
elec
tio
n
in
d
is
tr
ib
u
ted
d
en
ial
o
f
s
er
v
ice
(
DDo
S)
attac
k
d
etec
tio
n
.
T
h
is
in
teg
r
atio
n
o
p
tim
izes
f
ea
tu
r
e
s
elec
tio
n
f
r
o
m
lar
g
e
d
atasets
,
en
h
an
cin
g
d
etec
tio
n
ac
cu
r
ac
y
a
n
d
r
ed
u
cin
g
co
m
p
u
tatio
n
al
o
v
er
h
ea
d
.
Dao
u
d
a
n
d
Ma
h
f
o
u
d
h
i
[
2
0
]
in
tr
o
d
u
ce
s
s
ec
u
r
e
in
tellig
en
t
m
eth
o
d
f
o
r
attac
k
d
etec
tio
n
(
SIM
AD)
,
a
d
etec
tio
n
m
eth
o
d
co
m
b
in
in
g
m
ac
h
in
e
lea
r
n
in
g
an
d
an
o
m
aly
d
etec
tio
n
to
s
ec
u
r
e
I
o
T
-
Fo
g
en
v
ir
o
n
m
en
ts
b
y
m
o
n
ito
r
in
g
n
etwo
r
k
tr
a
f
f
ic
an
d
d
e
v
ice
b
eh
av
io
r
t
o
id
en
tif
y
th
r
ea
ts
lik
e
DDo
S
an
d
m
alw
ar
e
with
h
ig
h
ac
c
u
r
ac
y
.
I
t
a
ls
o
h
ig
h
lig
h
ts
ch
allen
g
es
s
u
c
h
as
th
e
n
ee
d
f
o
r
co
n
tin
u
o
u
s
m
o
d
el
u
p
d
ates
an
d
m
an
ag
in
g
en
c
r
y
p
te
d
tr
af
f
ic
in
r
eso
u
r
ce
-
lim
ited
s
ettin
g
s
.
Go
u
d
a
et
a
l.
[
2
1
]
p
r
o
p
o
s
es
an
attac
k
d
etec
tio
n
s
ch
em
e
f
o
r
m
o
b
ile
ad
h
o
c
n
etwo
r
k
s
in
in
ter
n
et
o
f
th
i
n
g
s
(
MA
NE
T
-
I
o
T
)
en
v
ir
o
n
m
en
t.
T
h
is
s
ch
em
e
u
s
es
th
e
ad
ap
tiv
e
tu
n
icate
s
war
m
alg
o
r
ith
m
(
AT
SA)
to
id
en
tify
an
d
m
itig
ate
th
r
ea
ts
f
r
o
m
b
lack
m
ailin
g
n
o
d
es.
T
h
is
s
ch
em
e
d
y
n
am
ically
ad
ap
ts
to
n
etwo
r
k
c
o
n
d
itio
n
s
an
d
th
r
ea
t
s
ev
er
ity
.
Ad
r
ian
et
a
l.
[
2
2
]
ex
p
l
o
r
es
u
s
in
g
PS
O
to
id
en
tify
attac
k
p
o
in
ts
o
n
I
o
T
d
e
v
ices
to
en
h
a
n
ce
th
e
ef
f
icac
y
o
f
in
tr
u
s
io
n
p
r
e
v
en
tio
n
s
y
s
tem
s
.
PS
O
in
s
p
ir
ed
b
y
co
llectiv
e
an
im
al
b
eh
av
io
r
s
,
tar
g
ets
th
e
o
p
t
im
izatio
n
o
f
attac
k
p
o
in
t
s
elec
ted
o
n
I
o
T
d
ev
ices,
wh
ich
ar
e
o
f
te
n
v
u
ln
er
ab
le
d
u
e
to
lim
ited
r
eso
u
r
ce
s
a
n
d
i
n
s
u
f
f
icien
t
s
ec
u
r
ity
.
T
h
e
s
tu
d
y
ev
al
u
ates
th
e
ef
f
ec
t
iv
en
ess
o
f
th
is
m
eth
o
d
a
n
d
d
is
cu
s
s
es
p
o
ten
tial
d
r
awb
ac
k
s
,
s
u
ch
as
r
eliab
ilit
y
o
f
s
p
ec
if
ic
ass
u
m
p
tio
n
s
an
d
p
e
r
f
o
r
m
an
ce
v
ar
iab
ilit
y
u
n
d
er
d
if
f
er
en
t c
o
n
d
itio
n
s
.
T
h
e
m
o
tiv
atio
n
f
o
r
ex
p
lo
r
in
g
o
p
tim
izatio
n
alg
o
r
ith
m
s
f
o
r
attac
k
d
etec
tio
n
s
tr
ateg
y
i
n
au
t
h
en
ticated
n
etwo
r
k
s
[
2
3
]
,
[
2
4
]
o
f
I
o
T
-
Fo
g
en
v
ir
o
n
m
e
n
ts
s
tem
s
f
r
o
m
th
e
n
ee
d
to
a
d
d
r
ess
th
e
id
en
tifie
d
g
ap
s
an
d
d
r
awb
ac
k
s
in
ex
is
tin
g
s
o
l
u
tio
n
s
.
B
y
in
v
esti
g
atin
g
th
e
ef
f
ic
ac
y
o
f
o
p
tim
izatio
n
tech
n
iq
u
e
s
in
im
p
r
o
v
in
g
th
e
ef
f
icien
cy
,
s
ca
lab
ilit
y
,
an
d
a
d
ap
tab
ilit
y
o
f
attac
k
d
etec
tio
n
s
y
s
tem
s
,
o
u
r
s
tu
d
y
aim
s
to
p
r
o
v
i
d
e
v
alu
ab
le
in
s
ig
h
ts
in
to
m
itig
atin
g
s
ec
u
r
ity
th
r
ea
ts
with
in
in
ter
c
o
n
n
ec
ted
ec
o
s
y
s
tem
s
.
Fu
r
th
e
r
m
o
r
e,
b
y
id
e
n
tify
in
g
o
p
tim
al
co
n
f
ig
u
r
atio
n
s
an
d
s
tr
ateg
ies
f
o
r
lev
e
r
ag
in
g
o
p
tim
izatio
n
alg
o
r
ith
m
s
,
we
s
ee
k
to
em
p
o
wer
s
tak
eh
o
ld
er
s
to
d
ev
el
o
p
p
r
o
a
ctiv
e
an
d
ef
f
ec
tiv
e
s
ec
u
r
ity
m
ea
s
u
r
es
[
2
5
]
,
[
2
6
]
t
h
at
u
p
h
o
ld
th
e
in
teg
r
ity
a
n
d
co
n
f
id
en
tiality
o
f
d
ata
in
I
o
T
-
Fo
g
en
v
ir
o
n
m
en
t.
T
h
e
c
o
n
t
r
i
b
u
t
i
o
n
o
f
r
e
s
e
a
r
c
h
i
n
at
t
a
c
k
d
et
ec
t
i
o
n
s
t
r
a
t
e
g
y
w
it
h
i
n
a
u
t
h
e
n
t
i
c
a
te
d
n
e
t
w
o
r
k
s
[
2
7
]
o
f
I
o
T
-
F
o
g
e
n
v
i
r
o
n
m
e
n
t
l
i
es
i
n
i
t
s
e
n
d
e
a
v
o
r
t
o
b
r
i
d
g
e
t
h
e
b
e
id
e
n
t
i
f
i
e
d
g
a
p
s
a
n
d
c
h
a
l
l
e
n
g
e
s
:
i
)
i
m
p
l
e
m
e
n
t
a
ti
o
n
o
f
a
u
t
h
e
n
t
i
c
a
t
e
d
n
e
t
w
o
r
k
o
f
I
o
T
-
F
o
g
e
n
v
i
r
o
n
m
e
n
t
;
i
i
)
i
m
p
l
e
m
e
n
t
a
t
i
o
n
o
f
S
a
i
l
f
is
h
c
a
t
o
p
t
i
m
iz
a
t
i
o
n
al
g
o
r
i
t
h
m
f
o
r
a
t
t
a
c
k
d
e
te
c
t
i
o
n
s
t
r
at
e
g
y
i
n
a
u
th
e
n
t
i
c
a
t
e
d
n
et
w
o
r
k
o
f
I
o
T
-
F
o
g
e
n
v
i
r
o
n
m
e
n
t
;
a
n
d
i
i
i
)
c
o
m
p
a
r
is
o
n
o
f
p
r
o
p
o
s
e
d
a
p
p
r
o
a
c
h
e
s
w
i
t
h
t
h
e
e
x
is
t
i
n
g
a
p
p
r
o
a
c
h
e
s
B
as
e
d
o
n
t
h
e
a
b
o
v
e
d
i
s
c
u
s
s
i
o
n
s
,
t
h
e
p
r
o
b
l
e
m
i
s
d
e
f
i
n
e
d
a
s
t
h
e
d
e
v
e
l
o
p
m
e
n
t
o
f
a
r
o
b
u
s
t
a
u
t
h
e
n
t
i
c
a
t
i
o
n
a
n
d
a
t
t
a
c
k
d
e
t
e
ct
i
o
n
s
y
s
t
e
m
ta
i
l
o
r
e
d
f
o
r
I
o
T
-
F
o
g
e
n
v
i
r
o
n
m
e
n
t
,
w
h
i
c
h
p
r
e
s
e
n
t
s
s
i
g
n
i
f
ica
n
t
c
h
a
l
l
e
n
g
es
.
T
h
e
i
n
t
e
g
r
a
t
i
o
n
o
f
E
t
h
e
r
e
u
m
s
m
a
r
t
c
o
n
t
r
a
c
t
s
f
o
r
d
e
v
i
ce
a
u
t
h
e
n
ti
c
a
ti
o
n
a
n
d
a
c
c
e
s
s
c
o
n
t
r
o
l
,
al
o
n
g
w
i
t
h
t
h
e
d
et
e
c
ti
o
n
o
f
v
a
r
i
o
u
s
t
y
p
e
s
o
f
a
t
t
ac
k
s
u
s
i
n
g
a
s
a
i
l
f
i
s
h
-
c
at
o
p
t
i
m
i
z
at
i
o
n
(
S
aC
O
)
b
a
s
e
d
g
e
n
e
r
a
t
i
v
e
a
d
v
e
r
s
a
r
i
a
l
n
e
t
w
o
r
k
(
G
AN
)
a
p
p
r
o
a
c
h
,
a
i
m
s
t
o
e
n
h
a
n
c
e
s
e
cu
r
i
t
y
w
i
t
h
i
n
t
h
is
c
o
m
p
l
e
x
e
c
o
s
y
s
t
e
m
.
H
o
w
e
v
e
r
,
e
n
s
u
r
i
n
g
s
e
am
l
e
s
s
a
u
t
h
e
n
t
i
ca
t
i
o
n
a
c
r
o
s
s
d
e
c
e
n
t
r
a
li
z
e
d
n
e
tw
o
r
k
s
a
n
d
e
f
f
e
c
t
i
v
e
l
y
d
et
e
c
ti
n
g
s
o
p
h
is
t
i
ca
t
e
d
at
t
a
c
k
s
r
e
m
ai
n
c
r
i
ti
c
a
l
i
s
s
u
e
s
.
T
h
is
s
t
u
d
y
a
d
d
r
e
s
s
es
t
h
e
s
e
c
h
a
l
le
n
g
e
s
b
y
p
r
o
p
o
s
i
n
g
a
h
y
b
r
i
d
a
p
p
r
o
ac
h
t
h
a
t
o
p
ti
m
i
z
es
a
u
t
h
e
n
t
i
c
ati
o
n
p
r
o
c
e
s
s
e
s
a
n
d
e
n
h
a
n
c
e
s
a
tt
a
c
k
d
e
t
e
ct
i
o
n
m
e
ch
a
n
i
s
m
s
s
p
e
c
i
f
i
c
a
ll
y
d
e
s
i
g
n
e
d
f
o
r
I
o
T
-
F
o
g
e
n
v
i
r
o
n
m
e
n
t
.
T
h
e
s
t
r
u
c
t
u
r
e
o
f
t
h
i
s
p
a
p
e
r
is
o
u
t
l
i
n
e
d
a
s
f
o
l
l
o
ws
:
s
e
c
ti
o
n
2
p
r
o
v
i
d
e
s
a
n
i
n
-
d
e
p
t
h
e
x
p
l
o
r
a
t
i
o
n
o
f
t
h
e
I
o
T
-
F
o
g
n
e
t
w
o
r
k
m
o
d
e
l
.
S
aCO
-
b
a
s
e
d
G
A
N
f
r
a
m
e
w
o
r
k
i
s
d
e
s
c
r
i
b
e
d
i
n
d
e
ta
i
l t
o
i
d
e
n
t
i
f
y
t
h
r
e
a
t
s
i
n
t
h
e
c
o
n
t
e
x
t o
f
I
o
T
-
F
o
g
c
o
m
p
u
t
i
n
g
e
n
v
i
r
o
n
m
e
n
t
.
S
e
c
ti
o
n
3
c
o
n
t
a
i
n
s
t
h
e
f
i
n
d
i
n
g
s
a
n
d
c
o
n
v
e
r
s
a
t
i
o
n
ab
o
u
t
t
h
e
s
u
g
g
e
s
t
e
d
p
a
r
a
d
i
g
m
.
T
h
e
c
o
n
c
l
u
s
i
o
n
o
f
t
h
i
s
r
es
e
a
r
c
h
w
o
r
k
is
p
r
o
v
i
d
e
d
i
n
s
e
c
ti
o
n
4
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
15
,
No
.
1
,
Feb
r
u
ar
y
20
25
:
1
1
0
9
-
1
1
2
2
1112
2.
M
E
T
H
O
D
I
n
th
is
s
ec
tio
n
,
p
r
o
p
o
s
ed
attac
k
d
etec
tio
n
s
tr
ateg
y
in
au
th
e
n
ticated
n
etwo
r
k
o
f
I
o
T
-
Fo
g
e
n
v
ir
o
n
m
e
n
t
is
p
r
esen
ted
.
T
h
e
p
r
o
p
o
s
ed
m
o
d
el
lev
er
ag
es
a
h
y
b
r
i
d
ap
p
r
o
ac
h
co
m
b
in
in
g
s
ailf
is
h
-
ca
t
o
p
tim
izatio
n
with
a
GAN
to
d
etec
t
attac
k
s
with
in
an
I
o
T
-
Fo
g
en
v
ir
o
n
m
en
t.
T
h
is
ap
p
r
o
ac
h
is
p
r
ec
ed
ed
b
y
a
r
o
b
u
s
t
au
th
en
ticatio
n
m
ec
h
an
is
m
u
s
in
g
E
t
h
er
eu
m
s
m
ar
t
co
n
tr
ac
ts
.
T
h
e
p
r
o
ce
s
s
en
co
m
p
ass
es
two
p
r
im
ar
y
p
h
a
s
es:
Au
th
en
ticatio
n
an
d
attac
k
d
etec
tio
n
.
Du
r
in
g
t
h
e
au
th
e
n
ticatio
n
p
h
ase
[
2
4
]
,
I
o
T
d
ev
ice
ac
ce
s
s
is
au
th
e
n
ticated
u
s
in
g
ac
ce
s
s
to
k
en
s
,
v
e
r
if
ied
th
r
o
u
g
h
p
r
o
ce
s
s
es
lik
e
h
ash
in
g
an
d
e
n
cr
y
p
tio
n
.
Po
s
t
-
au
t
h
en
ticatio
n
,
p
er
m
i
s
s
io
n
to
ac
ce
s
s
d
ata
is
g
r
an
ted
to
th
e
u
s
er
.
Fo
r
a
ttack
d
etec
tio
n
,
th
e
p
r
o
ce
s
s
b
eg
in
s
with
f
ea
tu
r
e
s
elec
tio
n
u
s
in
g
Min
k
o
wsk
i
d
is
tan
ce
o
n
n
etwo
r
k
d
ata,
f
o
l
lo
wed
b
y
attac
k
d
etec
tio
n
u
s
i
n
g
a
SaC
O
-
b
ased
GAN,
wh
i
ch
o
p
tim
ally
tu
n
es
GAN
'
s
in
ter
n
al
m
o
d
el
p
ar
am
e
ter
s
v
ia
in
teg
r
atio
n
o
f
s
ail
f
is
h
o
p
tim
izer
(
SF
O)
an
d
ca
t
s
war
m
o
p
tim
izer
(
C
SO)
tech
n
iq
u
es.
T
h
e
au
th
en
ticatio
n
an
d
attac
k
d
etec
tio
n
p
r
o
ce
s
s
es a
r
e
d
etailed
in
Fig
u
r
e
2
.
Fig
u
r
e
2
.
Au
t
h
en
ticatio
n
an
d
a
ttack
d
etec
tio
n
p
r
o
ce
s
s
2
.
1
.
Aut
hentic
a
t
io
n
p
ro
ce
s
s
T
h
is
s
ec
tio
n
d
escr
ib
es
th
e
au
t
h
en
ticatio
n
p
r
o
ce
s
s
f
o
r
a
d
ec
e
n
tr
alize
d
clo
u
d
s
to
r
ag
e
s
y
s
tem
,
in
v
o
lv
i
n
g
d
ev
ice
r
eg
is
tr
atio
n
,
m
a
p
p
in
g
,
au
th
en
ticatio
n
,
to
k
en
g
e
n
er
ati
o
n
,
an
d
d
ata
ex
ch
a
n
g
e,
as
d
e
p
icted
in
Fig
u
r
e
3
.
Fiv
e
en
titi
es
s
m
ar
t
co
n
tr
ac
t,
ad
m
in
,
Fo
g
n
o
d
es,
en
d
u
s
er
,
an
d
I
o
T
d
ev
ices
ar
e
in
v
o
lv
ed
,
with
th
e
a
d
m
i
n
d
ep
lo
y
in
g
th
e
s
m
ar
t
c
o
n
tr
ac
t
to
r
eg
is
ter
I
o
T
d
ev
ices
an
d
m
ap
th
em
t
o
m
an
a
g
e
f
o
g
n
o
d
es.
Au
th
en
ticatio
n
,
ac
ce
s
s
co
n
tr
o
l,
an
d
r
eg
is
tr
atio
n
ar
e
d
ec
en
tr
alize
d
t
h
r
o
u
g
h
th
e
s
m
ar
t
co
n
tr
ac
t,
wh
ich
also
r
ec
o
r
d
s
ap
p
r
o
v
e
d
d
ev
ice
u
s
er
s
an
d
th
ei
r
en
c
r
y
p
ted
k
e
y
wo
r
d
f
iles
.
T
h
e
au
t
h
en
ticatio
n
p
r
o
ce
s
s
em
p
lo
y
e
d
in
t
h
is
s
tu
d
y
a
r
e
attr
ib
u
ted
to
th
e
f
u
n
d
am
e
n
tal
wo
r
k
p
r
o
p
o
s
ed
in
o
u
r
p
r
e
v
io
u
s
r
esear
ch
ar
ticle
[
2
4
]
wh
er
e
th
e
r
elev
a
n
t
f
o
r
m
u
latio
n
ar
e
th
o
r
o
u
g
h
ly
elu
cid
ated
.
a.
Dev
ice
r
eg
is
tr
atio
n
p
h
ase:
T
h
e
ad
m
in
is
tr
ato
r
f
o
r
m
s
a
s
m
ar
t
co
n
tr
ac
t
u
s
in
g
th
e
I
o
T
d
ev
ice
'
s
I
D,
E
th
er
eu
m
ad
d
r
ess
,
an
d
attr
ib
u
tes.
T
h
e
y
th
en
r
eg
is
ter
it
with
a
u
s
er
I
D
an
d
an
en
c
r
y
p
ted
p
ass
wo
r
d
d
er
iv
ed
f
r
o
m
th
e
co
n
ca
ten
ated
d
ev
ice
I
D
an
d
E
t
h
er
eu
m
a
d
d
r
ess
.
Secu
r
e
ac
ce
s
s
is
en
s
u
r
ed
b
y
v
er
i
f
y
in
g
th
at
t
h
e
u
s
er
-
en
ter
e
d
p
ass
wo
r
d
m
atch
es th
e
s
to
r
ed
e
n
cr
y
p
ted
o
n
e.
b.
Ma
p
p
in
g
p
h
ase:
T
h
e
ad
m
in
is
tr
ato
r
f
ac
ilit
ates
I
o
T
d
ev
ice
-
to
-
f
o
g
n
o
d
e
m
a
p
p
in
g
b
y
g
e
n
e
r
atin
g
m
ess
ag
es
f
r
o
m
co
n
ca
te
n
ated
p
u
b
lic
k
ey
s
an
d
E
th
er
eu
m
ad
d
r
ess
es.
Fu
r
th
er
,
co
n
ca
ten
ate
th
ese
m
ess
ag
es
wi
th
a
r
an
d
o
m
n
u
m
b
e
r
f
o
r
th
e
m
a
p
p
i
n
g
f
u
n
ctio
n
.
Af
ter
m
ee
tin
g
ce
r
tain
co
n
d
itio
n
s
,
th
ese
m
ess
ag
es a
r
e
s
en
t to
th
e
r
esp
ec
tiv
e
f
o
g
n
o
d
es,
r
ec
o
r
d
e
d
o
n
I
o
T
d
ev
ices,
an
d
u
s
ed
to
s
ec
u
r
e
d
ev
ice
-
to
-
u
s
er
ass
o
ciatio
n
s
.
T
o
en
h
an
ce
s
ec
u
r
ity
,
u
s
er
p
ass
wo
r
d
s
,
I
Ds,
an
d
p
u
b
lic
k
e
y
s
ar
e
h
ash
ed
a
n
d
XORed
with
a
s
ec
u
r
ity
p
ar
am
eter
to
cr
ea
te
a
f
in
al
m
ess
ag
e
s
to
r
ed
o
n
I
o
T
d
ev
ices f
o
r
u
s
er
v
er
if
icatio
n
.
c.
Au
th
en
ticatio
n
p
h
ase:
W
h
en
an
en
d
u
s
er
attem
p
ts
to
lo
g
in
to
an
I
o
T
d
ev
ice,
th
ey
s
u
b
m
it
an
au
th
en
ticatio
n
r
eq
u
est
to
th
e
s
m
ar
t
co
n
tr
ac
t,
wh
ich
u
s
es
th
e
d
ev
ice'
s
s
et
au
th
en
ticatio
n
f
u
n
ctio
n
to
v
er
if
y
th
eir
cr
ed
en
tials
.
I
f
th
e
u
s
er
lack
s
th
e
r
eq
u
i
r
ed
p
er
m
is
s
io
n
s
,
th
e
s
m
ar
t
co
n
tr
ac
t
d
en
ies
ac
ce
s
s
,
r
ec
o
r
d
s
th
e
d
en
ial
,
an
d
n
o
tifie
s
th
e
u
s
er
ab
o
u
t th
e
r
ejec
tio
n
.
d.
T
o
k
en
g
en
er
atio
n
p
h
ase:
T
h
e
au
th
en
ticatio
n
p
r
o
ce
s
s
em
p
lo
y
s
to
k
en
-
b
ased
ac
ce
s
s
co
n
tr
o
l,
g
e
n
er
atin
g
to
k
en
s
f
r
o
m
t
h
e
E
th
e
r
eu
m
ad
d
r
ess
,
d
ev
ice
I
D,
an
d
cr
y
p
to
g
r
a
p
h
ic
elem
en
ts
.
Fo
g
n
o
d
es
v
er
i
f
y
th
ese
to
k
en
s
to
au
th
en
ticate
u
s
er
ac
ce
s
s
to
I
o
T
d
ev
ices,
with
en
h
an
ce
d
s
ec
u
r
ity
p
r
o
v
id
ed
b
y
d
ig
itally
s
ig
n
ed
m
ess
ag
es
an
d
s
ess
io
n
k
ey
s
to
s
ec
u
r
e
a
n
d
v
er
if
y
c
o
m
m
u
n
icatio
n
s
.
e.
D
a
t
a
e
x
c
h
a
n
g
e
p
h
a
s
e
:
D
u
r
i
n
g
t
h
e
d
a
t
a
e
x
c
h
a
n
g
e
p
h
a
s
e
,
I
o
T
d
e
v
i
c
e
s
a
n
d
u
s
e
r
s
s
e
c
u
r
e
l
y
t
r
an
s
m
i
t
e
n
c
r
y
p
t
e
d
d
a
t
a
o
v
e
r
SS
L
c
o
n
n
e
c
t
i
o
n
s
t
o
m
a
i
n
t
a
i
n
c
o
n
f
i
d
e
n
ti
a
l
it
y
a
n
d
i
n
t
e
g
r
i
t
y
.
A
d
d
it
i
o
n
a
l
l
y
,
f
o
g
n
o
d
es
a
n
d
e
n
d
u
s
e
r
s
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
S
a
ilfi
s
h
-
ca
t a
lg
o
r
ith
m
-
en
h
a
n
c
ed
g
en
era
tive
a
d
ve
r
s
a
r
ia
l
n
etw
o
r
k
fo
r
…
(
P
a
lla
vi
K
a
n
th
a
m
a
n
g
a
la
N
ir
a
n
ja
n
)
1113
u
s
e
p
r
i
v
a
t
e
a
n
d
s
e
s
s
i
o
n
k
e
y
s
f
o
r
e
n
c
r
y
p
t
i
o
n
.
T
h
e
s
u
c
c
es
s
o
f
d
at
a
e
x
c
h
a
n
g
e
r
el
i
es
o
n
t
h
e
c
o
n
s
is
t
e
n
c
y
o
f
s
i
g
n
e
d
m
e
s
s
a
g
es
b
e
tw
e
e
n
f
o
g
n
o
d
e
s
an
d
e
n
d
u
s
e
r
s
,
e
n
s
u
r
i
n
g
t
h
e
a
u
t
h
e
n
t
i
c
i
t
y
a
n
d
a
cc
u
r
a
c
y
o
f
t
h
e
d
a
ta
t
r
a
n
s
f
e
r
r
e
d
.
Fig
u
r
e
3
.
Au
t
h
en
ticatio
n
p
r
o
ce
s
s
2
.
2
.
At
t
a
c
k
det
ec
t
io
n pro
ce
s
s
Fo
llo
win
g
s
u
cc
ess
f
u
l
u
s
er
a
u
th
en
ticatio
n
an
d
d
ata
ex
ch
a
n
g
e,
th
e
attac
k
d
etec
tio
n
p
r
o
c
ess
b
eg
in
s
u
s
in
g
th
e
SaC
O
-
b
ased
GAN
ap
p
r
o
ac
h
as
illu
s
tr
ated
in
F
ig
u
r
e
4
.
I
n
itially
,
in
p
u
t
d
ata
u
n
d
er
g
o
es
f
ea
tu
r
e
s
elec
tio
n
v
ia
th
e
Min
k
o
wsk
i
d
is
tan
ce
m
etr
ic
to
s
elec
t
o
p
tim
a
l
f
ea
tu
r
es.
Su
b
s
eq
u
en
tly
,
attac
k
d
etec
tio
n
u
tili
ze
s
th
e
tu
n
ed
GAN
m
o
d
el,
o
p
ti
m
ized
b
y
th
e
SaC
O,
a
h
y
b
r
i
d
o
f
th
e
SFO
an
d
C
SO
.
T
h
e
in
itial
s
tep
s
o
f
th
e
SaC
O
-
b
ased
GAN
ap
p
r
o
ac
h
is
ex
p
lain
ed
b
elo
w.
Fig
u
r
e
4
.
I
ll
u
s
tr
ativ
e
r
ep
r
esen
t
atio
n
o
f
th
e
SaC
O
-
b
ased
GAN
p
r
o
p
o
s
ed
f
o
r
th
e
d
etec
tio
n
o
f
attac
k
s
in
th
e
I
o
T
-
Fo
g
e
n
v
ir
o
n
m
en
t
2
.
2
.
1
.
G
et
t
he
inp
ut
s
ig
na
l
T
h
e
in
p
u
t
d
ata
is
id
en
tifie
d
th
r
o
u
g
h
a
s
y
s
tem
atic
an
aly
s
is
o
f
t
h
e
in
f
o
r
m
atio
n
ex
tr
ac
te
d
f
r
o
m
a
d
ataset.
T
h
is
d
ataset,
d
en
o
ted
as
,
co
n
s
is
t
s
o
f
n
in
p
u
t
d
ata
p
o
in
ts
,
ea
ch
p
r
o
v
id
in
g
d
is
tin
ct
in
s
ig
h
ts
.
An
aly
zin
g
th
ese
d
ata
p
o
i
n
ts
f
ac
ilit
ates
th
e
d
is
co
v
er
y
o
f
p
atter
n
s
an
d
r
elati
o
n
s
h
ip
s
th
at
ar
e
c
r
itical
f
o
r
d
er
iv
in
g
in
f
o
r
m
ed
co
n
clu
s
io
n
s
f
r
o
m
th
e
d
ataset.
=
{
}
;
∈
{
1
,
2
,
…
.
.
,
}
(
1
)
2
.
2
.
2
.
M
ink
o
wsk
i dis
t
a
nce
f
o
r
s
elec
t
ing
t
he
f
ea
t
ures
T
h
e
f
ea
tu
r
e
s
elec
tio
n
m
o
d
u
le
p
r
o
ce
s
s
es th
e
in
p
u
t d
ata
b
y
em
p
lo
y
in
g
th
e
Min
k
o
wsk
i d
is
tan
ce
in
it
s
s
elec
tio
n
alg
o
r
ith
m
.
Sp
ec
if
ically
,
th
e
Min
k
o
wsk
i
d
is
tan
ce
o
f
o
r
d
er
b
etwe
en
two
n
eig
h
b
o
r
in
g
d
ata
p
o
in
ts
,
d
en
o
ted
as
an
d
,
q
u
an
tifie
s
th
eir
s
im
ilar
ity
an
d
is
m
ath
e
m
atica
lly
ex
p
r
ess
ed
as
(
2
)
.
T
h
is
d
is
tan
ce
m
etr
ic
is
ess
en
tial
f
o
r
d
ete
r
m
in
in
g
th
e
r
elev
an
ce
o
f
f
ea
t
u
r
es
an
d
e
n
h
an
cin
g
th
e
o
v
e
r
all
p
er
f
o
r
m
a
n
ce
o
f
th
e
s
elec
tio
n
p
r
o
ce
s
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
15
,
No
.
1
,
Feb
r
u
ar
y
20
25
:
1
1
0
9
-
1
1
2
2
1114
(
,
)
=
(
∑
|
−
|
=
1
)
1
(
2
)
2
.
2
.
3
.
Alg
o
rit
hm
ic
pro
ce
du
r
e
o
f
t
he
pro
po
s
ed
s
a
ilfish
-
ca
t
o
ptim
iza
t
io
n
f
o
r
a
t
t
a
c
k
det
e
ct
io
n
T
h
e
alg
o
r
ith
m
ic
p
r
o
ce
d
u
r
e
o
f
th
e
p
r
o
p
o
s
ed
SaC
O
f
o
r
attac
k
d
etec
tio
n
is
m
eticu
lo
u
s
ly
o
u
tlin
ed
.
T
h
is
ap
p
r
o
ac
h
in
clu
d
es
a
s
er
ies
o
f
s
y
s
tem
atic
s
tep
s
th
at
illu
s
tr
a
te
its
f
u
n
ctio
n
ality
an
d
ef
f
ec
tiv
en
ess
.
E
ac
h
s
tep
p
r
o
v
id
es
a
c
o
m
p
r
e
h
en
s
iv
e
u
n
d
er
s
tan
d
in
g
o
f
th
e
SaC
O
o
p
er
atio
n
,
s
u
p
p
o
r
tin
g
ef
f
icien
t
attac
k
d
etec
tio
n
.
R
ef
e
r
to
T
ab
le
1
f
o
r
a
co
m
p
lete
lis
t
o
f
n
o
tatio
n
s
an
d
th
eir
d
escr
ip
tio
n
s
u
s
ed
in
th
e
alg
o
r
ith
m
i
c
p
r
o
ce
d
u
r
e
o
f
th
e
p
r
o
p
o
s
ed
s
ailf
is
h
-
ca
t o
p
tim
iza
tio
n
f
o
r
attac
k
d
etec
tio
n
.
T
ab
le
1
.
No
tatio
n
s
a
n
d
its
d
escr
ip
tio
n
s
N
o
t
a
t
i
o
n
s
D
e
scri
p
t
i
o
n
B
pos
Th
e
p
o
s
i
t
i
o
n
s
o
f
a
l
l
sa
i
l
f
i
s
h
T
pos
Th
e
p
o
s
i
t
i
o
n
s
o
f
a
l
l
sar
d
i
n
e
B
f
i
t
F
i
t
n
e
ss
v
a
l
u
e
f
o
r
e
a
c
h
s
a
i
l
f
i
s
h
T
fit
F
i
t
n
e
ss
v
a
l
u
e
f
o
r
e
a
c
h
s
a
r
d
i
n
e
El
i
t
e
s
a
i
l
f
i
s
h
's
I
n
j
u
r
e
d
S
a
r
d
i
n
e
+
1
N
e
w
l
o
c
a
t
i
o
n
o
f
sar
d
i
n
e
C
u
r
r
e
n
t
l
o
c
a
t
i
o
n
o
f
sar
d
i
n
e
P
r
e
y
d
e
n
si
t
y
BA
N
u
mb
e
r
o
f
sa
i
l
f
i
s
h
's
a
t
t
a
c
k
p
o
w
e
r
a
t
e
v
e
r
y
i
t
e
r
a
t
i
o
n
Th
e
n
u
m
b
e
r
o
f
t
h
e
sa
i
l
f
i
sh
i
n
e
v
e
r
y
c
y
c
l
e
Th
e
n
u
m
b
e
r
o
f
t
h
e
sar
d
i
n
e
s i
n
e
v
e
r
y
c
y
c
l
e
Step
1
.
I
n
itializatio
n
o
f
th
e
p
o
p
u
latio
n
I
n
th
e
SF
O
s
ch
em
e,
s
ailf
is
h
ar
e
id
en
tifie
d
as
p
o
ten
tial
ca
n
d
i
d
ates,
wh
er
e
ea
ch
s
ailf
is
h
's
p
o
s
itio
n
in
a
s
ea
r
ch
s
p
ac
e
r
ep
r
esen
ts
a
p
r
o
b
lem
v
ar
iab
le.
I
n
itially
,
a
p
o
p
u
latio
n
o
f
s
ailf
is
h
is
r
an
d
o
m
ly
g
en
er
ated
in
th
e
s
o
lu
tio
n
s
p
ac
e.
T
h
e
p
o
s
itio
n
o
f
th
e
th
m
em
b
er
o
f
s
ailf
is
h
d
u
r
in
g
t
h
e
th
s
ea
r
ch
in
b
d
im
en
s
io
n
al
s
p
ac
e
i
s
d
ef
in
ed
as
(
3
)
:
,
∈
(
=
1
,
2
,
.
.
.
,
)
(
3
)
wh
er
e
d
en
o
tes
th
e
n
u
m
b
er
o
f
v
ar
iab
les
an
d
r
ep
r
esen
ts
th
e
n
u
m
b
er
o
f
s
ailf
is
h
m
em
b
er
s
.
Ma
tr
ix
is
u
tili
ze
d
to
s
to
r
e
th
e
p
o
s
itio
n
s
o
f
all
s
ailf
i
s
h
,
r
ep
r
esen
tin
g
th
e
v
ar
iab
les
o
f
all
s
o
lu
tio
n
s
th
r
o
u
g
h
o
u
t
th
e
o
p
tim
izatio
n
p
r
o
ce
s
s
.
B
po
s
=
[
1
,
1
1
,
2
2
,
1
2
,
2
…
1
,
…
2
,
⋮
⋮
,
1
,
2
⋮
⋮
…
,
]
(
4
)
w
h
er
e
in
d
icate
s
th
e
n
u
m
b
e
r
o
f
s
ailf
is
h
,
d
en
o
tes
th
e
n
u
m
b
e
r
o
f
v
ar
iab
les,
an
d
,
r
ep
r
esen
ts
th
e
v
alu
e
o
f
th
e
j
th
d
im
en
s
io
n
o
f
th
e
i
th
s
ailf
is
h
.
T
h
e
s
ch
o
o
l
o
f
s
ar
d
in
es
is
an
o
th
er
im
p
o
r
tan
t
c
o
m
p
o
n
en
t
in
th
e
SF
O
alg
o
r
ith
m
.
I
t
is
ass
u
m
ed
th
at
th
e
g
r
o
u
p
o
f
s
ar
d
in
es
also
m
o
v
es
with
in
th
e
s
ea
r
ch
s
p
a
ce
.
T
h
er
ef
o
r
e,
th
e
p
o
s
itio
n
s
o
f
s
ar
d
in
es a
n
d
th
eir
co
r
r
esp
o
n
d
in
g
f
itn
ess
v
alu
es a
r
e
u
tili
ze
d
as
(
5
)
:
=
[
1
,
1
1
,
2
2
,
1
2
,
2
…
1
,
…
2
,
⋮
⋮
,
1
,
2
⋮
⋮
…
,
]
(
5
)
wh
er
e
is
th
e
n
u
m
b
er
o
f
s
ar
d
in
es
an
d
,
d
en
o
tes
th
e
v
al
u
e
o
f
th
e
th
d
im
en
s
io
n
o
f
th
s
ar
d
in
e.
T
h
e
m
atr
ix
s
ig
n
if
ies th
e
p
o
s
itio
n
o
f
all
s
ar
d
in
es.
Step
2
.
Dete
r
m
in
atio
n
o
f
f
itn
e
s
s
T
h
e
f
itn
ess
o
f
ea
ch
s
ailf
is
h
is
d
eter
m
in
ed
b
y
ev
al
u
atin
g
th
e
f
itn
ess
f
u
n
ctio
n
as
(
6
)
:
=
(
1
,
2
,
…
,
)
(
6
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
S
a
ilfi
s
h
-
ca
t a
lg
o
r
ith
m
-
en
h
a
n
c
ed
g
en
era
tive
a
d
ve
r
s
a
r
ia
l
n
etw
o
r
k
fo
r
…
(
P
a
lla
vi
K
a
n
th
a
m
a
n
g
a
la
N
ir
a
n
ja
n
)
1115
T
o
ass
ess
ea
ch
s
ailf
i
s
h
,
th
e
f
o
l
lo
win
g
m
atr
ix
d
is
p
lay
s
th
e
f
itn
ess
v
alu
es f
o
r
all
s
o
lu
tio
n
s
:
B
f
it
=
[
(
1
,
1
1
,
2
(
2
,
1
2
,
2
…
1
,
)
…
2
,
)
⋮
⋮
(
,
1
,
2
⋮
⋮
…
,
)
]
=
[
1
2
⋮
]
(
7
)
I
n
th
is
s
ce
n
ar
io
,
r
ep
r
esen
ts
th
e
co
u
n
t
o
f
s
ailf
is
h
,
,
d
en
o
tes
th
e
th
d
im
en
s
io
n
v
alu
e
o
f
t
h
e
th
s
ailf
is
h
,
co
m
p
u
tes
th
e
f
itn
ess
f
u
n
ctio
n
,
an
d
r
ec
o
r
d
s
th
e
f
itn
ess
v
alu
es
r
ep
r
esen
tin
g
th
e
f
itn
ess
o
r
o
b
jectiv
e
f
u
n
ctio
n
o
u
tc
o
m
e
f
o
r
ea
c
h
s
ai
lf
is
h
.
T
h
e
in
itial
r
o
w
o
f
th
e
m
atr
ix
u
n
d
er
g
o
es
ev
alu
atio
n
with
in
th
e
f
itn
ess
f
u
n
ctio
n
,
an
d
its
r
esu
lt si
g
n
if
ie
s
th
e
f
itn
ess
v
alu
e
f
o
r
t
h
e
r
esp
ec
tiv
e
s
ailf
is
h
with
in
th
e
m
atr
ix
.
=
[
(
1
,
1
1
,
2
(
2
,
1
2
,
2
…
1
,
)
…
2
,
)
⋮
⋮
(
,
1
,
2
⋮
⋮
…
,
)
]
=
[
1
2
⋮
]
(
8
)
wh
er
e
d
en
o
tes
th
e
n
u
m
b
er
o
f
s
ar
d
in
es,
T
i,
j
r
ep
r
esen
ts
th
e
v
alu
e
o
f
th
e
th
d
im
en
s
io
n
o
f
th
e
th
s
ar
d
in
e,
s
ig
n
if
ies
th
e
o
b
jectiv
e
f
u
n
ctio
n
,
an
d
s
to
r
es
th
e
f
itn
ess
v
alu
e
f
o
r
ea
ch
s
ar
d
i
n
e.
I
t
is
im
p
o
r
tan
t
to
n
o
te
th
at
s
ailf
is
h
an
d
s
ar
d
in
es
a
r
e
co
m
p
lem
en
tar
y
elem
e
n
ts
in
d
is
co
v
er
in
g
s
o
lu
tio
n
s
.
I
n
th
is
alg
o
r
it
h
m
,
s
ailf
is
h
ar
e
th
e
p
r
im
ar
y
e
n
titi
es
d
is
p
er
s
ed
ac
r
o
s
s
th
e
s
ea
r
ch
s
p
ac
e,
wh
ile
s
ar
d
in
es
co
llab
o
r
ate
to
l
o
ca
te
o
p
tim
al
p
o
s
itio
n
s
with
in
th
is
d
o
m
ain
.
Sp
ec
if
ica
lly
,
s
ar
d
in
es
m
ay
b
e
p
r
ey
e
d
u
p
o
n
b
y
s
ailf
is
h
d
u
r
in
g
ex
p
l
o
r
atio
n
o
f
th
e
s
ea
r
ch
s
p
ac
e,
p
r
o
m
p
tin
g
s
ailf
is
h
to
u
p
d
ate
th
eir
p
o
s
itio
n
s
u
p
o
n
d
is
c
o
v
er
in
g
s
u
p
er
io
r
s
o
lu
tio
n
s
.
Step
3
.
E
liti
s
m
T
h
e
co
n
ce
p
t
o
f
elitis
m
in
th
e
s
ail
f
is
h
o
p
tim
izer
(
SF
O)
in
v
o
lv
es
p
r
eser
v
in
g
t
h
e
o
p
tim
al
s
o
lu
tio
n
ac
r
o
s
s
g
en
er
atio
n
s
with
o
u
t
m
o
d
if
icatio
n
.
I
n
th
e
c
o
n
tex
t
o
f
g
r
o
u
p
attac
k
s
wh
e
r
e
s
ailf
is
h
t
ar
g
et
s
ar
d
in
es,
th
e
elite
s
ailf
is
h
'
s
p
o
s
itio
n
in
f
lu
e
n
ce
s
th
e
ac
ce
ler
atio
n
a
n
d
m
a
n
eu
v
er
in
g
o
f
t
h
e
g
r
o
u
p
.
A
d
d
itio
n
ally
,
wo
u
n
d
ed
s
ar
d
in
es,
m
ar
k
ed
in
ea
ch
cy
cle,
b
ec
o
m
e
p
r
im
e
ca
n
d
id
ates
f
o
r
co
llab
o
r
ativ
e
s
ailf
is
h
h
u
n
tin
g
.
T
h
e
in
ju
r
ed
s
ar
d
in
e
with
th
e
h
ig
h
est
f
itn
e
s
s
at
a
p
ar
ticu
lar
iter
atio
n
a
n
d
th
e
elite
s
ailf
is
h
's
co
o
r
d
in
at
es
ar
e
in
d
icate
d
as
an
d
.
Step
4
.
Attack
alter
n
atio
n
s
tr
at
eg
y
Sailfish
p
r
ed
o
m
in
an
tly
en
g
a
g
e
in
s
o
lo
attac
k
s
o
n
p
r
ey
b
u
t
ca
n
en
h
a
n
ce
th
eir
h
u
n
tin
g
s
u
cc
ess
b
y
co
o
r
d
in
atin
g
attac
k
s
o
v
er
tim
e,
ad
ju
s
tin
g
th
eir
p
o
s
itio
n
s
r
elativ
e
to
o
th
er
h
u
n
ter
s
d
esp
ite
n
o
d
ir
ec
t
co
o
r
d
in
atio
n
.
T
h
is
b
eh
av
io
r
in
s
p
ir
es
th
e
SF
O,
wh
ich
m
im
ics
th
e
s
ai
lf
is
h
'
s
al
ter
n
atin
g
attac
k
s
tr
ateg
y
wh
en
h
u
n
tin
g
in
g
r
o
u
p
s
.
I
n
SF
O,
e
ac
h
s
ar
d
in
e
ad
ju
s
ts
its
p
o
s
itio
n
b
ased
o
n
th
e
o
p
tim
al
lo
ca
ti
o
n
o
f
s
ailf
is
h
a
n
d
attac
k
in
ten
s
ity
to
s
im
u
late
th
is
p
r
o
ce
s
s
ef
f
ec
tiv
ely
.
+
1
=
-
β
(
(
0
,
1
)
)
(
+
2
)
-
(
9
)
Hen
ce
,
wh
er
e
d
en
o
tes
th
e
p
o
s
itio
n
o
f
h
ar
m
ed
s
ar
d
in
es,
w
h
er
e
d
en
o
tes
th
e
lo
ca
tio
n
o
f
e
lite
s
ailf
is
h
,
wh
er
e
(
0
,
1
)
d
en
o
tes
th
e
r
a
n
d
o
m
n
u
m
b
e
r
b
etwe
en
0
a
n
d
1
,
an
d
wh
e
r
e
β
j
d
en
o
tes
th
e
co
e
f
f
icien
t
at
th
iter
atio
n
.
T
o
ac
h
iev
e
g
lo
b
ally
o
p
tim
al
s
o
lu
tio
n
s
in
att
ac
k
d
etec
tio
n
,
th
e
C
SO
is
in
co
r
p
o
r
ate
d
in
to
th
e
alg
o
r
ith
m
.
As a
r
esu
lt,
th
e
C
SO u
p
d
ate
eq
u
atio
n
is
ex
p
r
ess
ed
as
(
1
0
)
:
=
(
1
+
1
1
)
+
+
1
−
+
1
1
1
(
1
0
)
T
h
e
p
r
o
p
o
s
ed
SaC
O'
s
eq
u
atio
n
ca
n
b
e
wr
itten
as
(
1
1
)
,
+
1
=
2
1
1
2
1
1
+
2
−
(
(
0
,
1
)
)
[
(
1
+
1
1
)
+
+
1
1
1
(
1
−
(
(
0
,
1
)
)
2
)
−
2
(
0
,
1
)
+
]
(
1
1
)
wh
er
e,
=
2
∗
(
0
,
1
)
∗
−
.
Her
e,
"
p
r
ey
d
en
s
ity
"
r
ef
er
s
to
th
e
q
u
an
tity
o
f
p
r
e
y
at
ea
ch
iter
atio
n
.
W
h
en
th
e
s
ailf
i
s
h
r
ed
u
ce
th
e
am
o
u
n
t
o
f
p
r
e
y
d
u
r
in
g
g
r
o
u
p
h
u
n
tin
g
,
th
e
is
u
s
ed
to
u
p
d
ate
th
e
s
ailf
i
s
h
's
lo
ca
tio
n
ar
o
u
n
d
t
h
e
p
r
e
y
s
ch
o
o
l.
T
h
e
f
o
r
m
u
la
f
o
r
p
r
ey
d
en
s
i
ty
is
as
(
1
2
)
:
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
15
,
No
.
1
,
Feb
r
u
ar
y
20
25
:
1
1
0
9
-
1
1
2
2
1116
=
1
−
[
+
]
(
1
2
)
wh
er
e,
th
e
ter
m
s
an
d
s
ig
n
if
ies th
e
n
u
m
b
e
r
o
f
t
h
e
s
ailf
is
h
an
d
s
ar
d
in
es in
ev
e
r
y
cy
cle.
Step
5
.
Pu
r
s
u
it a
n
d
p
r
ed
atio
n
Du
r
in
g
t
h
e
c
o
m
m
en
ce
m
e
n
t
o
f
a
h
u
n
t,
b
o
th
th
e
s
ar
d
in
es'
ad
ep
tn
ess
at
ev
asio
n
an
d
th
e
s
ailf
is
h
's
ca
p
ac
ity
f
o
r
attac
k
ar
e
ty
p
ically
at
th
eir
ze
n
ith
.
C
o
n
s
eq
u
en
tly
,
in
th
e
in
itial
p
h
ase
o
f
th
e
h
u
n
t,
s
ailf
is
h
in
f
lict
wo
u
n
d
s
u
p
o
n
th
e
s
ar
d
in
es
with
in
th
e
p
r
ey
s
ch
o
o
l
with
o
u
t
n
e
ce
s
s
ar
ily
en
s
n
ar
in
g
th
em
en
tir
ely
.
T
h
e
im
p
r
ess
iv
e
o
f
f
en
s
iv
e
ca
p
ab
ilit
ies
o
f
s
ailf
i
s
h
d
im
in
is
h
th
e
s
ar
d
in
es
'
ef
f
ic
ac
y
in
escap
in
g
.
T
h
ese
s
ailf
is
h
d
y
n
am
ically
ad
ap
t
th
eir
tactics
as
th
e
s
ar
d
in
es
ac
cr
u
e
in
ju
r
ies,
u
ltima
tely
r
esu
ltin
g
in
a
d
ec
r
ea
s
e
in
th
e
p
r
e
y
'
s
ab
ilit
y
to
ev
ad
e
attac
k
s
as
th
e
h
u
n
t
p
r
o
g
r
ess
es
.
Su
b
s
eq
u
e
n
tly
,
th
e
s
u
cc
ess
r
a
te
o
f
s
ailf
is
h
p
ea
k
s
.
C
o
n
s
eq
u
en
tly
,
ea
ch
s
ar
d
in
e
ad
ju
s
ts
its
p
o
s
itio
n
u
tili
zin
g
a
s
p
ec
if
ic
eq
u
atio
n
tailo
r
e
d
to
th
e
ev
o
lv
in
g
d
y
n
am
ics o
f
th
e
h
u
n
t.
+
1
=
S*
(
-
+
)
(
1
3
)
T
h
e
s
y
m
b
o
ls
+
1
an
d
d
en
o
te
th
e
n
ew
an
d
cu
r
r
e
n
t
lo
ca
tio
n
o
f
s
a
r
d
in
e
j
r
esp
ec
tiv
ely
.
T
h
e
s
y
m
b
o
l
r
ef
er
s
to
a
r
an
d
o
m
n
u
m
b
er
r
a
n
g
in
g
f
r
o
m
0
to
1
.
T
h
e
s
y
m
b
o
l
r
ep
r
e
s
en
ts
th
e
o
p
tim
al
lo
ca
tio
n
o
f
e
lite
s
ailf
i
s
h
.
d
en
o
tes th
e
n
u
m
b
e
r
o
f
s
ailf
is
h
'
s
attac
k
p
o
wer
at
ev
er
y
iter
at
io
n
,
r
ep
r
esen
ted
as
(
1
4
)
,
=
∗
(
1
−
(
2
∗
∗
η
)
)
(
1
4
)
Her
e,
th
e
c
o
ef
f
icien
ts
an
d
η
r
ep
r
esen
t
f
ac
to
r
s
th
at
d
e
cr
ea
s
e
th
e
p
o
wer
o
f
th
e
attac
k
v
alu
e,
an
d
d
en
o
tes
th
e
cu
r
r
en
t
iter
atio
n
.
T
h
e
n
u
m
b
er
o
f
s
ar
d
in
es
th
at
u
p
d
ate
th
eir
lo
ca
tio
n
d
u
r
in
g
th
e
last
p
h
ase
o
f
th
e
h
u
n
t
is
ca
lcu
lated
u
s
in
g
as
(
1
5
)
,
(
1
6
)
:
μ
=
∗
(
1
5
)
λ
=
∗
(
1
6
)
Her
e,
μ
d
en
o
tes
th
e
lo
ca
tio
n
o
f
th
e
s
ar
d
in
e,
wh
ile
λ
r
ep
r
es
en
ts
th
e
n
u
m
b
er
o
f
v
ar
iab
les
ass
o
ciate
d
with
th
e
s
ar
d
in
es.
T
h
e
n
u
m
b
er
o
f
v
ar
ia
b
les
at
iter
atio
n
is
r
ep
r
esen
te
d
as
,
an
d
d
en
o
tes
th
e
n
u
m
b
e
r
o
f
s
ar
d
in
es
in
ea
ch
cy
cle.
W
h
en
th
e
is
le
s
s
th
an
0
.
5
,
th
e
s
ar
d
in
es
with
μ
v
ar
iab
les
ar
e
u
p
d
ated
;
h
o
we
v
er
,
if
th
e
is
g
r
ea
ter
th
an
o
r
e
q
u
al
to
0
.
5
,
t
h
en
th
e
lo
ca
tio
n
o
f
all
s
ar
d
i
n
es is
u
p
d
ated
.
Step
6
.
E
v
alu
ate
f
ea
s
ib
ilit
y
E
q
u
atio
n
(
6
)
is
u
s
ed
to
ca
lcu
late
ea
ch
s
ea
r
ch
ag
en
t'
s
f
itn
es
s
.
T
h
e
f
u
n
ctio
n
th
at
y
ield
s
th
e
b
e
s
t r
esu
lt i
s
k
n
o
wn
as th
e
o
p
tim
u
m
s
o
lu
tio
n
.
W
h
ile
th
e
v
alu
e
th
at
is
h
i
g
h
est r
ef
lects a
n
o
p
tim
al
f
itn
ess
m
ea
s
u
r
e.
Step
7
.
T
er
m
in
atio
n
Un
til
th
e
d
esig
n
ated
iter
atio
n
is
co
m
p
leted
o
r
an
id
ea
l
s
o
lu
t
io
n
is
o
b
tain
ed
,
th
e
p
r
e
v
io
u
s
l
y
in
d
icate
d
p
r
o
ce
d
u
r
es
co
n
tin
u
e.
T
h
e
im
p
lem
en
ted
SaC
O
'
s
p
s
eu
d
o
co
d
e
is
p
r
o
v
id
ed
b
y
Alg
o
r
ith
m
1
an
d
f
lo
wch
ar
t
o
f
th
e
p
r
o
p
o
s
ed
m
o
d
el
is
s
h
o
wn
in
F
ig
u
r
e
5
.
Alg
o
r
ith
m
1
.
Ps
eu
d
o
co
d
e
o
f
th
e
d
ev
elo
p
ed
m
o
d
el
Input: Initial population of sailfish and sardine
Output: Optimal solution (sailfish)
Initialize the
populations of sardine and sailfish, along with relevant parameters
Compute the fitness of each individual in the population
Determine the optimal sailfish and sardine based on their fitness
while the stopping criteria are not met:
for each sailfish:
Compute the coefficient β
j
at the current iteration
Update the Sailfish Cat Optimization (SaCO) using equation (11)
end for
Compute the attack power BA based on equation (14)
if BA<0.5:
Compute μ based on equation (15)
Estimate λ based on equation (16)
Choose the sardine using the value of μ and λ
Update the location of the selected sardine using equation (13)
else
Update the location of all sardines using equation (13)
end if
Compute the fitness of all sardine
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
S
a
ilfi
s
h
-
ca
t a
lg
o
r
ith
m
-
en
h
a
n
c
ed
g
en
era
tive
a
d
ve
r
s
a
r
ia
l
n
etw
o
r
k
fo
r
…
(
P
a
lla
vi
K
a
n
th
a
m
a
n
g
a
la
N
ir
a
n
ja
n
)
1117
if a better solution is found in the sardine population:
Update the optimal sailfish with the improved sardine
Eliminate the hunted sardine
Update best sailfish and sardine
end if
end
while
Return the best sailfish as the optimal solution.
End
Fig
u
r
e
5
.
Flo
wch
ar
t
o
f
th
e
d
ev
elo
p
ed
m
o
d
el
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
is
s
ec
tio
n
ex
p
lo
r
es
th
e
r
esu
lts
o
f
th
e
p
r
o
p
o
s
ed
attac
k
d
etec
tio
n
s
tr
ateg
y
in
th
e
I
o
T
-
Fo
g
co
n
tex
t,
p
r
o
v
id
i
n
g
d
etails
o
n
t
h
e
ex
p
e
r
im
en
tal
s
etu
p
an
d
d
ataset
d
es
cr
ip
tio
n
.
T
h
e
SaC
O
-
b
ased
g
en
er
ativ
e
ad
v
e
r
s
ar
ial
n
etwo
r
k
is
im
p
lem
en
ted
u
s
in
g
Py
th
o
n
o
n
a
W
in
d
o
ws
1
0
s
y
s
tem
with
an
I
n
tel
p
r
o
ce
s
s
o
r
a
n
d
4
GB
o
f
R
AM
.
T
h
e
s
tr
ateg
y
u
tili
ze
s
th
e
B
o
T
-
I
o
T
d
atab
ase
[
2
8
]
,
en
co
m
p
ass
in
g
a
d
iv
er
s
e
ar
r
ay
o
f
n
o
r
m
a
l
an
d
b
o
tn
et
tr
af
f
ic
ca
teg
o
r
ized
in
C
SV,
Ar
g
u
s
,
an
d
PC
AP
f
o
r
m
ats,
to
talin
g
o
v
er
7
2
m
illi
o
n
r
ec
o
r
d
s
.
T
h
e
d
ataset
co
n
s
is
t
s
o
f
PC
AP
f
iles
to
talin
g
u
p
to
6
9
.
3
GB
an
d
C
SV
f
lo
w
tr
af
f
ic
d
ata
o
f
1
6
.
7
GB
.
I
t
in
clu
d
es
v
ar
io
u
s
ty
p
es
o
f
attac
k
s
s
u
ch
as
d
ata
ex
f
iltra
tio
n
,
d
en
ia
l o
f
s
er
v
ice
(
Do
S
)
,
DDo
S,
s
er
v
ice
s
ca
n
,
k
ey
lo
g
g
in
g
,
an
d
OS a
ttack
s
.
3
.
1
.
Co
m
pa
ra
t
iv
e
t
ec
hn
iqu
es
Ou
r
p
r
o
p
o
s
ed
tech
n
iq
u
e
SaC
O
b
ased
GAN
co
m
p
ar
ed
with
k
-
n
e
u
r
al
n
etwo
r
k
(
KNN)
,
s
u
p
p
o
r
t
v
ec
t
o
r
m
ac
h
in
e
(
SVM)
[
2
5
]
,
n
eu
r
al
n
etwo
r
k
(
NN)
[
1
5
]
,
lin
ea
r
r
eg
r
ess
io
n
,
d
ee
p
c
o
n
v
o
l
u
tio
n
al
n
eu
r
al
n
etwo
r
k
(
DC
NN)
[
1
6
]
,
an
d
GAN
[
2
9
]
,
b
ased
o
n
r
ec
all,
p
r
ec
is
io
n
a
n
d
F
-
Me
asu
r
e
ev
alu
atio
n
m
etr
i
cs.
Pre
cisi
o
n
,
r
ec
all,
an
d
F
-
m
ea
s
u
r
e
ar
e
im
p
o
r
tan
t
m
etr
ics to
d
eter
m
in
e
alg
o
r
ith
m
p
er
f
o
r
m
an
ce
b
ec
a
u
s
e
th
ey
c
o
llectiv
ely
ass
es
s
th
e
ac
cu
r
ac
y
in
id
en
tify
in
g
attac
k
s
(
p
r
ec
is
io
n
)
,
th
e
co
m
p
lete
n
ess
o
f
attac
k
d
etec
tio
n
(
r
ec
all)
,
an
d
p
r
o
v
i
d
e
a
b
alan
ce
d
m
ea
s
u
r
e
th
at
c
o
n
s
id
er
s
b
o
th
ac
c
u
r
ac
y
an
d
co
m
p
r
eh
en
s
iv
en
ess
(
F
-
m
ea
s
u
r
e)
,
cr
u
cial
f
o
r
ev
alu
atin
g
th
e
ef
f
ec
tiv
en
ess
o
f
s
ec
u
r
ity
m
ea
s
u
r
es in
I
o
T
-
Fo
g
en
v
ir
o
n
m
e
n
ts
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
15
,
No
.
1
,
Feb
r
u
ar
y
20
25
:
1
1
0
9
-
1
1
2
2
1118
3
.
2
.
Co
m
pa
ra
t
iv
e
a
na
ly
s
is
T
h
is
s
ec
tio
n
p
r
o
v
id
es
a
d
etailed
co
m
p
ar
ativ
e
a
n
aly
s
is
o
f
t
h
e
attac
k
d
etec
tio
n
m
o
d
el
in
th
e
I
o
T
-
Fo
g
en
v
ir
o
n
m
en
t,
f
o
cu
s
in
g
o
n
th
r
e
e
d
is
tin
ct
ty
p
es
o
f
attac
k
s
:
b
a
ck
d
o
o
r
attac
k
s
,
f
u
zz
e
r
s
attac
k
s
,
an
d
Do
S
attac
k
s
.
T
h
e
p
e
r
f
o
r
m
an
ce
e
v
alu
atio
n
o
f
th
e
p
r
o
p
o
s
ed
SaC
O
-
b
ased
GAN
m
eth
o
d
is
ass
ess
ed
u
s
i
n
g
m
etr
ics
s
u
c
h
as
p
r
ec
is
io
n
,
r
ec
all,
an
d
F
-
m
ea
s
u
r
e.
3
.
2
.
1
.
Ana
ly
s
is
ba
s
ed
o
n ba
c
k
do
o
r
a
t
t
a
c
k
Fig
u
r
e
6
p
r
o
v
i
d
es
a
co
m
p
ar
ativ
e
ev
alu
atio
n
o
f
th
e
SaC
O
-
b
ased
GAN
m
eth
o
d
s
p
ec
if
ically
d
ev
elo
p
e
d
f
o
r
d
etec
tin
g
b
ac
k
d
o
o
r
attac
k
s
.
I
n
Fig
u
r
e
6
(
a
)
,
a
co
m
p
a
r
is
o
n
o
f
r
ec
all
is
p
r
esen
ted
,
r
ev
ea
lin
g
v
ar
iatio
n
s
with
ch
an
g
es
in
th
e
p
er
ce
n
tag
e
o
f
tr
ain
in
g
d
ata.
Sp
ec
if
ically
,
wi
th
7
0
%
tr
ain
in
g
d
ata,
ex
is
tin
g
m
eth
o
d
s
s
u
ch
as
KNN,
SVM,
NN,
lin
ea
r
r
e
g
r
e
s
s
io
n
,
DC
NN,
GAN,
an
d
th
e
d
ev
elo
p
e
d
SaC
O
-
b
ased
GAN
ex
h
ib
it
r
ec
all
v
alu
es
o
f
8
3
.
4
1
%,
8
3
.
7
3
%,
8
3
.
7
9
%,
8
5
.
2
3
%,
8
5
.
6
7
%,
8
6
.
7
3
%,
a
n
d
8
7
.
2
3
%,
r
esp
ec
tiv
ely
.
Ad
d
it
io
n
ally
,
Fig
u
r
e
6
(
b
)
d
is
p
lay
s
a
co
m
p
ar
ativ
e
an
aly
s
is
o
f
p
r
ec
is
io
n
v
al
u
es
ac
r
o
s
s
d
if
f
er
en
t
p
e
r
ce
n
tag
es
o
f
tr
ain
in
g
d
ata.
At
a
tr
ain
i
n
g
d
ata
p
er
ce
n
tag
e
o
f
7
0
%,
KNN,
SVM,
N
N,
lin
ea
r
r
eg
r
ess
i
o
n
,
DC
NN,
GAN,
an
d
th
e
d
ev
elo
p
ed
SaC
O
-
b
ased
GAN
d
em
o
n
s
tr
ate
p
r
ec
is
io
n
v
alu
es
o
f
8
1
.
5
9
%,
8
2
.
7
1
%,
8
2
.
7
7
%,
8
3
.
0
9
%,
8
3
.
2
1
%,
8
5
.
9
9
%,
an
d
8
7
.
8
5
%,
r
esp
ec
tiv
ely
.
Fu
r
th
er
m
o
r
e,
Fig
u
r
e
6
(
c)
r
e
p
r
esen
ts
a
co
m
p
a
r
ativ
e
an
aly
s
is
o
f
F
-
m
ea
s
u
r
e
v
alu
es
with
v
ar
y
in
g
p
er
ce
n
tag
es
o
f
tr
ain
i
n
g
d
ata.
T
h
e
e
x
is
tin
g
attac
k
d
etec
tio
n
m
eth
o
d
s
(
KNN,
SVM,
NN
,
lin
ea
r
r
eg
r
ess
io
n
,
DC
NN,
GA
N)
an
d
th
e
p
r
o
p
o
s
ed
SaC
O
-
b
ased
GAN
m
eth
o
d
ac
h
iev
e
F
-
m
ea
s
u
r
e
v
alu
es
o
f
8
2
.
0
8
%,
8
2
.
2
7
%,
8
2
.
7
2
%,
8
4
.
0
7
%,
8
4
.
3
6
%,
8
5
.
4
4
%,
an
d
8
7
.
0
7
%
wh
en
tr
ai
n
ed
with
7
0
%
o
f
th
e
d
ata.
T
h
is
em
p
h
asizes
th
e
b
alan
ce
d
p
er
f
o
r
m
a
n
ce
o
f
th
e
SaC
O
-
b
ased
GAN
in
b
o
th
r
ec
all
an
d
p
r
ec
is
io
n
,
h
ig
h
lig
h
ti
n
g
its
ca
p
ab
ilit
y
to
ef
f
ec
tiv
ely
d
etec
t b
ac
k
d
o
o
r
attac
k
s
ac
r
o
s
s
v
ar
y
in
g
tr
ain
in
g
d
ata
s
ce
n
ar
io
s
.
I
n
s
u
m
m
ar
y
,
Fi
g
u
r
e
6
d
e
m
o
n
s
tr
ates
th
at
th
e
SaC
O
-
b
ased
GAN
m
e
th
o
d
o
u
tp
er
f
o
r
m
s
tr
ad
itio
n
al
m
eth
o
d
s
in
r
ec
all,
p
r
ec
is
io
n
,
a
n
d
F
-
m
ea
s
u
r
e
wh
e
n
d
etec
tin
g
b
ac
k
d
o
o
r
attac
k
s
in
I
o
T
-
Fo
g
en
v
ir
o
n
m
en
t.
T
h
e
r
esu
lts
u
n
d
er
s
co
r
e
its
p
o
ten
tial
as
an
ad
v
an
ce
d
s
ec
u
r
ity
s
o
lu
tio
n
f
o
r
m
itig
atin
g
b
ac
k
d
o
o
r
th
r
ea
ts
ef
f
ec
tiv
ely
.
(
a)
(
b
)
(
c)
Fig
u
r
e
6
.
C
o
m
p
a
r
ativ
e
an
aly
s
i
s
u
s
in
g
b
ac
k
d
o
o
r
attac
k
(
a)
r
ec
all,
(
b
)
p
r
ec
is
io
n
,
an
d
(
c)
F
-
m
e
asu
r
e
Evaluation Warning : The document was created with Spire.PDF for Python.