Analysis and Prevention of AI-Based Phishing Email Attacks
Electronics,
Год журнала:
2024,
Номер
13(10), С. 1839 - 1839
Опубликована: Май 9, 2024
Phishing
email
attacks
are
among
the
most
common
and
harmful
cybersecurity
attacks.
With
emergence
of
generative
AI,
phishing
can
be
based
on
emails
generated
automatically,
making
it
more
difficult
to
detect
them.
That
is,
instead
a
single
format
sent
large
number
recipients,
AI
used
send
each
potential
victim
different
email,
for
systems
identify
scam
before
reaches
recipient.
Here,
we
describe
corpus
AI-generated
emails.
We
also
use
machine
learning
tools
test
ability
automatic
text
analysis
The
results
encouraging,
show
that
an
with
high
accuracy
compared
regular
or
human-generated
By
applying
descriptive
analytics,
specific
differences
between
manually
crafted
profiled
in
their
style
from
scams.
Therefore,
identification
as
warning
user.
paper
describes
made
open
public
consequent
studies.
While
is
emails,
therefore,
important
train
order
repel
future
powered
by
AI.
Язык: Английский
A data science and machine learning approach to continuous analysis of Shakespeare's plays
Journal of Data Mining & Digital Humanities,
Год журнала:
2023,
Номер
2023
Опубликована: Июль 13, 2023
The
availability
of
quantitative
text
analysis
methods
has
provided
new
ways
analyzing
literature
in
a
manner
that
was
not
available
the
pre-information
era.
Here
we
apply
comprehensive
machine
learning
to
work
William
Shakespeare.
shows
clear
changes
style
writing
over
time,
with
most
significant
sentence
length,
frequency
adjectives
and
adverbs,
sentiments
expressed
text.
Applying
make
stylometric
prediction
year
play
Pearson
correlation
0.71
between
actual
predicted
year,
indicating
Shakespeare's
as
reflected
by
measurements
changed
time.
Additionally,
it
stylometrics
some
plays
is
more
similar
written
either
before
or
after
they
were
written.
For
instance,
Romeo
Juliet
dated
1596,
but
Shakespeare
1600.
source
code
for
free
download.
Язык: Английский
A data science and machine learning approach to continuous analysis of Shakespeare's plays
arXiv (Cornell University),
Год журнала:
2023,
Номер
unknown
Опубликована: Янв. 1, 2023
The
availability
of
quantitative
text
analysis
methods
has
provided
new
ways
analyzing
literature
in
a
manner
that
was
not
available
the
pre-information
era.
Here
we
apply
comprehensive
machine
learning
to
work
William
Shakespeare.
shows
clear
changes
style
writing
over
time,
with
most
significant
sentence
length,
frequency
adjectives
and
adverbs,
sentiments
expressed
text.
Applying
make
stylometric
prediction
year
play
Pearson
correlation
0.71
between
actual
predicted
year,
indicating
Shakespeare's
as
reflected
by
measurements
changed
time.
Additionally,
it
stylometrics
some
plays
is
more
similar
written
either
before
or
after
they
were
written.
For
instance,
Romeo
Juliet
dated
1596,
but
Shakespeare
1600.
source
code
for
free
download.
Язык: Английский