Enhancing Security in Smart Contracts with AI-Powered Solutions

The
growth
of

blockchain
technology
is
not
a
new
phenomenon
in
the
technical
landscape.
Blockchain
not
only
serves
the
foundations
of

cryptocurrencies
but
also
utilizes

smart
contracts
for
developing
innovative
applications.
Smart
contracts
can
help
automate
and
execute
agreements
without
the
involvement
of
intermediaries.
The
contracts
can
improve
efficiency,
security,
and
transparency.

However,
they
are
not
completely
immune
to
different
errors
and
security
vulnerabilities.
The
rising
interest
in

smart
contract
security
is
proof
of
the
fact
that
security
issues
can
affect
the
use
of
smart
contracts.
With
the
help
of
a
review
of
the
existing
state
of
security
for
smart
contracts,
you
can
understand
why
you
need
innovative
approaches
for
safety
of
smart
contracts.
Let
us
find
out
more
about
the
implications
of

AI
for
improving
security
of
smart
contracts.

Build
your
identity
as
a
certified
blockchain
expert
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career
prospects.

Existing
State
of
Security
for
Smart
Contracts

Smart
contracts
are
one
of
the
vital
components
of
the
blockchain
landscape.
They
help
in
automation
of
complex
financial
transactions
and
workflows
for
exchanging
information
without
the
involvement
of
intermediaries.
Blockchain
technology
has
formidable
potential
for
introducing
fundamental
changes
in
the
approaches
to
conducting
business.

However,
some
of
the
high-profile
smart
contract
security
breaches
have
led
to
significant
financial
losses
and
damage
to
reputation
of
projects.
Before
you
find
the
answers
to
“How
does
AI
improve
smart
contracts
security?”
you
should
know
about
the
notable
security
concerns
that
affect
smart
contracts.
Some
of
the
prominent
security
issues
for
smart
contracts
include
errors
in
code,
malicious
inputs,
and
insecure
third-party
integrations.

The
first
notable
attack
on
smart
contracts
was
the

DAO
hack
in
2016,
which
led
to
a
loss
of
over
$50
million.
Developers
had
to
implement
a
hard
fork
of
the

Ethereum
blockchain
to
restore
the
missing
funds.
In
July
2017,
the
Parity
wallet
breach
resulted
in
theft
of
over
$30
million.
The
primary
reason
behind
the
Parity
wallet
breach
was
the
vulnerability
in
a
smart
contract
library.

The
necessity
of
AI-powered
smart
contracts
for
improving
security
can
also
point
to
the
smart
contract
exploits
on

Binance
Smart
Chain
in
2021.
Another
notable
platform
that
suffered
the
brunt
of
smart
contract
security
breaches
is
KingDice.
Hackers
leveraged
a
flaw
in
the
network’s
smart
contracts
to
steal
almost
$300,000
in
August
2017.

Such
types
of
incidents
emphasize
the
need
for
ensuring
security
of
smart
contracts
and
monitoring
smart
contracts
for
security
against
common
attacks
and
vulnerabilities.
On
the
other
hand,
it
is
difficult
to
ensure
security
of
smart
contracts
without
the
prominent
challenges.
Why
do
you
need
AI
smart
contracts
to
ensure
better
security?
The
complexity
of
the
process
for
analyzing
and
verification
presents
a
major
challenge,
even
with
the
comprehensive
verification
and
code
review
process.

On
top
of
it,
the
lack
of
uniformity
in
the
smart
contract
development
environments
can
create
complications
in
ensuring
safety.
Therefore,
AI
and

ML
have
emerged
as
major
tools
for
addressing
the
challenges
in
detection
of

smart
contract
security
issues.
Artificial
intelligence
and
machine
learning
can
support
the
proactive
detection
of
anomalies
and
potential
risks
in
smart
contracts,
thereby
improving
their
overall
security.

Want
to
learn
about
the
critical
vulnerabilities
and
security
risks
in
smart
contract
development?
Enroll
now
in

Smart
Contracts
Security
Course

What
is
the
Recommended
Technique
for
Security
of
Smart
Contracts?

The
foremost
approach
for
ensuring
smart
contract
security
points
at

smart
contract
audits.
Smart
contract
auditing
focuses
on
a
comprehensive
examination
of
the
code
of
a
contract
for
identification
of
potential
bugs
or
vulnerabilities.
Traditional
methods
for
smart
contract
audits
include
manual
reviews
and
testing.

Manual
audits
can
serve
as
effective
solutions
for
ensuring
security
of
smart
contracts.
However,
traditional
smart
contract
audits
are
expensive
and
time-consuming
tasks
that
present
concerns
of
human
error.
At
this
point
of
time,
artificial
intelligence
can
offer
a
revolutionary
transformation
of
smart
contract
audits.

How
Can
AI
Help
in
Improving
Smart
Contract
Audits?

Smart
contract
auditing
can
be
improved
by
using
artificial
intelligence
tools
and
innovative
capabilities.
You
can
find
multiple
ways
to
enhance
smart
contract
audits
through
AI.
The
artificial
intelligence
smart
contracts
equation
could
help
in
ensuring
major
improvements
in
the
following
areas.

• Natural
  Language
  Processing

Natural
language
processing,
or
NLP,
is
an
important
AI
tool
that
can
help
improve
smart
contract
audits.
NLP
techniques
can
help
in
extracting
relevant
insights
from
audit
reports
of
smart
contracts.
It
can
support
auditors
in
faster
assessment
of
the
overall
security
status
of
smart
contracts
through
automatic
summarization
of
the
findings
from
smart
contract
audit
reports.
NLP
techniques
can
help
in
easier
identification
of
critical
issues
alongside
offering
effective
recommendations
for
resolving
the
issues.

• Dynamic
  Analysis
  and
  Extended
  Test
  Coverage

Artificial
intelligence
could
also
improve
the
smart
contract
testing
process
through
generation
and
execution
of
test
cases
for
assessment
of
smart
contract
behavior
in
different
conditions.
The
best
practices
for
ensuring
smart
contracts
security
with
AI
involve
simulation
of
different
scenarios
through
test
cases.
AI
algorithms
can
help
in
identification
of
edge
cases
and
potential
vulnerabilities
which
cannot
be
identified
through
manual
testing.
It
can
ensure
better
test
coverage
alongside
ensuring
resilience
of
smart
contracts.

• Static
  Analysis

Artificial
intelligence
can
help
in
improving
security
of
smart
contracts
through
static
analysis
of
smart
contract
code.
AI
tools
enable
automated
identification
of
common
security
vulnerabilities.
AI
algorithms
can
help
in
analysis
of
code
structure
and
identification
of
common
patterns
followed
by
comparing
the
patterns
with
known
vulnerabilities.
The
automated
approach
can
support
auditors
in
faster
identification
of
potential
risks,
thereby
saving
your
time
and
effort.

Machine
Learning
Applications
for
Detecting
Vulnerabilities

Machine
learning
algorithms
are
trained
with
massive
amounts
of
smart
contract
data
for
recognizing
patterns
and
identification
of
potential
vulnerabilities.
ML
algorithms
can
ensure
smart
contract
security
by
continuously
learning
from
historical
data
to
detect
common
coding
flaws,
such
as

integer
overflow
or

reentrancy
attacks.
Machine
learning
models
can
ensure
continuous
improvements
in
accuracy
and
serve
as
crucial
tools
for
auditors.

What
are
the
Benefits
of
AI
for
Smart
Contract
Audits?

Artificial
intelligence
can
help
in
improving
security
of
smart
contracts
with
prominent
value-based
advantages.
Here
are
some
of
the
notable
advantages
of
artificial
intelligence
for
improving
smart
contract
audits.

AI
algorithms
can
help
in
analysis
of
code
with
improved
accuracy
by
reducing
the
possibilities
of
human
error
that
happens
during
manual
reviews.
Automated
processes
in
AI-based
smart
contract
audits
can
help
ensure
better
readability
of
smart
contract
audits
and
consistency
of
audit
reports.

• Efficiency

Another
addition
among
responses
to
“How
does
AI
improve
smart
contracts
security?”
points
to
the
improvements
in
efficiency.
Smart
contract
audits
with
artificial
intelligence
can
help
reduce
the
efforts
and
time
required
for
identification
of
potential
vulnerabilities.
AI
can
not
only
ensure
that
you
find
security
risks
in
smart
contracts
quickly
but
also
offer
other
improvements.
Auditors
can
use
artificial
intelligence
for
faster

deployment
of
smart
contracts
with
better
status
and
overall
security.

• Continuous
  Improvement

Artificial
intelligence
algorithms
could
learn
continuously
about
new
vulnerabilities
and
attack
vectors.
The
attack
vectors
can
ensure
continuous
improvements
in
audits’
ability
to
detect
potential
risks.
You
can
rely
on
AI-powered
smart
contracts
to
leverage
the
adaptive
nature
of
AI.
Artificial
intelligence
can
adapt
to
emerging
threats
by
maintaining
higher
standards
of
security.

• Scalability

The
benefits
of
AI
smart
contracts
can
help
ensure
effective
processing
of
massive
volumes
of
data
and
code.
Artificial
intelligence
can
help
auditors
in
scaling
up
their
efforts
without
any
impact
on
quality.
The
advantages
of
scalability
are
that
it
can
support
the
rapidly
growing

blockchain
ecosystems
where
you
can
find
multiple
smart
contracts
being
created
and
deployed
every
day.

Start
learning
Smart
Contracts
and
its
development
tools
with
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Contracts Skill
Path with
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Innovative
AI
Techniques
for
Smart
Contract
Vulnerability
Detection

The
importance
of
artificial
intelligence
for
security
of
smart
contracts
extends
beyond
the
use
of
AI
for
smart
contract
audits.
You
must
also
notice
that
artificial
intelligence
smart
contracts
could
leverage
different
innovative
techniques
to
offer
better
security.
Smart
contract
developers
can
use
the
following
AI-powered
techniques
to
improve
security
of
smart
contracts.

• Temporal
  Message
  Propagation
  Network

The
Temporal
Message
Propagation
Network
can
help
in
ensuring
vulnerability
detection
by
leveraging
graph
features.
With
the
help
of
graph
neural
networks
and
expertise
in
smart
contracts,
developers
can
detect
weaknesses
in
smart
contracts.
The
unique
approach
considers
the
temporal
propagation
of
messages
in
the
contract
execution,
thereby
ensuring
better
accuracy
in
vulnerability
detection.

• Limited
  Labeled
  Data
  with
  ASSBert

The
use
of
AI-powered
smart
contracts
for
improvements
in
security
also
points
to
limited
labeled
data
with
ASSBert.
One
of
the
common
challenges
of
AI-based
systems
is
the
limited
availability
of
labeled
data.
Smart
contract
vulnerability
detection
systems
can
leverage
Semi-Supervised
Learning
or
SSL
and
bidirectional
encoder
representations
from
transformer
models
such
as
BERT.
Such
approaches
can
help
vulnerability
detection
systems
by
using
labeled
and
unlabeled
data
for
enhanced
abilities
to
identify
vulnerabilities.

• Two-Stage
  SC
  Debugger
  ReVuIDL

The
detection
and
identification
of
smart
contract
vulnerabilities
is
a
challenging
task.
However,
the
answers
to
“How
does
AI
improve
smart
contracts
security?”
also
point
to
the
use
of
a
two-stage
SC
debugger,
complemented
with

deep
learning
techniques.
The
debugger
has
been
tailored
specifically
for
identification
and
location
of
reentrancy
vulnerabilities,
which
emerge
from
improper
management
of
external
calls
in
the
contract
code.
With
the
help
of
deep
learning
techniques,
the
debugger
offers
an
accurate
and
automated
method
for
identifying
and
locating
vulnerabilities.

• Multi-modal
  AI
  Framework
  VDDL

The
multi-modal
AI
framework
VDDL
technique
can
support
the
detection
of
vulnerabilities
through
analysis
of
contract
code
from
different
perspectives.
The
bidirectional
nature
of
the
transformer
model
offers
a
comprehensive
understanding
of
the
structure
and
context
of
the
code.

Familiarize
yourself
with
the
complete
Ethereum
smart
contract
development
lifecycle
and
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fluency
in
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best
practices
for
smart
contract
coding,
testing,
and
deployment
with Smart
Contracts
Development
Course.

What
are
the
Innovative
Advancements
in
Vulnerability
Detection?

The
applications
of
artificial
intelligence
for
smart
contract
security
also
invite
attention
to
the
innovations
in
vulnerability
detection.
You
can
find
innovative
techniques
such
as
MulCas,
SCSGuard,
SHGTNs,
and
real-time
fraud
detection
for
detecting
vulnerabilities
in
smart
contracts.

Artificial
intelligence
has
powered
innovation
in
the
domain
of
vulnerability
detection.
For
example,
the
MulCas
model
complemented
with
a
larger
dataset
can
help
identify
Ponzi
schemes
during
the
development
of
smart
contracts.
SCSGuard
is
another
innovative
AI
technology
that
utilizes
artificial
intelligence
for
identifying
fraudulent
activity
through
incorporation
of
bytecode
and
Gated
Recurrent
Unit
or
GRU
networks.

Artificial
intelligence
can
also
support
real-time
smart
contract
reviews
with
the
help
of
algorithms
that
support
tracking
of
money
flow.
The
real-time
detection
of
irregularities
in
smart
contracts
can
help
developers
identify
unusual
transactions
and
implement
preemptive
actions.

What
are
the
Latest
Advancements
in
AI-based
Vulnerability
Detection?

The
latest
advancements
in
AI-based
vulnerability
detection
also
showcase
the
role
of
artificial
intelligence
in
detecting
vulnerabilities.
First
of
all,
you
can
notice
the
example
of
ICodeNet
for
vulnerability
detection.
The
tool
has
been
designed
to
tackle
the
issues
with
local
information
loss,
which
is
a
major
challenge
for
vulnerability
detection.
ICodeNet
leverages
convolutional
layers
for
capturing
intricate
patterns
in
the
source
code
of
the
contract.
It
can
help
in
better
identification
of
vulnerabilities
with
emphasis
on
the
localized
details.

Another
example
of
innovation
in
smart
contracts
security
with
AI
draws
attention
towards
multi-agent
fuzz
testing.
The
combination
of
multi-agent
fuzz
testing
and
deep
reinforcement
learning
can
help
in
unraveling
the
vulnerabilities
in
execution
environment
of
smart
contracts
and
the
interaction
dynamics.

The
multi-agent
approach
facilitates
a
broader
exploration
of
potential
scenarios
for
attacks
on
smart
contracts
with
robust
security
assessments.
Furthermore,
deep
learning
models
such
as
Artificial
Neural
Networks,
Long
Short-Term
Memory,
and
Gated
Recurrent
Units
can
also
help
in
assessment
of
smart
contract
vulnerabilities.

Want
to
know
the
real-world
examples
of
smart
contracts
and
understand
how
you
can
use
it
for
your
business?
Check
the
presentation
Now
on Examples
Of
Smart
Contracts

Bottom
Line

The
uses
of

artificial
intelligence
for
improving
security
of

smart
contracts
can
provide
significant
improvements.
Artificial
intelligence
offers
the
value
advantages
of
automation,
efficiency,
and
accuracy
in
smart
contract
vulnerability
detection.
The
advantages
of
AI-powered
smart
contracts
are
that
they
can
offer
significant
improvements
in
the
security
of
smart
contracts
through
effective
audits.

AI-powered
techniques
such
as
dynamic
analysis,
machine
learning,
and
natural
language
processing
can
improve
smart
contract
audits.
On
top
of
it,
innovative
AI
techniques
for
smart
contract
vulnerability
detection
can
also
provide
exclusive
value
advantages
for
smart
contract
development.
For
example,
multi-agent
fuzz
testing
and
deep
learning
models
can
offer
significant
improvements
for
smart
contract
vulnerability
detection.
Learn
more
about
the
uses
of
artificial
intelligence
for
improving
security
of
smart
contracts
now.

*Disclaimer:
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article
should
not
be
taken
as,
and
is
not
intended
to
provide
any
investment
advice.
Claims
made
in
this
article
do
not
constitute
investment
advice
and
should
not
be
taken
as
such.
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Blockchains
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loss
sustained
by
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person
who
relies
on
this
article.
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