The Future of Network Security: Predictive Analytics and ML-Driven Solutions

As
the
digital
age
evolves
and
continues
to
shape
the
business
landscape,
corporate
networks
have
become
increasingly
complex
and
distributed.
The
amount
of
data
a
company
collects
to
detect
malicious
behaviour
constantly
increases,
making
it
challenging
to
detect
deceptive
and
unknown
attack
patterns
and
the
so-called
“needle
in
the
haystack”.
With
a
growing
number
of
cybersecurity
threats,
such
as
data
breaches,
ransomware
attacks,
and
malicious
insiders,
organizations
are
facing
significant
challenges
in
successfully
monitoring
and
securing
their
networks.
Furthermore,
the
talent
shortage
in
the
field
of
cybersecurity
makes
manual
threat
hunting
and
log
correlation
a
cumbersome
and
difficult
task.
To
address
these
challenges,
organizations
are
turning
to
predictive
analytics
and
Machine
Learning
(ML)
driven
network
security
solutions
as
essential
tools
for
securing
their
networks
against
cyber
threats
and
the
unknown
bad.

The
Role
of
ML-Driven
Network
Security
Solutions

ML-driven
network
security
solutions
in
cybersecurity
refer
to
the
use
of
self-learning
algorithms
and
other
predictive
technologies
(statistics,
time
analysis,
correlations
etc.)
to
automate
various
aspects
of
threat
detection.
The
use
of
ML
algorithms
is
becoming
increasingly
popular
for
scalable
technologies
due
to
the
limitations
present
in
traditional
rule-based
security
solutions.
This
results
in
the
processing
of
data
through
advanced
algorithms
that
can
identify
patterns,
anomalies,
and
other
subtle
indicators
of
malicious
activity,
including
new
and
evolving
threats
that
may
not
have
known
bad
indicators
or
existing
signatures.

Detecting
known
threat
indicators
and
blocking
established
attack
patterns
is
still
a
crucial
part
of
overall
cyber
hygiene.
However,
traditional
approaches
using
threat
feeds
and
static
rules
can
become
time-consuming
when
it
comes
to
maintaining
and
covering
all
the
different
log
sources.
In
addition,
Indicators
of
Attack
(IoA)
or
Indicators
of
Compromise
(IoC)
may
not
be
available
at
the
time
of
an
attack
or
are
quickly
outdated.
Consequently,
companies
require
other
approaches
to
fill
this
gap
in
their
cybersecurity
posture.

In
summary,
the
mentioned
drawbacks
of
rule-based
security
solutions
highlight
the
significance
of
taking
a
more
holistic
approach
to
network
security,
which
should
nowadays
include

ML-powered
Network
Detection
and
Response
(NDR)
solutions
to
complement
traditional
detection
capabilities
and
preventive
security
measures.

The
Benefits
of
ML
for
Network
Security

So,
how
is
Machine
Learning
(ML)
shaping
the
future
of
network
security?
The
truth
is
ML-powered
security
solutions
are
bringing
about
a
significant
transformation
in
network
security
by
providing
security
teams
with
numerous
benefits
and
enhancing
the
overall
threat
detection
capabilities
of
organizations:

• 
  Big
  data
  analytics:With
  the
  ever-increasing
  amount
  of
  data
  and
  different
  log
  sources,
  organisations
  must
  be
  able
  to
  process
  vast
  amounts
  of
  information
  in
  real-time,
  including
  network
  traffic
  logs,
  endpoints,
  and
  other
  sources
  of
  information
  related
  to
  cyber
  threats.
  In
  this
  regard,
  ML
  algorithms
  can
  aid
  in
  the
  detection
  of
  security
  threats
  by
  identifying
  patterns
  and
  anomalies
  that
  may
  otherwise
  go
  unnoticed.
  Consequently,
  the
  ability
  and
  flexibility
  of
  a
  solution
  to
  incorporate
  different
  log
  sources
  should
  be
  a
  key
  requirement
  for
  threat
  detection
  capabilities.
• 
  Automated
  analysis
  of
  anomalous
  behavior:
  AI
  enables
  a
  much-required
  health
  monitoring
  of
  network
  activity
  by
  utilising
  the
  analysis
  of
  normal
  network
  traffic
  as
  a
  baseline.
  With
  the
  help
  of
  automated
  correlation
  and
  clustering,
  outliers
  and
  unusual
  behavior
  can
  be
  detected,
  reducing
  the
  need
  for
  manual
  detection
  engineering
  and
  threat
  hunting.
  Key
  questions
  to
  be
  answered
  include
  “what
  is
  the
  activity
  of
  other
  clients
  in
  the
  network?”
  and
  “is
  a
  client’s
  behavior
  in
  line
  with
  its
  own
  previous
  activities?”
  These
  approaches
  allow
  for
  the
  detection
  of
  unusual
  behaviors
  like
  domain-generated
  algorithms
  (DGA)
  domains,
  volume-based
  irregularities
  in
  network
  connections,
  and
  unusual
  communication
  patterns
  (e.g.,
  lateral
  movement)
  in
  the
  network.
  Therefore,
  comparing
  a
  client’s
  current
  behavior
  with
  that
  of
  its
  peers
  serves
  as
  a
  suitable
  baseline
  for
  identifying
  subtle
  anomalies.
• 
  Detect
  unknown
  attacks
  in
  real-time:
  Whileit
  is
  relatively
  easy
  to
  directly
  detect
  known
  bad
  indicators
  (specific
  IP
  addresses,
  domains
  etc.),
  many
  attacks
  can
  go
  undetected
  when
  these
  indicators
  are
  not
  present.
  If
  that
  is
  the
  case,
  statistics,
  time
  and
  correlation-based
  detections
  are
  of
  enormous
  value
  to
  detect
  unknown
  attack
  patterns
  in
  an
  automated
  manner.
  By
  incorporating
  algorithmic
  approaches,
  traditional
  security
  solutions
  based
  on
  signatures
  and
  indicators
  of
  compromise
  (IoC)
  can
  be
  enhanced
  to
  become
  more
  self-sufficient
  and
  less
  reliant
  on
  known
  malware
  indicators.
• 
  Self-learning
  detection
  capabilities:
  ML-driven
  solutions
  learn
  from
  past
  events
  in
  order
  to
  continuously
  improve
  their
  threat
  detection
  capabilities,
  threat
  scoring,
  clustering
  and
  network
  visualisations.
  This
  may
  involve
  training
  the
  algorithms
  themselves
  or
  adjusting
  how
  information
  is
  presented
  based
  on
  feedback
  from
  analysts.
• 
  Enhance
  Incident
  Response:By
  learning
  from
  an
  analyst’s
  past
  incident
  response
  activities,
  ML
  can
  automate
  certain
  aspects
  of
  the
  incident
  response
  process,
  minimizing
  the
  time
  and
  resources
  required
  to
  address
  a
  security
  breach.
  This
  can
  involve
  using
  algorithms
  to
  analyze
  text
  and
  evidence,
  identifying
  root
  causes
  and
  attack
  patterns.

Example
of
an
ML-driven
Network
Security
Solution

When
it
comes
to
ML-driven
Network
Detection
&
Response
(NDR)
solutions
that
incorporate
the
outlined
benefits,

ExeonTrace
stands
out
as
a
leading
network
security
solution
in
Europe.
Based
on
award-winning
ML
algorithms,
which
incorporate
a
decade
of
academic
research,
ExeonTrace
provides
organizations
with
advanced
ML
threat
detection
capabilities,
complete
network
visibility,
flexible
log
source
integration
and
big
data
analytics.
In
addition,
the
algorithms
rely
on

metadata
analysis
instead
of
actual
payloads
which
makes
them
unaffected
by
encryption,
completely
hardware-free
and
compatible
with
most
cybersecurity
infrastructures.
As
a
result,
ExeonTrace
is
able
to
process
raw
log
data
into
powerful
graph
databases,
which
are
then
analyzed
by
supervised
and
unsupervised
ML-models.
Through
correlation
and
event
fusion,
the
algorithms
can
accurately
pinpoint
high-fidelity
anomalies
and
subtle
cues
of
malicious
behavior,
even
when
dealing
with
novel
or
emerging
cyber
threats
that
may
lack
established
signatures
or
known
malicious
indicators.

Security Analytics Pipeline: Detection of network anomalies through ML

Conclusion

As
the
threat
of
cyber
attacks
becomes
increasingly
complex,
organizations
must
go
beyond
traditional
security
measures
to
protect
their
networks.
As
a
result,
many
companies
are
now
turning
to
Machine
Learning
(ML)
and
predictive
analytics
to
strengthen
their
security
defenses.
In
this
regard,
ML-driven
Network
Detection
&
Response
(NDR)
solutions,
such
as
ExeonTrace,
are
designed
to
help
organizations
stay
ahead
of
the
ever-evolving
threat
landscape.
By
utilizing
advanced
ML
algorithms
that
analyze
network
traffic
and
application
logs,
ExeonTrace
offers
organizations
quick
detection
and
response
to
even
the
most
sophisticated
cyberattacks.

ExeonTrace Platform: Network visibility

Book
a
free
demo
to
discover
how
ExeonTrace
leverages
ML
algorithms
to
make
your
organisation
more
cyber
resilient
–
quickly,
reliable
and
completely
hardware-free.

About Author

AndyC

Andy Curtis is an award-winning security consultant, researcher and public speaker. He has been working in the computer security industry since the early 1990s, having been employed by state and federal government, leading healthcare and banking providers across three continents. He has given talks about computer security for some of the world’s largest companies, worked with law enforcement agencies on investigations into hacking groups, and is a regular voice on TV and radio explaining IT security threats.

See author's posts