SJ113 Master 2

5 Ways Machine Learning
Can Be Used for Security
During 2016, we had a record high of 1,093 breaches, according to
the Identify Threat Resource Center. Don’t let your business become
a statistic. Machine learning is being applied to five new areas with
hopes that this capability can catch threats before they result in
major security incidents.
Do you enjoy being
the new Grandmaster?
Learning
security moves.
When a hacker’s system which is
capable of machine learning goes up
against an enterprise system, there’s
a battle between security know-how.
And, when data centre security is
also learning defensive moves, there
can be a chaotic and rapid exchange
of strategic moves. That is, until
someone wins.
Briefly, machine learning is a series
of techniques used to “train” a
machine how to solve a problem.
Although, there’s been a lot of “new”
hype around machine learning, it’s
currently being used now in several
industries. Common applications
of machine learning in technology
include voice recognition, fraud
detection, email spam filtering, search
recommendations, and video analysis.
So, chances are you’ve already seen
machine learning in action.
You’re most likely already using
machine learning as part of your
data centre security protection.
Today’s defense solutions are
optimised to prevent targeted attacks,
advanced threats, and malware
from embedding or spreading
within a data centre or network. For
example, at Zones we offer Trend
Micro’s solution that can analyse
unknown threats with machine
learning algorithms to make real-time
decisions on whether network traffic
is malicious or benign.
Zones also provides solutions that
protect enterprises from attack
methods that no one has ever
seen before. We help you apply
a unique combination of behavior
analytics, data science techniques,
and machine learning algorithms.
This baseline, “normal” network,
and endpoint behavior, helps identify
attack indicators and minimise
false positives.
These examples of cross-generational
techniques to defend networks
against known, undisclosed, and
unknown threats, demonstrate the
value of today’s security solutions.
Winning the
defense game.
With machine learning in play,
security becomes the only game
to win. Many advanced threats
are designed specifically to bypass
traditional security defenses. Today’s
sophisticated threats require new
ways to protect everyone and
everything – users, networks, data,
customers, organisations, and the list
goes on.
While no one can say that a single
security solution is foolproof, machine
learning can certainly help you
go from vulnerable to victorious
in one fell swoop. Today’s security
solutions use machine learning –
behavioral analysis, intrusion and
exploit prevention, investigation
and forensics, memory inspection,
prevalence/maturity checks, and
reputational intelligence – to protect
against threats. In some cases,
this occurs even months before a
vulnerability can be exploited by a
threat. Seeing ahead is a significant
strength in the security game.
Instead of tossing security solutions
around without rhyme or reason
like pieces on a chessboard, let
Zones help you figure out your next
move. For starters, it’s easy to see
every piece that’s undefended. Once
you know your undefended pieces,
you can make a jump in security
strengths. Zones Security Assessment
services can help you make that jump.
Whether it’s security assessments,
solutions, or both, we’re ready
to help you be the winner of
the security match, and help you
turn “check” into “checkmate.”
1. Closing IoT vulnerabilities: You
can leverage machine learning
technology to analyse data and
network interactions, determining
safe device behavior. With this
level of insight into general use
patterns, it becomes easier to
identify abnormal activity and
block harmful actions.
2. Preventing false alerts: When
machine learning identifies
something as suspicious, you
can provide feedback by tagging
legitimate threats. The solution
then adjusts its monitoring filters,
boosting its ability to find and
mitigate real breaches before
they cause damage.
3. Stopping exploits: Criminal
hackers often share ideas and
information on forums and
websites that aren’t indexed by
search engines. Machine learning
can be used to monitor traffic
within these areas to identify
data relating to zero-day exploits.
Then, you can use this data to
close vulnerabilities and stop
patch exploits before they result
in a data breach.
4. Predicting threats: Cybersecurity
solutions can easily integrate
Source: Trend Micro blog, 16/3/17. http://blog.
trendmicro.com/5-ways-machine-learning-canbe-used-for-security-today/
with machine learning to detect
network changes over time
and revise behavioral profiles.
This helps predict threats based
on historical data and limit
the number of false positives
that appear.
5. Providing holistic protection:
Upgrade to the latest security
solutions that combine proven
threat detection techniques,
advanced protection methods,
and high-fidelity machine
learning. This combination
ensures that you’ll have a holistic,
effective enterprise defense.
A layered security
infrastructure will protect
your company against
98 percent of the known
threats, but there’s
always the possibility of
compromise through an
unknown vulnerability.”
22 Solutions by Zones Autumn 2017
ukzones.com +44 (0) 207 608 7676 23