At its simplest level, machine
learning is defined as “the ability (for computers) to learn without being
explicitly programmed.” Using mathematical techniques across huge datasets,
machine learning algorithms essentially build models of behaviors and use those
models as a basis for making future predictions based on new input data. It is
Netflix offering up new TV series based on your previous viewing history, and
the self-driving car learning about road conditions from a near-miss with a
pedestrian.
So, what are the machine learning applications in information
security?
In principle, machine learning can help businesses better analyze
threats and respond to attacks and security incidents. It could also help to
automate more menial tasks previously carried out by stretched and sometimes
under-skilled security teams.
Subsequently, machine learning in security is a fast-growing
trend. Analysts at ABI Research estimate that machine learning in cybersecurity
will boost spending in big data, artificial
intelligence (AI) and analytics to $96 billion by 2021, while some of the
world’s technology giants are already taking a stand to better protect their
own customers.
Google is using machine learning to analyze threats against mobile
endpoints running on Android — as well as identifying and removing malware from
infected handsets, while cloud infrastructure giant Amazon has acquired
start-up harvest.AI and launched Macie, a service that uses machine learning to
uncover, sort and classify data stored on the S3 cloud storage service.
Simultaneously, enterprise security vendors have been working
towards incorporating machine learning into new and old products, largely in a
bid to improve malware detection. “Most of the major companies in security have
moved from a purely “signature-based” system of a few years ago used to detect
malware, to a machine learning system that tries to interpret actions and
events and learns from a variety of sources what is safe and what is not,” says
Jack Gold, president and principal analyst at J. Gold Associates. “It’s still a
nascent field, but it is clearly the way to go in the future. Artificial
intelligence and machine learning will dramatically change how security is
done.”
Though this transformation won’t happen overnight, machine
learning is already emerging in certain areas. “AI — as a wider definition which
includes machine learning and deep learning — is in its early phase of
empowering cyber defense where we mostly see the obvious use cases of
identifying patterns of malicious activities whether on the endpoint, network,
fraud or at the SIEM,” says Dudu Mimran, CTO of Deutsche Telekom Innovation
Laboratories (and also of the Cyber Security Research Center at Israel’s
Ben-Gurion University). “I believe we will see more and more use cases, in the
areas of defense against service disruptions, attribution and user behavior
modification.”
Here, we break down the top use cases of machine learning in
security.
1. Using machine learning to detect malicious activity and stop
attacks
Machine learning algorithms will help businesses to detect
malicious activity faster and stop attacks before they get started. David
Palmer should know. As director of technology at UK-based start-up Darktrace –
a firm that has seen a lot of success around its machine learning-based
Enterprise Immune Solution since the firm’s foundation in 2013 – he has seen
the impact on such technologies.
Palmer says that Darktrace recently helped one casino in North
America when its algorithms detected a data exfiltration attack that used a
“connected fish tank as the entryway into the network.” The firm also claims to
have prevented a similar attack during the Wannacry ransomware crisis
last summer.
“Our algorithms
spotted the attack within seconds in one NHS agency’s network, and the threat
was mitigated without causing any damage to that organization,” he said of the
ransomware, which infected more than 200,000 victims across 150 countries.
“In fact, none of our customers were harmed by the WannaCry attack
including those that hadn’t patched against it.”
2.
Using machine learning to analyze mobile endpoints
Machine
learning is already going mainstream on mobile devices, but thus far most of
this activity has been for driving improved voice-based experiences on the
likes of Google Now, Apple’s Siri, and Amazon’s Alexa. Yet there is an
application for security too. As mentioned above, Google is using machine
learning to analyze threats against mobile endpoints, while enterprise is
seeing an opportunity to protect the growing number of bring-your-own and
choose-your-own mobile devices.
3.
Using machine learning to enhance human analysis
At the
heart of machine learning in security, there is the belief that it helps human
analysts with all aspects of the job, including detecting malicious attacks,
analyzing the network, endpoint protection and vulnerability assessment.
There’s arguably most excitement though around threat intelligence. For
example, in 2016, MIT’s Computer Science and Artificial Intelligence Lab
(CSAIL) developed a system called AI2, an adaptive machine learning security
platform that helped analysts find those ‘needles in the haystack’. Reviewing
millions of logins each day, the system was able to filter data and pass it
onto the human analyst, reducing alerts down to around 100 per day
4.
Using machine learning to automate repetitive security tasks
The
real benefit of machine learning is that it could automate repetitive tasks,
enabling staff to focus on more important work. Palmer says that machine
learning ultimately should aim to “remove the need for humans to do repetitive,
low-value decision-making activity, like triaging threat intelligence. “Let the
machines handle the repetitive work and the tactical firefighting like
interrupting ransomware so that the humans can free up time to deal with
strategic issues — like modernizing off Windows XP — instead.” Booz Allen
Hamilton has gone down this route, reportedly using AI tools to more efficiently
allocate human security resources, triaging threats so workers could focus on
the most critical attacks.
5.
Using machine learning to close zero-day vulnerabilities
Some
believe that machine learning could help close vulnerabilities, particularly zero-day
threats and others that target largely unsecured IoT devices. There has been
proactive work in this area: A team at Arizona State University used machine
learning to monitor traffic on the dark web to identify data relating to
zero-day exploits, according to Forbes. Armed with this type of insight,
organizations could potentially close vulnerabilities and stop patch exploits
before they result in a data breach.
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