The document discusses the challenges and advancements in using big data analytics for cybersecurity, highlighting that while perimeter defenses are crucial, they cannot stop all threats. As sophisticated attacks evolve, companies increasingly rely on forensic tools and security analytics to detect and respond to these threats through detailed analysis of data. However, the use of big data in security remains a developing field, with many organizations still grappling with the vast amounts of data and the necessary technologies for effective analysis.

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Could big data be the key to identifying
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By Robert Lemos
PLUS Secure the perimeter, but analyze the data >>

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October 2013 Issue 13
CONTENTS
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COVER STORY
Big Data Detectives
Companies are using data analytics to improve security
but they’re challenged by immature technology and a
scarcity of expertise. p5
DARK DOMINION
Bolster Perimeter Protection
Security analytics is the next generation of defense. p4
CONTACTS
Editorial and Business Contacts p14
More From Dark Reading
Detect Business Threats
Dark Reading’s Security Monitoring Tech Center is a
single community dedicated to the tools and techniques used to analyze security activity and detect
potential threats to the business.
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How Do You Handle Big Data?
The Big Data Conference provides three days of
comprehensive content for
business and technology pros
seeking to capitalize on the
boom in data volume. In Chicago, Oct. 21-23.
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Cloud Connect’s summits, panels and boot camps
draw fellow IT pros wrestling with cloud challenges.
In Chicago, Oct. 21-23.
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The Changing Face Of APTs
Advanced persistent threats are
evolving in motivation, malice
and sophistication. Are you
ready to stop the madness?
darkreading.com/issue/aptaugust2013
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Secure The Perimeter, Then Add Big Data
A few years ago, my pest control company
started a service called “perimeter defense.”
Instead of bringing a technician inside and
spraying each room, my exterminators said
they would treat the house from the outside,
effectively creating a safe “wall” that vermin
couldn’t penetrate.
Sorry, I had to stop typing for a moment
and wipe another bug off my screen. I wonder why the spider in the corner doesn’t ever
catch those things?
Like most IT security professionals, I no longer believe that perimeter defense will stop
all the intruders. Yes, it helps a lot. But I’ve accepted the fact that no matter how good my
outside defense is, the most sophisticated
pests will find their way in. As a result, I keep
flyswatters and bug spray handy. I have my
own strategies for finding and killing what
evades my external defenses.
In the enterprise, this process for detecting and eliminating the attacks that bypass
the perimeter — sometimes called incident
response — is becoming an increasingly im-
portant part of enterprise defense. Assume
you will be hacked, the logic goes, and be
ready with your backup plan. It’s the virtual
equivalent of my flyswatter and bug spray.
Unlike pesky bugs, though, a sophisticated
enterprise compromise can result in very
big problems — loss of customer data, loss
of service and even loss of business. One
fierce attack could cost your company millions of dollars and, if it’s properly hidden,
could suck the data out of your systems for
months, or even years.
To help reduce this risk, many big companies are relying on forensic tools and experts to detect these sophisticated threats
and root them out before they can damage
the business. “Incident response” has become “data forensics,” in which the goal is
to identify evasive attacks through detailed
analysis of digital evidence found in system
log files and security event management
systems that track security-related data and
flag anomalous activity.
The problem is that there’s so much secu-
TIM WILSON
@darkreadingtim
rity-related information that finding and correlating the few bits of data needed to identify an attack can be nearly impossible for a
single human. To quote one expert, “it’s like
trying to find a needle in a stack of needles.”
The forensics problem has given rise to a
new class of tools and best practices being
called “security analytics.” The detailed study
of security data increasingly embraces big
data analysis tools and techniques used by
other parts of the business, and it’s becoming the next generation of defense. This special digital issue of Dark Reading offers a look
at this new trend.
Like me and my exterminators, enterprises
haven’t given up on perimeter defense, but
they acknowledge it can’t stop everything.
For today’s business, security doesn’t just
mean developing an effective screen against
attack, it means an effective strategy for wiping out the bugs that get through the holes.
Tim Wilson is editor of DarkReading.com. Write to him at
timothy.wilson@ubm.com.
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Big Data
Detectives
Could big data be the key
to identifying sophisticated
threats? Security experts are
on the case.
darkreading.com
@roblemos
or Vigilant, it started in 2009. And as
with most companies, it started small.
The security services startup, now part of
audit and consulting firm Deloitte, wanted
a way to bring information about external threats to clients that were using SIEM
(security information and event management) systems to monitor their own environments. The Vigilant team knew that
the combination of external threat data
with internal security event data could be
a powerful way to improve enterprise defenses, but crunching all that data would
be a monumental task.
Vigilant began combining threat intelligence feeds, filtering the data to pull out
the most important information for each
client, and then transmitting the data to
their clients’ SIEM systems. The company
started with two threat lists: domains servOctober 2013 5

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Our Threat Intelligence Tech
Center provides in-depth
information on collecting and
analyzing data on emerging
cybersecurity threats.
Click Here
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ing malware, and domains compromised by
the Trojans SpyEye and Zeus. To reduce false
alarms and aid in analysis, the company began adding more data feeds.
Vigilant’s analysts quickly became addicted
to the analysis. Each new source of data gave
them the ability to tease out additional information on threats. By 2011, the company was
processing about 50 to 100 GBs per day. But
the company’s systems couldn’t keep up with
the flow of data, and it started missing performance deadlines, says Joe Magee, co-founder
and former CTO of Vigilant, who is now a director at Deloitte.
“We were not able to catch up,” Magee says.
“We were not able to process the information
and push it out fast enough, and that’s when
it became a big data issue for us. We needed
to be able to rip through this data in Googlelike fashion.”
The volume of data and rate of change
caused the problem, because most of the
data came in the form of feeds updated daily
with gigabytes of data. It overwhelmed the
company’s initial database built on top of
Postgres. In 2011, Vigilant moved to Hadoop
and became one of many companies — both
vendors and enterprises — that are advocating the use of big data analytics to improve
the response to security threats.
Big Data Still Just A Promise
For security teams, the use of analytics on
massive quantities of security data — from device and application logs to collections of captured network packets and operational business data — promises better visibility into the
security threats that elude current defenses.
Big data analytics can be more complex than
the log collection and analysis conducted by
most SIEM systems, so automating the number crunching is often needed to let security
pros more easily use statistical correlations
to discover trends and anomalies. Tracking
days or weeks of business activity allows the
system to find outliers — a user who accesses
far more data on a daily basis than the average
employee, or a system that has a sudden spike
“A company can have so much
data and try to do so much with
it, and there are no SIEM solutions
that can handle it.”
—Lucas Zaichkowsky, AccessData
in resource consumption. Analysts then can
dig deeper into the large data sets of security
information for any flagged events.
“Big data is not just about gaining insights,
it’s about helping remediate issues faster,” says
Jason Corbin, director of security intelligence
strategy for IBM Security Systems. “The big
problem is that [security teams] are overwhelmed with information they have. All
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that information goes to some guy who has
to sift through tons of incidents or vulnerability reports and decide what they need to
patch or virtually patch or fix. Security teams
fall behind, and that’s how companies suffer
breaches based on known but unpatched
vulnerabilities.”
But for many companies, the promise of big
data in security is just that — a promise. While
security teams hope to gain more awareness
of what is going on in their networks by collecting and analyzing more of their data, the
technology is still in its adolescence. “Hadoop
has been around for a while, but it is still figuring out what it is and what is wants to be,”
says Adrian Lane, CTO for security consultancy Securosis.
Still, the potential is huge, Lane adds. Companies that kick off a big data project for security can collect an immense volume of data
and have a security analyst poke through
the information, ask queries of the data and
make important discoveries.
How Big Is Big?
Big data itself isn’t a technology or a method
of analysis. It’s a concept that involves collecting, managing and making sense of more and
new data sources. It’s about analyzing the
darkreading.com
“dark data” (data that is collected but rarely
used) created by business devices and systems. For companies, that means collecting
orders of magnitude more data.
Business projects aimed at using big data
to support security typically follow two
paths. In the first, security teams gain access to a company’s operational data and
Which types of data should be analyzed?
Opinions vary. Many SIEM vendors argue that
the proliferation of device log data creates a
big data problem. Other companies, such as
RSA, use a more strict definition. For them, big
data means monitoring all of the information
that crosses the enterprise network — perhaps an unsurprising opinion for a company
When Will You Use Big Data Analytics For Cyber Defense?
23%
No plans
Using now
39%
17%
12%
Within 9 months
Within 3 months
9%
Within 6 months
Data: Ponemon Institute’s “Big Data Analytics In Cyber Defense” report, surveying 706 IT security practitioners, February 2013
run an analysis against that data to highlight
events that may indicate a security threat.
Alternatively, the team can store data from
security devices and other related systems
and analyze the secu­ ity-specific data for
r
correlations that flag a potential attack.
owned by storage system maker EMC.
“People think that any time you collect security information, that is big data,” says Eddie
Schwartz, chief information security officer
for RSA. “No, it ‘s a new way of looking at information. Big data means that we’re looking
October 2013 7

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at transactional information, we’re looking at the full
context and content of network traffic.”
For large companies, the creation of a big data
store of security information may result as the byproduct of normal business, or it may be a goal.
But some big data advocates urge companies to
search for more data sources under a “more is better”
mantra. “One of the tenets of big data is that if I have
a larger data set, I may see correlations that I might
not have seen before,” says Samuel Harris, director of
enterprise risk management for Teradata.
Yet deriving security intelligence from a large collection of business data requires hard work. Many
enterprises have tried to merge additional analytics
capabilities into SIEM systems, but that has caused
more headaches than hits, says Lucas Zaichkowsky,
enterprise defense architect for AccessData, a computer forensics and security consulting firm.
“A company can have so much data and try to do
so much with it, and there are no SIEM solutions that
can handle it,” he says. “There are a lot of failed SIEM
projects.”
In fact, growth in the types and volume of data
produced by networking hardware creates the greatest challenge for companies trying to mine network
data. In a study of companies’ attitudes toward using
big data analytics for security, half of 706 respondents had trouble handling the growth of network
data, the Ponemon Institute found. Only 5% of IT

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security respondents believed the growth in
data is an opportunity.
From Big Data To Bad Guys
Nevertheless, there are success stories in
combining big data and security. In 2009,
IT security firm BeyondTrust embarked on
its own big data project. To help security
managers focus on the most pressing vulnerabilities, the company pulled together
frequently updated internal information —
such as the configuration of every machine in
a 100,000-client network — with information
on the latest vulnerabilities, exploit kits and
attacks.
Combining external and internal sets of
data can help companies focus on the few
vulnerabilities that really make a difference
— situations where the company has systems using vulnerable software, and attack-
Protecting Big Data
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U
sing big data could be a boon to security, but enterprises should not forget
about protecting the big data itself.
Because big data can be a complete record of a business’s operations, it’s important to lock it down, says Erik Jarlstrom, VP
of technology solutions at Dataguise. Companies need to secure big data stores early
to avoid delaying the project.
Big data resides in highly distributed clusters of computers, so securing the entire
systems is a challenge, according to Adrian
Lane, CTO of security consultancy Securosis, which recently released a research paper on big data security. Because data is
distributed among the nodes and distributed in multiple copies, it’s difficult to know
where your data resides. In most cases,
there is no generally available encryption
for repositories, and no role-based administrative controls.
Lane advises that companies should use
the Kerberos protocol to authenticate big
data nodes and add file encryption.
“We hear [from security architects] the
most popular security model is to just hide
the entire cluster within their infrastructure,”
Lane writes. “But those repositories are now
Web accessible and very attractive targets.”
—Robert Lemos
ers know about the software flaws and are
actively exploiting them.
“As a customer, it lets me determine what
do I have to do this week and what do I have
to do next week to prevent my company
from being hacked,” says Marc Maiffret, CTO
for BeyondTrust.
Another benefit is that BeyondTrust customers can see where they are vulnerable and
also query the data for more specific information. “We know there is no way that we have
thought of every scenario of how people will
use this data, so we give them the tools and
let them work with the data,” Maiffret says.
Another success story: At the RSA Conference in 2012, Preston Wood, chief security officer at Salt Lake City-based Zions Bancorporation, outlined the bank’s use of analytics to
mine security events. Zions used open source
Hadoop coupled with Google’s MapReduce
and business intelligence tools to correlate
logs from antivirus, databases, firewalls, intrusion-detection systems and financial-industry-specific sources of information, such
as credit applications and data. Using these
methods, Zions has been able to collect and
take action on security information in minutes when it used to take hours, Wood said.
In most cases, big data techniques are used
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to detect compromises that have already
occurred, rather than to prevent them. Because companies are living in a state of compromise, they need to gather as much information as possible on what is happening in
their network, says AccessData’s Zaichkowsky.
“They are accepting that there always will be
a Victim Zero, and instead focus on spotting
the activity.”
Using statistical techniques such as linear
regression, general linear models and machine learning, a security analyst looking
at data can find odd behavior, suspicious
events and other anomalies indicative of
a compromise. While some events — such
as an internal system accessed from Russia
at night — are easy to identify as suspicious, more subtle transactions are missed
because an analyst hasn’t created a rule
to watch for the activity. Mapping access
attempts from each system, for example,
could help security teams pinpoint when a
compromised computer is trying every system on a network.
“If I ask a business person what ‘bad’ looks
like, it’s not an easy question,” says RSA’s
RSA, for example, regularly explores different data sets within its own business to find
new sources of data that can be mined for
security information, Schwartz says.
Is Your Security Data Considered Big Data?
Don’t know
No
2%
11%
No, but it will be in 24 months
14%
44%
No, but it will be in 12 months
Yes
30%
Data: Enterprise Strategy Group’s “The Big Data Security Analytics Era Is Here” report, surveying 257 security professionals, January 2013
Schwartz. “But mathematically, these types of
anomalous transactions are much more obvious when you do statistical analysis.”
Unlike log data, which resembles the summary information on a phone bill, big data
systems collect detailed records, network
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packet data, and other data and metadata
that are important to enterprise security.
For instance, a SIEM system may note that
an EXE file had been downloaded to a desktop, and that the domain it came from was
not on any blacklist. However, using other
data, a different picture can emerge: The program was packed and obfuscated, downloaded from a nonstandard port and sent
from a domain that was only 3 days old.
“Using full-packet capture solutions and
big data analytics, we see everything,” says
John Vecchi, VP of product strategy for65
Solera
60
Networks, a security analytics firm acquired
55
by Blue Coat in May. “We are going to be able
50
to see things and derive information that you
45
would never be able to know from looking at
40
log data.”
35
In addition to allowing security analysts
30
a deeper look, scrutinizing big data gives
25
them more flexibility to find indicators of
20
compromise that may not be immediately
15
evident. One problem with current SIEM
10
systems is that they typically define their
5
searches and analyses performed on the
0
log files, giving the user less flexibility, says
Mark Seward, a senior director at Splunk,
which offers tools for searching and analyzing security data.
“If I let my vendor determine in advance
what data I am going to see, then I am already
essentially compromised,” Seward says.
Waiting For Maturity
While big data analysis holds promise for
security, a number of factors have slowed its
adoption. First, most enterprises don’t have
a line item in the budget for big data security
are also concerned that big data projects
might introduce risk by forcing changes to
the way security systems collect and report
data, he notes.
Another major obstacle is the shortage of
experts with the skills to mine large security
databases for information. In addition to having the abilities of a data scientist, any big
data security project leader also needs secu-
Which Of These Big Data Tools Are In Use At Your Company?
Microsoft Excel
65%
Microsoft SQL PDW
38%
Enterprise search system (any brand)
Oracle Exadata
26%
21%
IBM DB2 Smart Analytics System
16%
Hadoop/MapReduce
14%
Data: InformationWeek 2013 Big Data Survey of 257 business technology professionals at organizations with 50 or more employees, September 2012
projects. “Big data is about solving business
problems, and security is generally, in the beginning, not one of those business problems,”
says Hadi Nahari, chief security architect for
graphics chipmaker Nvidia. Some companies
rity expertise and a focus on usability, says
Teradata’s Harris.
The lack of skilled personnel was the third
most significant barrier to a strong security
posture among enterprises, according to the
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Ponemon Institute’s “Big Data Analytics In Cyber Defense” report, commissioned by Teradata.
The top two barriers, according to the report, were
a lack of effective security technology and an insufficient view into business processes — chosen by 43%
and 42% of respondents, respectively. During its RSA
2012 presentation, Zions Bancorporation introduced
“Big data is about solving
business problems, and security
is generally not one of those
business problems.”
—Hadi Nahari, chief security architect, Nvidia
a team of three employees, including a data scientist,
who created and run the company’s big data project.
But most companies can’t afford to hire so many
people for a big data security project.
Another hurdle to using big data in security is the
relative immaturity of the market. While a number
of security products now tout some tie-in with big
data analytics, they require a great deal of expertise
to use and maintain. “Big data has been around for
a while, but it’s only in its second generation,” Securosis’s Lane says. “It’s not ready for prime time for
many companies.”
The easiest way for a company to get started in
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analyzing its security data is to buy a large server and
start collecting information, says Vigilant’s Magee.
Many Vigilant clients are considering buying a large
32- or 64-CPU server and a fast data store, and some
of them work with business teams that are already
familiar with Hadoop.
“We can leverage Moore’s Law to get out in front
of this problem. We can start putting data into it
and analyze it,” Magee says. “While that may seem
like a very simple or mundane version of SIEM, companies want that ability. They want to ask questions
of their data.”
For small and midsize businesses that don’t have
the resources to start up their own big data project,
the only likely solution is to settle for services that incorporate external feeds and security analytics, says
Jon Oltsik, senior principal analyst with the Enterprise Strategy Group. While big data analytics can be
more effective than SIEM, it isn’t easy to incorporate
into a business.
“Easy is the key word,” Oltsik says. “Big data is
too complex and too costly for most midsize businesses, so the question is who can deliver the intelligence of big data at a lower cost than doing it
themselves. For most smaller companies, that will
be a service provider.”
Robert Lemos is a veteran technology journalist and former research
engineer. Write to us at editors@darkreading.com.
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Director of Marketing, Monique Luttrell
(415) 947-6958, monique.luttrell@ubm.com
Marketing Assistant, Hilary Jansen
(415) 947-6205, hilary.jansen@ubm.com
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