Dark Reading is part of the Informa Tech Division of Informa PLC

This site is operated by a business or businesses owned by Informa PLC and all copyright resides with them.Informa PLC's registered office is 5 Howick Place, London SW1P 1WG. Registered in England and Wales. Number 8860726.

Operational Security //

Risk Management

08:05 AM
Simon Marshall
Simon Marshall
Simon Marshall

Vulnerability Remediation: Best Practice or Best Guess?

A new study from Kenna Security and the Cyentia Institute finds that even the most well-thought-out vulnerability remediation strategy is no better than a good guess. However, machine learning could lead to better results.

If you don't want to hear (structured) criticism of your vulnerability remediation strategy, close your ears now. Because chances are, enterprise security teams are doing no better statistically than random chance.

That's the startling finding of a new study from the Cyentia Institute and Kenna Security, a San Francisco-based predictive cyber risk firm. The two analyzed five years' worth of historical vulnerability data from 15 sources, and found that current remediation approaches to prioritizing and resolving vulnerabilities are about as effective -- even sometimes less effective -- than tackling issues in a random order.

It's not that remediation techs are doing a bad job once they've identified an issue, it's that deciding what order to tackle them is leaving enterprises open to damage from unpatched exploits further down the checklist.

(Source: Flickr)
(Source: Flickr)

"Effective remediation depends on quickly determining which vulnerabilities warrant action and which of those have highest priority, but prioritization remains one of the biggest challenges in vulnerability management," Kenna CEO Karim Toubba said.

"Businesses can no longer afford to react to cyber threats, as the research shows that most common remediation strategies are about as effective as rolling dice," Toubba added.

Predictive, not reactive
The concept of handling vulnerabilities remains unchanged: identify and remediate as rapidly as possible against an increasing number and velocity of threats. What's new is a change in posture that seeks to become predictive, rather than reactive. In past years, IT security has used analog tuning to try and identify and prioritize remediation, but this approach is now outmoded.

"Fast forward to 2018, and risk-based intelligent vulnerability management platforms now consume terabytes of configuration data, asset data, vulnerability data and threat intelligence to create a fine-grained analysis of which systems really need immediate patching against current threats," said Jon Oltsik, a senior principal analyst with the Enterprise Strategy Group.

Now there's a drive to move beyond real-time assessment of data into forecasting risks before an attack is possible. But of course, that's not easy.

Enterprises have an average of between 18 million and 24 million vulnerabilities across 60,000 assets, according to Cyentia. Every day of the year, they're faced with handling about 40 new vulnerabilities, and last year saw this number peak -- double that of 2016 -- and tracking to further grow this year.

The challenge is increased because most published vulnerabilities aren't used by attackers -- about 75% of known vulnerabilities never have an exploit developed for them, and then only 2% are ever used in an attack. As enterprises try to sort the wheat from the chaff, they're pressured because about half of new vulnerabilities are published within two weeks, effectively giving companies only ten working days to find them.

Now entering its fifth year, the 2020 Vision Executive Summit is an exclusive meeting of global CSP executives focused on navigating the disruptive forces at work in telecom today. Join us in Lisbon on December 4-6 to meet with fellow experts as we define the future of next-gen communications and how to make it profitable.

Essentially, this requires as wide a data input funnel as possible, filtered by a risk scoring model that provides results that increase the probability that a vulnerability will be exploited.

And the key to all of this seems to be machine learning.

Which vulnerabilities are hot?
"We use Machine Learning to comb through all the vulnerabilities previously released to figure out exactly what about a vulnerability makes it likely that an attacker would write an exploit for it," Michael Roytman, Kenna's chief data scientist, told SecurityNow. "We consider around 100,000 variables in doing so, and once we have a good idea of what those factors are, we make a best guess for every new vulnerability as it comes out."

He takes a leaf out of Charles Darwin's On the Origin of Species, constantly evolving the platform to adapt to new vulnerabilities which continuously pop up in the order of several million every 24 hours. Rather than using all of these inputs as training data for the platform -- meaning a risk the platform would never properly mature -- Roytman employs the concept of the survival of the fittest.

The performance of the current model is measured against a potential new one, using recent historical data. Whichever performs the best is taken forward for the next 24 hours. The "genetic origin" of today's model was created from selecting the best of 400 such initial models by giving each thousands of passes over an initial data set.

"We made every mistake imaginable, but as long as we understood how the algorithms worked, and as long as we kept a cool head and measured performance using sound statistical testing, we kept making steps in the right direction," he explained.

Related posts:

— Simon Marshall, Technology Journalist, special to Security Now

Comment  | 
Print  | 
More Insights
Threaded  |  Newest First  |  Oldest First
7 Old IT Things Every New InfoSec Pro Should Know
Joan Goodchild, Staff Editor,  4/20/2021
Cloud-Native Businesses Struggle With Security
Robert Lemos, Contributing Writer,  5/6/2021
Defending Against Web Scraping Attacks
Rob Simon, Principal Security Consultant at TrustedSec,  5/7/2021
Register for Dark Reading Newsletters
White Papers
Current Issue
2021 Top Enterprise IT Trends
We've identified the key trends that are poised to impact the IT landscape in 2021. Find out why they're important and how they will affect you today!
Flash Poll
Twitter Feed
Dark Reading - Bug Report
Bug Report
Enterprise Vulnerabilities
From DHS/US-CERT's National Vulnerability Database
PUBLISHED: 2021-05-11
In the Linux kernel 5.11 through 5.12.2, isotp_setsockopt in net/can/isotp.c allows privilege escalation to root by leveraging a use-after-free. (This does not affect earlier versions that lack CAN ISOTP SF_BROADCAST support.)
PUBLISHED: 2021-05-11
A flaw was found in the hivex library in versions before 1.3.20. It is caused due to a lack of bounds check within the hivex_open function. An attacker could input a specially crafted Windows Registry (hive) file which would cause hivex to read memory beyond its normal bounds or cause the program to...
PUBLISHED: 2021-05-11
A flaw was found in ImageMagick in versions before 7.0.11 and before 6.9.12, where a division by zero in WaveImage() of MagickCore/visual-effects.c may trigger undefined behavior via a crafted image file submitted to an application using ImageMagick. The highest threat from this vulnerability is to ...
PUBLISHED: 2021-05-11
A flaw was found in ImageMagick in versions before 7.0.11, where a division by zero ConvertXYZToJzazbz() of MagickCore/colorspace.c may trigger undefined behavior via a crafted image file that is submitted by an attacker and processed by an application using ImageMagick. The highest threat from this...
PUBLISHED: 2021-05-11
A flaw was found in ImageMagick in versions before 7.0.11, where a division by zero in sRGBTransformImage() in the MagickCore/colorspace.c may trigger undefined behavior via a crafted image file that is submitted by an attacker processed by an application using ImageMagick. The highest threat from t...