machine learning infographic new - amazon s3 · machine learning in our complimentary white paper,...

You want to talk about scale? Let’s talk about scale. This massive feature space is what enables BrightCloud to effectively categorize brand new, zero-day internet objects. Neural Nets Webroot applies extremely large and complex neural nets on the order of 40 million nodes, for its machine learning models They are used to digest and analyze the massive number of characteristics we capture for each object. Neural Nets Processing Power Real World Results 10 MILLION Webroot has the potential to capture up to 10 million input characteristics for each internet object that we classify 40 MILLION We then train the machine by assigning up to 40 million weights to our models MILLIONS Webroot classifies millions of internet objects every day to determine if they are benign or malicious Webroot trains and publishes 1,000 models a day 1,000 The Webroot cloud-based platform has the potential to capture millions of characteristics for each object being classified. These characteristics create a “feature space” for internet objects. The location of an object in this“feature space” defines the object. At present, the training of a Webroot model utilizes approximately 10 million data points to determine 40 to 50 million model parameters. To accomplish this, we leverage Amazon Web Services and the San Diego Supercomputer Center at the University of California, San Diego in La Jolla, CA, typically leveraging up to one terabyte of RAM and 40-50 nodes. This increases to 91% after 12 hours MRG research indicates that Webroot correctly identified 100% of malicious files within 24 hours Webroot witnessed 293 million new unique files in 2016 alone 94% of malicious files first seen in 2016 were only seen on one PC in Webroot’s user base, demonstrating the continuing growth of polymorphic malware. In 2016, Webroot classified an average of 736,000 new files as malicious per month. Webroot was able to classify 59% of new malicious files within the first hour Read more about how Webroot is identifying zero-day threats using machine learning in our complimentary white paper, “Automating Threat Detection with Advanced Machine Learning at Scale: The Webroot Approach” 59% 91% 100% Webroot can catch the most elusive, never-before-seen threats. How? It's simple: The scale, speed and volume of Webroot's machine learning is unmatched, which leads to the effective characterization of internet threats in near real time.

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Page 1: Machine Learning Infographic New - Amazon S3 · machine learning in our complimentary white paper, “Automating Threat Detection with Advanced Machine Learning at Scale: The Webroot

You want to talk about scale? Let’s talk about scale.

This massive feature space is what enables BrightCloud to effectively categorize brand new, zero-day internet objects.

Neural Nets Webroot applies extremely large and complex neural nets on the order of 40 million nodes, for its machine learning models

They are used to digest and analyze the massive number of characteristics we capture for each object.

Neural Nets

Processing Power

Real World Results

10 MILLION

Webroot has the potential to capture up to 10 million input characteristics for each internet object that we classify

40 MILLION

We then train the machine by assigning up to 40 million weights to our models

MILLIONSWebroot classifies millions of internet objects every day to determine if they are benign or malicious

Webroot trains and publishes 1,000 models a day1,000

The Webroot cloud-based platform has the potential to capture millions of characteristics for each object being classified. These characteristics create a “feature space” for internet objects. The location of an object in this“feature space” defines the object.

At present, the training of a Webroot model utilizes approximately 10 million data points to determine 40 to 50 million model parameters.

To accomplish this, we leverage Amazon Web Services and the San Diego Supercomputer Center at the University of California, San Diego in La Jolla, CA, typically leveraging up to one terabyte of RAM and 40-50 nodes.

This increases to 91% after 12 hoursMRG research indicates that Webroot correctly identified 100% of malicious files within 24 hours

Webroot witnessed 293 million new unique files in 2016 alone

94% of malicious files first seen in 2016 were only seen on one PC in Webroot’s user base, demonstrating the continuing growth of polymorphic malware.

In 2016, Webroot classified an average of 736,000 new files as malicious per month.

Webroot was able to classify 59% of new malicious files within the first hour

Read more about how Webroot is identifying zero-day threats using machine learning in our complimentary white paper, “Automating Threat Detection with Advanced Machine Learning at Scale: The Webroot Approach”

59% 91% 100%

Webroot can catch the most elusive, never-before-seen threats.

How? It's simple: The scale, speed and volume of Webroot's machine learning is unmatched, which leads to the effective characterization of internet threats in near real time.

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