Page 75 - Cyber Defense eMagazine - November 2017
P. 75
A DIFFERENT APPROACH THAT INCREASES ACCURACY
To maximize coverage and accuracy, the approach to machine learning we take at Barkly
involves nightly training of our models (which keeps protection up-to-date) as well as the
creation of organization-specific models trained against each company’s unique software profile.
Not only does that allow our models to be more current and responsive to the newest threats, it
also allows them to be less reactive to the legitimate goodware deployed in each environment.
Here’s how it works: Each night, we collect thousands of samples of new malicious software,
and we combine those samples with up-to-the-minute data on the known-good software
organizations are running. We then re-train and redistribute the updated models, which have
been tailored and optimized specifically for each organization. Thanks to that cadence, we’re
able to provide more accurate, maximized protection that maintains its strength over time.
We believe this new, responsive approach represents an exciting step forward in the way
security providers can apply machine learning. But the truth is we still have a very long way to
go before we tap the technology’s full potential. As adoption of machine learning becomes more
prevalent we’re eagerly anticipating more breakthroughs that tip the scales against attackers.
About the Author
Sarosh Petkar is a BS/MS student of the RIT Computing Security
department. He is currently working as a Malware Analyst at
Barkly, the Endpoint Protection Platform that delivers the
strongest protection with the fewest false positives and simplest
management.
His interests include reverse engineering, network security, and
cryptography.
He can be reached online at [email protected].
75 Cyber Defense eMagazine – November 2017 Edition
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