A posting from dark reading by Kelly Jackson Higgins titled “Researchers Enlist Machine Learning In Malware Detection ”
No sandbox required for schooling software to speedily spot malware, researchers will demonstrate at Black Hat USA.
In 100 milliseconds or less, researchers are now able to determine whether a piece of code is malware or not — and without the need to isolate it in a sandbox for analysis.
Welcome to the age of machine learning as a tool for more efficiently detecting malware, via so-called “deep learning” techniques. Researchers have built a special machine learning tool module that employs static analysis of a piece of code to quickly spot — and ultimately, stop — malware infections. A pair of researchers plans to demonstrate live at Black Hat USA next month just how this approach can spot malware from live malware feeds.
Matt Wolff, chief data scientist at Cylance, says his team is applying deep learning–a more granular subset of machine learning–to malware detection by training the software via legitimate files and malicious ones, and teaching the application/algorithm which is which. The application then can take files it’s never seen before and spot malware, he says.
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