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This enables not only detection, but also prevention in all cases (the moment a malicious file is
detected; it is already removed as well).
Our brain works in a similar way as well; it takes us a long time to learn something, but once we
learn it, we can use it very quickly in prediction mode.
Furthermore, when applying deep learning, as opposed to machine learning, there is no need to
conduct manual feature engineering.
Instead, datasets of many millions of malicious and legitimate raw files are fed into the
infrastructure, enabling deep learning to learn on its own the useful high-level, non-linear
features necessary for accurate classification.
Moreover, due to the input-agnostic nature of deep learning, any malicious file type (e.g., EXE,
DLL, PDF, DOC, Android APK, etc.) is detected.
The application of deep learning to cybersecurity results in cutting-edge capabilities of highly
accurate detection and real-time prevention – instinctive protection – on any device, platform,
and operating system.
Deep Instinct, the first company to apply deep learning to cybersecurity, is blazing the trail to a
new level of protection that effectively overcomes the growing sophistication of cyber-attacks.
About the Author
Guy Caspi, CEO, Deep Instinct
Mr. Caspi is a leading mathematician and a data scientist global expert. Mr. Caspi has 15 years
of extensive experience in applying mathematics and machine learning in a technology elite unit
of the Israel Defense Forces (IDF), financial institutions and intelligence organizations around
the world. Mr. Caspi led some of the largest government cyber and Big Data projects in Israel
and other countries. In addition, Mr. Caspi was the president and general manager of a leading
division at Comverse/Verint Group.
Mr. Caspi brings substantial management background in leading innovative technological
challenges from R&D through commercialization. Mr. Caspi holds B.Sc., M.Sc., and MBA
degrees in Mathematics, Machine Learning and Business from leading universities in Israel and
the U.S.
36 Cyber Warnings E-Magazine – April 2016 Edition
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