Combing through the fuzz: Using fuzzy hashing and deep learning to counter malware detection evasion techniques

A new approach for malware classification combines deep learning with fuzzy hashing. Fuzzy hashes identify similarities among malicious files and a deep learning methodology inspired by natural language processing (NLP) better identifies similarities that actually matter, improving detection quality and scale of deployment.
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Seeing the big picture: Deep learning-based fusion of behavior signals for threat detection

Learn how we’re using deep learning to build a powerful, high-precision classification model for long sequences of wide-ranging signals occurring at different times.
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Microsoft researchers work with Intel Labs to explore new deep learning approaches for malware classification

Researchers from Microsoft Threat Protection Intelligence Team and Intel Labs collaborated to study the application of deep transfer learning technique from computer vision to static malware classification.
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Deep learning rises: New methods for detecting malicious PowerShell

We adopted a deep learning technique that was initially developed for natural language processing and applied to expand Microsoft Defender ATP’s coverage of detecting malicious PowerShell scripts, which continue to be a critical attack vector.
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