New research, tooling, and partnerships for more secure AI and machine learning

At Microsoft, we’ve been working on the challenges and opportunities of AI for years. Today we’re sharing some recent developments so that the community can be better informed and better equipped for a new world of AI exploration.
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AI-driven adaptive protection against human-operated ransomware

We developed a cloud-based machine learning system that, when queried by a device, intelligently predicts if it is at risk, then automatically issues a more aggressive blocking verdict to protect the device, thwarting an attacker’s next steps.
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Gamifying machine learning for stronger security and AI models

We are open sourcing the Python source code of a research toolkit we call CyberBattleSim, an experimental research project that investigates how autonomous agents operate in a simulated enterprise environment using high-level abstraction of computer networks and cybersecurity concepts.
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Cyberattacks against machine learning systems are more common than you think

Machine learning (ML) is making incredible transformations in critical areas such as finance, healthcare, and defense, impacting nearly every aspect of our lives. Many businesses, eager to capitalize on advancements in ML, have not scrutinized the security of their ML systems. Today, along with MITRE, and contributions from 11 organizations including IBM, NVIDIA, Bosch, Microsoft…
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Stopping Active Directory attacks and other post-exploitation behavior with AMSI and machine learning

Microsoft Defender ATP leverages AMSI’s visibility into scripts and harnesses the power of machine learning to detect and stop post-exploitation activities that largely rely on scripts.
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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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From unstructured data to actionable intelligence: Using machine learning for threat intelligence

Machine learning and natural language processing can automate the processing of unstructured text for insightful, actionable threat intelligence.
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