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.
The post New research, tooling, and partnerships for more secure AI and machine learning appeared first on Microsoft Security Blog. READ MORE HERE…
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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Counterfit is a command-line tool for security professionals to red team AI systems and systematically scans for vulnerabilities as part of AI risk assessment.
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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.
The post Gamifying machine learning for stronger security and AI models appeared first on Microsoft Security. READ MORE HERE…
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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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.
The post Stopping Active Directory attacks and other post-exploitation behavior with AMSI and machine learning appeared first on Microsoft Security. READ MORE HERE…
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.
The post Seeing the big picture: Deep learning-based fusion of behavior signals for threat detection appeared first on Microsoft Security. READ MORE HERE…
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.
The post Microsoft researchers work with Intel Labs to explore new deep learning approaches for malware classification appeared first on Microsoft Security. READ MORE HERE…
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.
The post Deep learning rises: New methods for detecting malicious PowerShell appeared first on Microsoft Security. READ MORE HERE…
Machine learning and natural language processing can automate the processing of unstructured text for insightful, actionable threat intelligence.
The post From unstructured data to actionable intelligence: Using machine learning for threat intelligence appeared first on Microsoft Security. READ MORE HERE…