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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New machine learning model sifts through the good to unearth the bad in evasive malware

Most machine learning models are trained on a mix of malicious and clean features. Attackers routinely try to throw these models off balance by stuffing clean features into malware. Monotonic models are resistant against adversarial attacks because they are trained differently: they only look for malicious features. The magic is this: Attackers can’t evade a monotonic model by adding clean features. To evade a monotonic model, an attacker would have to remove malicious features.
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Inside out: Get to know the advanced technologies at the core of Microsoft Defender ATP next generation protection

While Windows Defender Antivirus makes catching 5 billion threats on devices every month look easy, multiple advanced detection and prevention technologies work under the hood to make this happen. Multiple next-generation protection engines to detect and stop a wide range of threats and attacker techniques at multiple points, providing industry-best detection and blocking capabilities.
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How Machine Learning can Expose and Illustrate Network Threats

Although machine learning algorithms have been around for years, additional use cases are being discovered and applied all the time, particularly when it comes to network and data security. As years have passed, the skills and sophisticated approaches being utilized by hackers have risen in severity and frequency, and white hats as well as enterprise…
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Tech Support Scams: What are They and How do I Stay Safe?

If you read this blog regularly you’re no doubt aware that cyber-criminals are a determined bunch, with a large range of tools and tactics at their disposal to rob you of your identity and hard-earned cash. Tech support scams (TSS) are an increasingly popular way for them to do just this. In 2017, Microsoft Customer…
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