AI and machine learning

Microsoft Secure

Data science for cybersecurity: A probabilistic time series model for detecting RDP inbound brute force attacks

Microsoft Defender ATP data scientists and threat hunters collaborate to use a data science-driven approach to detecting RDP brute force attacks to protect customers against real-world threats.
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Microsoft Secure

Finding a common language to describe AI security threats

As artificial intelligence (AI) and machine learning systems become increasingly important to our lives, it’s critical that when they fail we understand how and why. Many research papers have been dedicated to this topic, but inconsistent vocabulary has limited their usefulness. In collaboration with Harvard University’s Berkman Klein Center, Microsoft published a series of materials that…
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Microsoft Secure

In hot pursuit of elusive threats: AI-driven behavior-based blocking stops attacks in their tracks

Two new machine learning protection features within the behavioral blocking and containment capabilities in Microsoft Defender ATP specialize in detecting threats by analyzing behavior, adding new layers of protection after an attack has started running.
The post In hot pursuit of elusive threats: AI-driven behavior-based blocking stops attacks in their tracks appeared first on Microsoft Security. READ MORE HERE…

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Microsoft Secure

Azure Sentinel—the cloud-native SIEM that empowers defenders is now generally available

Our goal has remained the same since we first launched Microsoft Azure Sentinel in February: empower security operations teams to help enhance the security posture of our customers. Today, we take the next step in that journey by making Azure Sentinel generally available.
The post Azure Sentinel—the cloud-native SIEM that empowers defenders is now generally available appeared first on Microsoft Security. READ MORE HERE…

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