Microsoft’s 4 principals for an effective security operations center

Microsoft Chief Cybersecurity Strategist, Jonathan Trull, outlines four principles any organization can use to improve the effectiveness of its SOC.
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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.
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Bring your own LOLBin: Multi-stage, fileless Nodersok campaign delivers rare Node.js-based malware

A new fileless malware campaign we dubbed Nodersok delivers two very unusual LOLBins to turn infected machines into zombie proxies.
The post Bring your own LOLBin: Multi-stage, fileless Nodersok campaign delivers rare Node.js-based malware appeared first on Microsoft Security. READ MORE HERE…

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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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Improve security and simplify operations with Windows Defender Antivirus + Morphisec

Learn how Towne Properties uses Windows Defender Antivirus and Morphisec to protect against advanced memory-based attacks while simplifying operations.
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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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A case study in industry collaboration: Poisoned RDP vulnerability disclosure and response

Through a cross-company, cross-continent collaboration, we discovered a vulnerability, secured customers, and developed fix, all while learning important lessons that we can share with the industry.
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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.
The post New machine learning model sifts through the good to unearth the bad in evasive malware appeared first on Microsoft Security. READ MORE HERE…

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