Microsoft Defender Advanced Threat Protection

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Bring your own LOLBin: Multi-stage, fileless Nodersok campaign delivers rare Node.js-based malware

Credit to Author: Eric Avena| Date: Thu, 26 Sep 2019 17:34:41 +0000

We’ve discussed the challenges that fileless threats pose in security, and how Microsoft Defender Advanced Threat Protection (Microsoft Defender ATP) employs advanced strategies to defeat these sophisticated threats. Part of the slyness of fileless malware is their use of living-off-the-land techniques, which refer to the abuse of legitimate tools, also called living-off-the-land binaries (LOLBins), that…

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MicrosoftSecurity

From unstructured data to actionable intelligence: Using machine learning for threat intelligence

Credit to Author: Eric Avena| Date: Thu, 08 Aug 2019 16:30:12 +0000

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

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MicrosoftSecurity

A case study in industry collaboration: Poisoned RDP vulnerability disclosure and response

Credit to Author: Eric Avena| Date: Wed, 07 Aug 2019 23:50:25 +0000

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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MicrosoftSecurity

How Windows Defender Antivirus integrates hardware-based system integrity for informed, extensive endpoint protection

Credit to Author: Eric Avena| Date: Wed, 31 Jul 2019 16:30:35 +0000

The deep integration of Windows Defender Antivirus with hardware-based isolation capabilities allows the detection of artifacts of attacks that tamper with kernel-mode agents at the hypervisor level.

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MicrosoftSecurity

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

Credit to Author: Eric Avena| Date: Thu, 25 Jul 2019 16:30:55 +0000

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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