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Microsoft Security

Behavioral blocking and containment: Transforming optics into protection

March 18, 2020 admin behavioral blocking and containment, cybersecurity, endpoint detection and response (edr), endpoint protection platform (EPP), Endpoint security, juicy potato, Microsoft Defender Advanced Threat Protection, Microsoft Defender ATP, Microsoft security intelligence, Microsoft Threat Protection, next generation protection, qrat, Security Intelligence, Threat protection

Credit to Author: Eric Avena| Date: Mon, 09 Mar 2020 16:30:20 +0000

Behavioral blocking and containment capabilities leverage multiple Microsoft Defender ATP components and features to immediately stop attacks before they can progress. We have expanded these capabilities to get even broader visibility into malicious behavior by using a rapid protection loop engine that leverages endpoint and detection response (EDR) sensors.

The post Behavioral blocking and containment: Transforming optics into protection appeared first on Microsoft Security .

Microsoft Security

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

July 31, 2019 admin assertion engine, cybersecurity, hardware-based isolation, kernel-mode attacks, Microsoft Defender Advanced Threat Protection, Microsoft security intelligence, next generation protection, Privilege Escalation, runtime attestation, Security Intelligence, Threat protection, token swap, token theft, Windows Defender Antivirus, Windows Security

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.

The post How Windows Defender Antivirus integrates hardware-based system integrity for informed, extensive endpoint protection appeared first on Microsoft Security .

Microsoft Security

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

July 25, 2019 admin AI and machine learning, Antivirus, automation, cybersecurity, detection evasion, Endpoint security, LockerGoga, machine learning, Microsoft Defender Advanced Threat Protection, Microsoft Defender ATP, Microsoft security intelligence, monotonic models, next generation protection, Windows Defender Antivirus

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.

The post New machine learning model sifts through the good to unearth the bad in evasive malware appeared first on Microsoft Security .