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

July 25, 2019 0 Comments 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.

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