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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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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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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