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.

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

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Malware targeting industrial plants: a threat to physical security

Credit to Author: Pieter Arntz| Date: Wed, 17 Apr 2019 16:04:20 +0000

When malware shuts down the computer systems of an industrial plant, it could threaten the physical security of those working in or living near it. Here’s how to protect your workforce and your business from targeted threats.

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The post Malware targeting industrial plants: a threat to physical security appeared first on Malwarebytes Labs.

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This Week in Security News: Radio Frequency Technology and Telecom Crimes

Credit to Author: Jon Clay (Global Threat Communications)| Date: Fri, 22 Mar 2019 14:00:51 +0000

Welcome to our weekly roundup, where we share what you need to know about the cybersecurity news and events that happened over the past few days. This week, learn how radio frequency technology is putting industrial organizations at risk. Also, understand the threat landscape of telecommunications and how to prepare for future threats. Read on:…

The post This Week in Security News: Radio Frequency Technology and Telecom Crimes appeared first on .

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