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.

The post From unstructured data to actionable intelligence: Using machine learning for threat intelligence appeared first on Microsoft Security.

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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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The security of machine learning

Credit to Author: Greg Iddon| Date: Wed, 24 Jul 2019 08:42:37 +0000

Artificial intelligence and machine learning are persistently in the headlines with rich debate over its next advances. Will cybercriminals further leverage machine learning to craft attacks? Can defenders build a machine learning model capable of detecting all malware? We believe machine learning is an essential and critical piece of cybersecurity, but it must be only [&#8230;]<img src=”http://feeds.feedburner.com/~r/sophos/dgdY/~4/z8gcd4bDkho” height=”1″ width=”1″ alt=””/>

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Stop facial recognition trials now, warns UK committee

Credit to Author: Danny Bradbury| Date: Mon, 22 Jul 2019 10:26:39 +0000

The UK government should suspend trials of automatic facial recognition systems until it can meet regulators’ concerns about the technology, according to a report released Friday.<img src=”http://feeds.feedburner.com/~r/nakedsecurity/~4/0CSbKeo22L8″ height=”1″ width=”1″ alt=””/>

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Inside out: Get to know the advanced technologies at the core of Microsoft Defender ATP next generation protection

Credit to Author: Eric Avena| Date: Mon, 24 Jun 2019 15:00:55 +0000

While Windows Defender Antivirus makes catching 5 billion threats on devices every month look easy, multiple advanced detection and prevention technologies work under the hood to make this happen. Multiple next-generation protection engines to detect and stop a wide range of threats and attacker techniques at multiple points, providing industry-best detection and blocking capabilities.

The post Inside out: Get to know the advanced technologies at the core of Microsoft Defender ATP next generation protection appeared first on Microsoft Security.

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Labs report: Malicious AI is coming—is the security world ready?

Credit to Author: Malwarebytes Labs| Date: Wed, 19 Jun 2019 15:00:00 +0000

Malwarebytes Labs’ latest report looks at the near-future reality of malicious artificial intelligence (AI), evaluating how the technology is being used today, and how it realistically might be abused in the next few years.

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The post Labs report: Malicious AI is coming—is the security world ready? appeared first on Malwarebytes Labs.

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Machine learning hones weapons of maldoc destruction

Credit to Author: Jason Zhang| Date: Tue, 09 Apr 2019 14:49:19 +0000

By Jason Zhang Criminals continue to leverage the features of Adobe&#8217;s PDF document format to engage in malware and phishing attacks, with no sign of a slowdown. Last year at Black Hat USA, I gave a presentation about PDF-based malware detection using machine learning. We discovered that the best AV engine could only catch fewer [&#8230;]<img src=”http://feeds.feedburner.com/~r/sophos/dgdY/~4/3k1sko1i93Y” height=”1″ width=”1″ alt=””/>

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