Month: July 2019

ScadaICSSchneider

New ways automakers are leveraging power monitoring to drive down manufacturing plant costs

Credit to Author: Tony Hunt| Date: Thu, 25 Jul 2019 14:00:06 +0000

In automotive manufacturing, electricity makes up the majority of total energy cost compared to natural gas, water and other purchased fuels. Electricity is crucial for important processes such as painting,… Read more »

The post New ways automakers are leveraging power monitoring to drive down manufacturing plant costs appeared first on Schneider Electric Blog.

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IndependentKrebs

The Unsexy Threat to Election Security

Credit to Author: BrianKrebs| Date: Thu, 25 Jul 2019 17:01:41 +0000

Much has been written about the need to further secure our elections, from ensuring the integrity of voting machines to combating fake news. But according to a report quietly issued by a California grand jury this week, more attention needs to be paid to securing social media and email accounts used by election officials at the state and local level.

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MalwareBytesSecurity

Changing California’s privacy law: A snapshot at the support and opposition

Credit to Author: David Ruiz| Date: Thu, 25 Jul 2019 15:59:59 +0000

Before the California Senate returns from its summer recess, we look at the authors, supporters, opponents, and donors involved in an extended fight to change California’s privacy law, the California Consumer Privacy Act.

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The post Changing California’s privacy law: A snapshot at the support and opposition appeared first on Malwarebytes Labs.

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MicrosoftSecurity

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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