Take 1 Security Podcast: Episode 9

Take 1 Security Podcast: Episode 9



START CONTENT


* Sorry about the audio last week; wireless headsets don’t compare to the Yeti
* The CIA is focusing on cyberespionage in its new management
* Anthem is refusing an audit by the OIG office–an org that audits health care groups that provide services to federal employees


* Nothing says I’m guilty like refusing an audit
* Reminds me of the Russians refusing the crash investigation in Game of Cards

* There’s been a possible credit card breach at the Mandarin Oriental hotel chain


* The incident was reported by Brian Krebs

* Three people were indicted in the Epsilon hack


* Resulted in around 1 billion email addresses being stolen

* Dave Aitel thinks junk hacking is a waste


* Basically hacking your blender or whatever
* In my opinion he’s missing the point that most conferences are like this
* I think there’s a hierarchy of talks


* Create new defense tool based on new defense idea
* Create new defense idea
* Create new attack tool based on new attack idea
* Create new attack idea
* Create new tool for existing attack or defense idea
* Describe existing attack or defense idea


* Microsoft has reported it’s vulnerable to FREAK as well, making it even more serious


* FREAK has proved to be less alarming than previous SSL vulns simply because of the difficulty of attack



END CONTENT

Play Podcast

Notes


* I think I’m going to standardize the intro and outro so that I only end up recording the actual story content each week.
* Any recommendations on what else you’d like to see would be appreciated.

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Episoder(532)

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Unsupervised Learning: No. 112

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12 Feb 201822min

Unsupervised Learning: No. 111

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5 Feb 201814min

Unsupervised Learning: No. 109

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22 Jan 201814min

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8 Jan 201830min

The Biggest Advantage in Machine Learning Will Come From Superior Coverage, Not Superior Analysis

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Many people, in many fields, think Machine Learning won't replace their analysts because their humans are better than an algorithm. But it's not just about side-by-side comparisons. The bigger question is, "what percentage of the data can humans actually look at?", and the answer to that question (a tiny fraction) is the reason ML will be so helpful.Become a Member: https://danielmiessler.com/upgradeSee omnystudio.com/listener for privacy information.

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