Infosecurity.US

Information Security & Occasional Forays Into Adjacent Realms

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PREDATOR →

October 31, 2016 by Marc Handelman in All is Information, Analytics, Alternate Attack Analysis, Machine Learning, Information Security, Cybernetic Crime

PREDATOR – Proactive Recognition and Elimination of Domain Abuse at Time-Of-Registration, described in the released paper, details the newly developed capability to predict bad-behavior (in this case criminally bad behavior), with the use of analytics at the time of domain registration. Created by Nick Feamster, Shuang Ho, Alex Kantchelian, Brad Miller and Vern Paxson. Outstanding.

"Princeton professor Nick Feamster and University of California Santa Barbara PhD student Shuang Ho worked with Alex Kantchelian (UC Berkley), Google's Brad Miller and Vern Paxson of the International Computer Science Institute to create PREDATOR – Proactive Recognition and Elimination of Domain Abuse at Time-Of-Registration...." "The important numbers are: the researchers say PREDATOR identified 70 per cent of domain registrations that were later abused; and they claim a false positive rate of just 0.35 per cent." - via El Reg's Richard Chirgwin

 

October 31, 2016 /Marc Handelman
All is Information, Analytics, Alternate Attack Analysis, Machine Learning, Information Security, Cybernetic Crime

Ruling

July 21, 2015 by Marc Handelman in All is Information, Hardware Security, Information Security, Robots, Machine Learning
July 21, 2015 /Marc Handelman /Source
All is Information, Hardware Security, Information Security, Robots, Machine Learning

Pinto's "A Deep-Dive on Machine Learning-Based Monitoring" →

April 07, 2015 by Marc Handelman in All is Information, Alternate Attack Analysis, Computer Science, Compute Infrastructure, Information Security, Machine Learning
April 07, 2015 /Marc Handelman
All is Information, Alternate Attack Analysis, Computer Science, Compute Infrastructure, Information Security, Machine Learning
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