Results


How to use machine learning to identify "good" customers vs "bad" customers - BDO Canada - IT Solutions

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Good profitable customers rarely become unprofitable. This method has become simplistic, as customers rarely consume overhead costs equally. ABC bases customer profitability analysis on the principle customers consume activities. A model can be built which calculates loss ratio based on Activity Based Costing.


Carbon Black warns that artificial intelligence is not a silver bullet

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The research, which Carbon Black says looked "Beyond the Hype" found that the roles of AI and ML in preventing cyber-attacks have been met with both hope and skepticism. The vast majority (93 percent) of the 400 security researchers interviewed while conducting this research said non-malware attacks pose more of a business risk than commodity malware attacks, and more importantly that these are often not stopped by traditional anti-virus offerings. Mike Viscuso, co-founder and CTO of Carbon Black told SC Media UK: "Researchers have reported seeing an increase in the number, and sophistication, of non-malware attacks. These attacks are specifically designed to evade file-based prevention mechanisms and leverage native operating system tools to keep attackers under the radar." One respondent explained: "Most users seem to be familiar with the idea that their computer or network may have accidentally become infected with a virus, but rarely consider a person who is actually attacking them in a more proactive and targeted manner."


The Care and Feeding of Machine Learning - Carbon Black

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Ingest incoming binaries: extract and compute features, statistics, and abstractions from incoming binaries. Binaries come from customers, partners, and trawls of the web for the diverse goodware and malware samples. The output of this task is a series of predictions about binaries' potential maliciousness and relationships to known malware families. Intelligence comes from our partners, our customers, and Carbon Black malware analysts.