The rare form of machine learning that can spot hackers who have already broken in

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The rare form of machine learning that can spot hackers who h
Darktrace’s unsupervised-learning models sound the alarm before intruders c

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Based on that idea, the group founded a new cybersecurity company called Darktrace.
The firm partnered with mathematicians at the University of Cambridge to develop a tool that would use machine learning to catch internal breaches.
Rather than train the algorithms on historical examples of attacks, however, they needed a way for the system to recognize new instances of anomalous behavior.
They turned to unsupervised learning, a technique based on a rare type of machine-learning algorithm that doesn’t require humans to specify what to look for.
The vast majority of machine-learning applications rely on supervised learning.
This involves feeding a machine massive amounts of carefully labeled data to train it to recognize a narrowly defined pattern.
Say you want your machine to recognize golden retrievers.
In cybersecurity, supervised learning works pretty well.
For another, supervised-learning algorithms work best with balanced data sets—in other words, ones that have an equal number of examples of what it’s looking for and what it can ignore.
Cybersecurity data is highly unbalanced: there are very few examples of threatening behavior buried in an overwhelming amount of normal behavior.
Fortunately, where supervised learning falters, unsupervised learning excels.
That raw data is funneled to over 60 different unsupervised-learning algorithms that compete with one another to find anomalous behavior.
AN UNSUPERVISED SPACE Several other companies have also converged on using unsupervised learning to heighten digital security systems.
Shape Security combines the complementary strengths of both supervised and unsupervised techniques.
DataVisor says it primarily uses unsupervised techniques.
All that complexity is packaged into a final visualization that allows human operators to quickly see and respond to likely breache

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