To make machine learning effective, you have to keep revisiting your work, retraining your algorithms over and over to produce newer and better classifiers as attackers keep retesting their threats against them. The machine becomes the analyst.īut the ‘learning’ part has to be done just right. Previously, writing a classifier was always work done by human analysts, but machine learning allows for it to be done in an automatic way without a human needing to write the program. The algorithm outputs a ‘classifier’ that can then be used to look at a new file it has never seen before and determine if that file, or that URL, or that situation on an endpoint, whatever the particular in question may be, is good or bad. A bit more deeply.Īccording to Eric Chien, Distinguished Engineer and Technical Director of Symantec’s Security Technology and Response (STAR) Division, “the reality is we've been doing machine learning for years.”Īt its simplest level, cyber security machine learning involves feeding large amounts of data about both malicious and legitimate files into an algorithm. It may be currently hailed as security’s ‘shiny new toy,’ but for many it is just a newly-purchased hammer and now everything looks like a nail.Īt Symantec, however, machine learning is seen a bit differently. Yet across the cyber security industry, machine learning is often a buzz-phrase, casually thrown about without any real understanding of what it actually is, what it truly can do, or what it requires.
The field continues to amaze (and promise profits). Today that may seem quaint, but when Samuel’s program was first demonstrated, so the story goes, IBM’s stock jumped 15 points, overnight. The term ‘Machine Learning’ was first coined in 1959 by a pioneer in the field named Arthur Samuels, who developed a revolutionary computer program at IBM that could play checkers against a human opponent - and get better as it did.
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