The Promise of Machine Learning in Cybersecurity

I don’t normally like to post about news articles that cite me, but I’m particularly proud of two recent appearances.

The first is a defense of machine learning to help assist with solving some very hard but important problems in cybersecurity, on CSO Online:

I was inspired to submit content in response to Simon Crosby’s attack on machine learning on Dark Reading. While I agree with Crosby that there is a lot of snake oil and marketing in this very hot space, I feel strongly that it is dangerous to ignore techniques such as machine learning (and statistics and probabilistic methods and visualization and…), especially since those are exactly the tools that can help build exactly what Crosby is asking for: “tools that enhance their ability to quickly search for and identify components of a new attack”.

The second is an interview with me on CIM Magazine. Christopher Pollon did a great job asking the right questions, and the result was a very approachable description of exactly why machine learning and related methods hold so much promise.

Machine learning and other related mathematical and statistical methods are not magic, nor are they a silver bullet. But that doesn’t mean we should ignore them. They have do so much good and proven so effective in so many other problem domains and industries, from healthcare to power transmission to computer vision. We have only just started applying them to cybersecurity problems, and we need to keep going and learning together.

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