Artificial intelligence (AI) and machine learning (ML) are incredibly powerful tools for the security industry as a whole, not to mention their capabilities when applied to any industry.
Once I started learning ML while working at Cylance, I identified how powerful of a tool it was. It changed how I thought about problems, and enabled me to tackle problems at scales that otherwise I would consider impossible. Not only that, but it allowed me to free up my own time; as the ML does work, I can be working on more, enabling me to not only handle harder problems, but more of them at once. As an employee and potential hire by other organizations, my value drastically increased.
Let’s be real, it’s also just really cool to work with. For example, our 2016 Blackhat USA talk on ML in infosec shows a bunch of fun examples:
VIDEO: Cylance Data Science Team at Black Hat 2016
When getting into the ML world, I found many educational resources had this hyper focus on explaining on how every algorithm worked instead of showing how ML could be practically applied. They also typically didn’t focus on what a developer/researcher/etc. would really need to know to get started with machine learning.
No, you don’t need to be able to do back propagation by hand to utilize a neural network, but it does help to understand the theory. For this reason, in our new book, we focus more on algorithms at a high level, and avoid digging too much into the weeds of everything. We also provide examples that allow for immediate takeaways with simple tools that implement machine learning techniques explained in each chapter.
We start out by covering the topic of clustering. Clustering is essentially grouping pieces of (Read more...)
This is a Security Bloggers Network syndicated blog post authored by The Cylance Data Science Team. Read the original post at: Cylance Blog