Machine Learning in Cybersecurity: Fact, Fantasy, and Moving Forward
In the last several years we have seen personal assistants like Alexa, self-driving cars, and automated foreign language translation all made possible by machine learning. Looking for new investment opportunities, the venture capital community has turned its attention to machine learning in cybersecurity and is investing millions of dollars into the sector. Now, nearly every product advertises machine learning, which has led to confusion between what’s real and what’s marketing. This talk will demystify machine learning in cybersecurity, separating fact from fantasy. We will discuss what machine learning is, why it is particularly challenging in cybersecurity, and how to design machine learning applications that will have the biggest impact for cyber applications.
Dan Liebermann is a senior associate at Booz Allen where he leads the advanced analytics team in the firm’s commercial practice. He has thirteen years of experience leading the development and implementation of successful strategic and technical solutions in the data and analytics space. Over the course of his career he has developed advanced analytics solutions as a data scientist and analyst, and most recently has focused on helping organizations develop and stand-up their own advanced analytics capabilities across industries. Mr. Liebermann holds a master’s degree from Columbia University, a bachelor’s degree from Carnegie Mellon University, and certifications in data science, agile management, and project management.