Machine Learning in Cyber Security
While the underlying techniques for Machine Learning can be complex, the primary goal is relatively simple – to use a set of known examples (e.g. identified attacks) to train a generalized model that can estimate a value or a verdict for previously unknown examples (e.g. new attacks).
What’s needed is a framework that demystifies Machine Learning and removes the associated stigma by enabling organizations to navigate the buying process. The core components of Features, Training Set, Algorithm and Accuracy can be used to understand, analyze and evaluate any security solution based on Machine Learning.
Read our Machine Learning executive brief to find out how.