Often in applying machine learning to cybersecurity, we are faced with highly imbalanced datasets. For instance, it may be much easier to access a large collection of benign samples than it is to collect malicious samples. Conversely, you may be working at an enterprise that, for legal reasons, is prohibited from saving benign samples. In either case, your dataset will be highly skewed toward one class. As a consequence, naive machine learning aimed at maximizing accuracy will result in a classifier that predicts almost all samples as coming from the overrepresented class. There are several techniques that can be used to tackle the challenge of class imbalance.
Tackling class imbalance
Getting ready
Preparation for this...
