The ML series blogs we posted, recently, focused on the details of creating ML models addressing VMRay’s defined use case, which is enhancing its phishing URL detection. In this series, we tackled how we engineered features (i.e. feature engineering) to be used in model training, using the clean output of VMRay’s superior technology.
We provided a few examples of what features we extracted, what’s the nature/classification of this feature (e.g. URL-content based), and what’s the source of this feature (i.e. which artifact from VMRay’s sandbox output). In addition, we also provided a brief discussion on how we used ensembling for the final prediction. ...read more!
