ADAM: Automated detection and attribution of malicious webpages

Malicious webpages are a prevalent and severe threat in the Internet security landscape. This fact has motivated numerous static and dynamic techniques for their accurate and efficient detection. Building on this existing literature, this work introduces ADAM, a system that uses machine-learning over network metadata derived from the sandboxed execution of webpage content. Machine-trained models are not novel in this problem space. Instead, it is the dynamic network artifacts (and their subsequent feature representations) collected during rendering that are the greatest contribution of this work.