Content-based deep learning for inline phishing detection

Abstract

An inline and offline machine learning pipeline for detection of phishing attacks with a holistic, easily upgradeable framework is presented herein. A packet analyzer records capture logs of network traffic between an endpoint device and a firewall. A parser extracts inputs from the capture logs inline that it communicates to one of an inline model and an offline model for phishing detection. The inline model and offline model are neural networks with parallelizable network architectures that do not depend on handcrafted inputs. The inline model operates inline with the packet analyzer and parser and makes fast phishing attack classifications based on inputs generated from capture logs. The offline model uses additional inputs such as inputs generated from network logs to make phishing attack classifications.