  Current content filtering and blocking methods are susceptible to various
circumvention techniques and are relatively slow in dealing with new threats.
This is due to these methods using shallow pattern recognition that is based on
regular expression rules found in crowdsourced block lists. We propose a novel
system that aims to remedy the aforementioned issues by examining deep textual
patterns of network-oriented content relating to the domain being interacted
with. Moreover, we propose to use federated learning that allows users to take
advantage of each other's localized knowledge/experience regarding what should
or should not be blocked on a network without compromising privacy. Our
experiments show the promise of our proposed approach in real world settings.
We also provide data-driven recommendations on how to best implement the
proposed system.
