Abstract
In recent years, there is a notable rise for proactive, intelligence-driven cyber defense mechanisms. Following this demand, we study here how to leverage the spread of adoption behavior among individuals to predict their posts on hacking forums of the darkweb, driven by the influential activities of their peers. We formulate our problem as a sequential rule mining task, where the goal is to discover user posting rules through sequences of user posts, to later use those rules to make predictions in a near future. We run our experiments using multiple post time granularities and time-windows for obtaining rules, observing precision results up to 0.78 and precision gains up to 837%, when compared to the prior probabilities of hackers posts. Our approach is an additional step in the fight against cyber-attacks.