The monitoring of underground criminal activities is often automated to maximize the data collection and to train ML models to automatically adapt data collection tools to different communities. On the other hand, sophisticated adversaries may adopt crawling-detection capabilities that may significantly jeopardize researchers' opportunities to perform the data collection, for example by putting their accounts under the spotlight and being expelled from the community. This is particularly undesirable in prominent and high-profile criminal communities where entry costs are significant (either monetarily or for example for background checking or other trust-building mechanisms). This work presents CARONTE, a tool to semi-automatically learn virtually any forum structure for parsing and data-extraction, while maintaining a low profile for the data collection and avoiding the requirement of collecting massive datasets to maintain tool scalability. We showcase CARONTE against four underground forum communities, and show that from the adversary's perspective CARONTE maintains a profile similar to humans, whereas state-of-the-art crawling tools show clearly distinct and easy to detect patterns of automated activity
