This paper addresses the challenge of user identification in anonymity networks like Tor, which host underground markets and discussion forums for illegal activities. The authors propose classification methods for two key tasks: alias classification and authorship attribution. By applying these methods to a Tor forum focused on drug trafficking, they achieve high accuracy. The approach combines character-level n-grams, stylometric features, and timestamp data to effectively identify users despite anonymization techniques.