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Darknet market forums are frequently used to exchange illegal goods andservices between parties who use encryption to conceal their identities. TheTor network is used to host these markets, which guarantees additionalanonymization from IP and location tracking, making it challenging to linkacross malicious users using multiple accounts (sybils). Additionally, usersmigrate to new forums when one is closed, making it difficult to link usersacross multiple forums. We develop a novel stylometry-based multitask learningapproach for natural language and interaction modeling using graph embeddingsto construct low-dimensional representations of short episodes of user activityfor authorship attribution. We provide a comprehensive evaluation of ourmethods across four different darknet forums demonstrating its efficacy overthe state-of-the-art, with a lift of up to 2.5X on Mean Retrieval Rank and 2Xon Recall@10.

SYSML: StYlometry with Structure and Multitask Learning: Implications for Darknet Forum Migrant Analysis