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

Abstract

Darknet market forums are frequently used to exchange illegal goods and services between parties who use encryption to conceal their identities. The Tor network is used to host these markets, which guarantees additional anonymization from IP and location tracking, making it challenging to link across malicious users using multiple accounts (sybils). Additionally, users migrate to new forums when one is closed further increasing the difficulty of linking users across multiple forums. We develop a novel stylometry-based multitask learning approach for natural language and model interactions using graph embeddings to construct low-dimensional representations of short episodes of user activity for authorship attribution. We provide a comprehensive evaluation of our methods across four different darknet forums demonstrating its efficacy over the state-of-the-art, with a lift of up to 2.5X on Mean Retrieval Rank and 2X on Recall@10.

Multi-Identity Recognition of Darknet Vendors Based on Metric Learning

“…The anonymity of the dark web makes it difficult to track the identities of the buyers and sellers in these transactions. Additionally, some vendors register accounts on the same market or multiple markets to reach a larger pool of potential buyers [7,8]. In this context, researching the problem of identifying vendors' multiple identities on the dark web is particularly important.…”