Abstract
Darknet crypto markets are online marketplaces using crypto currencies (e.g., Bitcoin, Monero) and advanced encryption techniques to offer anonymity to vendors and consumers trading for illegal goods or services. The exact volume of substances advertised and sold through these crypto markets is difficult to assess, at least partially, because vendors tend to maintain multiple accounts (or Sybil accounts) within and across different crypto markets. Linking these different accounts will allow us to accurately evaluate the volume of substances advertised across the different crypto markets by each vendor. In this paper, we present a multi-view unsupervised framework (eDarkFind) that helps modeling vendor characteristics and facilitates Sybil account detection. We employ a multi-view learning paradigm to generalize and improve the performance by exploiting the diverse views from multiple rich sources such as BERT, stylometric, and location representation. Our model is further tailored to take advantage of domain-specific knowledge such as the Drug Abuse Ontology to take into consideration the substance information. We performed extensive experiments and demonstrated that the multiple views obtained from diverse sources can be effective in linking Sybil accounts. Our proposed eDarkFind model achieves an accuracy of 98% on three real-world datasets which shows the generality of the approach.
Original language | English |
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Title of host publication | WWW '20: Proceedings of The Web Conference 2020 |
Publisher | Association for Computing Machinery, Inc |
Pages | 1955-1965 |
Number of pages | 11 |
ISBN (Electronic) | 9781450370233 |
DOIs | |
State | Published - Apr 20 2020 |
Externally published | Yes |
Event | 29th International World Wide Web Conference - Taipei, Taiwan, Province of China Duration: Apr 20 2020 → Apr 24 2020 Conference number: 29 |
Conference
Conference | 29th International World Wide Web Conference |
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Abbreviated title | WWW 2020 |
Country/Territory | Taiwan, Province of China |
City | Taipei |
Period | 4/20/20 → 4/24/20 |
ASJC Scopus Subject Areas
- Computer Networks and Communications
- Software
Keywords
- Correlation Analysis
- Darknet markets
- Drug Trafficker Identification
- Multi-view Learning
- Stylometry
- Sybil Detection
Disciplines
- Computer Sciences
- Engineering