TY - GEN
T1 - EDarkFind
T2 - 29th International World Wide Web Conference, WWW 2020
AU - Kumar, Ramnath
AU - Yadav, Shweta
AU - Daniulaityte, Raminta
AU - Lamy, Francois
AU - Thirunarayan, Krishnaprasad
AU - Lokala, Usha
AU - Sheth, Amit
N1 - Publisher Copyright:
© 2020 ACM.
PY - 2020/4/20
Y1 - 2020/4/20
N2 - 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.
AB - 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.
KW - Correlation Analysis
KW - Darknet Market
KW - Drug Trafficker Identification
KW - Multi-view Learning
KW - Stylometry
KW - Sybil Detection
UR - http://www.scopus.com/inward/record.url?scp=85086566910&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85086566910&partnerID=8YFLogxK
U2 - 10.1145/3366423.3380263
DO - 10.1145/3366423.3380263
M3 - Conference contribution
AN - SCOPUS:85086566910
T3 - The Web Conference 2020 - Proceedings of the World Wide Web Conference, WWW 2020
SP - 1955
EP - 1965
BT - The Web Conference 2020 - Proceedings of the World Wide Web Conference, WWW 2020
PB - Association for Computing Machinery, Inc
Y2 - 20 April 2020 through 24 April 2020
ER -