TY - GEN
T1 - Identifying high-impact opioid products and key sellers in dark net marketplaces
T2 - 17th IEEE International Conference on Intelligence and Security Informatics, ISI 2019
AU - Du, Po Yi
AU - Ebrahimi, Mohammadreza
AU - Zhang, Ning
AU - Chen, Hsinchun
AU - Brown, Randall A.
AU - Samtani, Sagar
N1 - Funding Information:
This material is based upon work supported by the National Science Foundation under Grant No. NSF SES-1314631 and NSF CNS-1850362.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - As the Internet based applications become more and more ubiquitous, drug retailing on Dark Net Marketplaces (DNMs) has raised public health and law enforcement concerns due to its highly accessible and anonymous nature. To combat illegal drug transaction among DNMs, authorities often require agents to impersonate DNM customers in order to identify key actors within the community. This process can be costly in time and resource. Research in DNMs have been conducted to provide better understanding of DNM characteristics and drug sellers' behavior. Built upon the existing work, researchers can further leverage predictive analytics techniques to take proactive measures and reduce the associated costs. To this end, we propose a systematic analytical approach to identify key opioid sellers in DNMs. Utilizing machine learning and text analysis, this research provides prediction of high-impact opioid products in two major DNMs. Through linking the high-impact products and their sellers, we then identify the key opioid sellers among the communities. This work intends to help law enforcement authorities to formulate strategies by providing specific targets within the DNMs and reduce the time and resources required for prosecuting and eliminating the criminals from the market.
AB - As the Internet based applications become more and more ubiquitous, drug retailing on Dark Net Marketplaces (DNMs) has raised public health and law enforcement concerns due to its highly accessible and anonymous nature. To combat illegal drug transaction among DNMs, authorities often require agents to impersonate DNM customers in order to identify key actors within the community. This process can be costly in time and resource. Research in DNMs have been conducted to provide better understanding of DNM characteristics and drug sellers' behavior. Built upon the existing work, researchers can further leverage predictive analytics techniques to take proactive measures and reduce the associated costs. To this end, we propose a systematic analytical approach to identify key opioid sellers in DNMs. Utilizing machine learning and text analysis, this research provides prediction of high-impact opioid products in two major DNMs. Through linking the high-impact products and their sellers, we then identify the key opioid sellers among the communities. This work intends to help law enforcement authorities to formulate strategies by providing specific targets within the DNMs and reduce the time and resources required for prosecuting and eliminating the criminals from the market.
KW - Dark Net Marketplace (DNM)
KW - High-impact opioid product prediction
KW - Key seller identification
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=85072986177&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85072986177&partnerID=8YFLogxK
U2 - 10.1109/ISI.2019.8823196
DO - 10.1109/ISI.2019.8823196
M3 - Conference contribution
AN - SCOPUS:85072986177
T3 - 2019 IEEE International Conference on Intelligence and Security Informatics, ISI 2019
SP - 110
EP - 115
BT - 2019 IEEE International Conference on Intelligence and Security Informatics, ISI 2019
A2 - Zheng, Xiaolong
A2 - Abbasi, Ahmed
A2 - Chau, Michael
A2 - Wang, Alan
A2 - Zhou, Lina
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 1 July 2019 through 3 July 2019
ER -