在暗网市场中识别高影响力阿片类产品和关键卖家:可解释的文本分析方法

Po-Yi Du, Mohammadreza Ebrahimi, Ning Zhang, Hsinchun Chen, Randall A. Brown, S. Samtani
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引用次数: 1

摘要

随着基于互联网的应用程序变得越来越普遍,暗网市场(dnm)上的药品零售由于其高度可访问性和匿名性引起了公共卫生和执法方面的关注。为了打击DNM之间的非法毒品交易,当局经常要求代理人冒充DNM客户,以确定社区内的关键行为者。这个过程在时间和资源上都很昂贵。对DNM的研究是为了更好地理解DNM的特征和药物销售商的行为。在现有工作的基础上,研究人员可以进一步利用预测分析技术采取主动措施并降低相关成本。为此,我们提出了一种系统的分析方法来识别dnm中的关键阿片类药物销售商。利用机器学习和文本分析,本研究在两个主要的dnm中提供了高影响阿片类药物产品的预测。通过将高影响力产品与其卖家联系起来,我们确定了社区中关键的阿片类药物卖家。这项工作旨在帮助执法当局制定战略,在dnm内提供具体目标,减少起诉和消灭市场上的罪犯所需的时间和资源。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identifying High-Impact Opioid Products and Key Sellers in Dark Net Marketplaces: An Interpretable Text Analytics Approach
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.
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