Your Style Your Identity: Leveraging Writing and Photography Styles for Drug Trafficker Identification in Darknet Markets over Attributed Heterogeneous Information Network
Your Style Your Identity: Leveraging Writing and Photography Styles for Drug Trafficker Identification in Darknet Markets over Attributed Heterogeneous Information Network
复制标题
DOI：
10.1145/3308558.3313537
复制
发表时间：
2019-05
期刊：
影响因子：
--
通讯作者：
Yiming Zhang;Yujie Fan;Wei Song;Shifu Hou;Yanfang Ye;X. Li;Liang Zhao;C. Shi;Jiabin Wang;Qi Xiong
中科院分区：
文献类型：
--
作者：
Yiming Zhang;Yujie Fan;Wei Song;Shifu Hou;Yanfang Ye;X. Li;Liang Zhao;C. Shi;Jiabin Wang;Qi Xiong
由于其匿名性，在Darknet（例如，梦想市场和Valhalla）中，地下药品市场的增长急剧增长。为了打击网络空间中的毒品贩运（又称非法药物交易），迫切需要自动分析DarkNet市场的参与者。但是，主要挑战之一是贩毒者（即供应商）可以在不同市场或同一市场内维持多个帐户。为了解决这个问题，在本文中，我们提出并开发了一个名为USTYLE-UID的智能系统，该系统在第一次尝试时利用了毒品贩运者身份的写作和摄影风格。 USTYLE-UID的核心是一个归因的异质信息网络（AHIN），它优雅地整合了写作和摄影样式，以及文本和照片内容，以及其他支持属性（即贩运者和药物信息）以及各种各样的属性关系。建立在构造的hin上，以有效地衡量构造的ahin中与节点（即贩运者）的相关性，我们提出了一个新的网络嵌入模型vendor2vec，以了解有关ahin节点的低维表示，该节点附加了互补的属性信息。在节点中指导基于元路径的随机步行进行路径实例采样。之后，我们设计了一个名为Videntifier的学习模型，以分类给定的贩运者是否是同一个人。对来自四个不同暗网市场的数据收集的全面实验，以验证uStyle-uid的有效性，这通过与替代方法进行比较，将我们提出的方法整合在毒品贩运者识别中。
Due to its anonymity, there has been a dramatic growth of underground drug markets hosted in the darknet (e.g., Dream Market and Valhalla). To combat drug trafficking (a.k.a. illicit drug trading) in the cyberspace, there is an urgent need for automatic analysis of participants in darknet markets. However, one of the key challenges is that drug traffickers (i.e., vendors) may maintain multiple accounts across different markets or within the same market. To address this issue, in this paper, we propose and develop an intelligent system named uStyle-uID leveraging both writing and photography styles for drug trafficker identification at the first attempt. At the core of uStyle-uID is an attributed heterogeneous information network (AHIN) which elegantly integrates both writing and photography styles along with the text and photo contents, as well as other supporting attributes (i.e., trafficker and drug information) and various kinds of relations. Built on the constructed AHIN, to efficiently measure the relatedness over nodes (i.e., traffickers) in the constructed AHIN, we propose a new network embedding model Vendor2Vec to learn the low-dimensional representations for the nodes in AHIN, which leverages complementary attribute information attached in the nodes to guide the meta-path based random walk for path instances sampling. After that, we devise a learning model named vIdentifier to classify if a given pair of traffickers are the same individual. Comprehensive experiments on the data collections from four different darknet markets are conducted to validate the effectiveness of uStyle-uID which integrates our proposed method in drug trafficker identification by comparisons with alternative approaches.