银行账户交易网络中特定组织发现研究  

Research on the method of discovering specific organization structure in bank account transaction network

作　　者：吕芳 卢西婧 王巍[1] 黄俊恒[1] 王佰玲[1] LYU Fang;LU Xijing;WANG Wei;HUANG Junheng;WANG Bailing(School of Computer Science and Technology,Harbin Institute of Technology(Weihai),Weihai 264209,China)

机构地区：[1]哈尔滨工业大学(威海)计算机科学与技术学院,山东 威海 264209

出　　处：《网络与信息安全学报》2020年第1期62-69,共8页Chinese Journal of Network and Information Security

基　　金：国家重点研发计划基金资助项目(No.2018YFB2004201);国家前沿科技创新专项基金资助项目(No.2016QY05X1002-2);国家区域创新中心科技专项基金资助项目(No.2017QYCX14);山东省重点研发计划基金资助项目(No.2017CXGC0706);中央高校基本科研业务费专项基金资助项目(No.HIT.NSRIF.2020098);2017威海市大学共建基金资助项目。

摘　　要：近年来,非法传销、非法集资和洗钱等涉众型非法金融活动屡禁不止,从资金交易网络中进行异常检测的研究,逐渐引起研究者的重视。非法组织中银行账户间的资金流转方式隐含了其成员的关系架构。以关键角色账户为核心种子节点,结合交易关系进行特定异常组织的发现研究。首先,基于银行账户的交易特点,建立了一个有向加权资金交易网络模型。进而,结合账户的局部拓扑结构,定义了组织中的两种核心节点,即黑洞节点和星光节点。利用两种节点的关联关系,提出一种"黑洞&星光"组织发现算法。在含有传销组织的真实银行交易数据上进行实验,结果表明上述算法对发现传销组织的有效性。In recent years,stakeholder economic crime behaviors such as illegal pyramid schemes,illegal fund raising and money laundering despite repeated prohibitions,makes the research of anomaly detection in financial transaction network has gradually attracted the attention of researchers.The way how to fund flow between bank accounts in an illegal organization implies the relationship structure of their members.Firstly,a directed weighted transaction network model was built on the basis of the transaction characteristics.Then,combining with the local topology structure of the built transaction network of the accounts,two kinds of core nodes of the organization,including black hole nodes and star nodes,were defined.By analyzing the relationship between those two kinds nodes,an organization discovery algorithm of combining"black hole and star nodes"based on spanning subgraph was proposed.Experiments on real bank accounts transaction network containing illegal pyramid scheme organizations show the effectiveness of the algorithm in discovering the specific tree organization structure.

关 键 词：资金交易网络 黑洞节点 星光节点 生成子图 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]