Frequent path discovery algorithm for financial network

Fang LYU; Fenghe TANG; Junheng HUANG; Bailing WANG

doi:10.11959/j.issn.2096-109x.2019050

您当前的位置：

首页 >

文章列表页 >

Frequent path discovery algorithm for financial network

Special Column: Routing Technology in Complex Network Environment | 更新时间：2024-06-03

- Frequent path discovery algorithm for financial network
- Chinese Journal of Network and Information Security Vol. 5, Issue 5, Pages: 48-55(2019)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-109x.2019050
  CLC： TP393
- Online First：2019-10，
  
  Published：15 October 2019
- 稿件说明：
移动端阅览
Fang LYU, Fenghe TANG, Junheng HUANG, et al. Frequent path discovery algorithm for financial network[J]. Chinese Journal of Network and Information Security, 2019, 5(5): 48-55.
DOI：

Fang LYU, Fenghe TANG, Junheng HUANG, et al. Frequent path discovery algorithm for financial network[J]. Chinese Journal of Network and Information Security, 2019, 5(5): 48-55. DOI： 10.11959/j.issn.2096-109x.2019050.

摘要

随着各种非法金融活动的泛滥，从金融网络中发现犯罪线索的分析研究越来越引起学者的重视。对银行账户交易数据的特点进行了详细分析，建立了银行账户交易网络通用模型。在此基础上，为解决金融实体之间关系强度的评估问题，提出了双向活跃边搜索计算方法。为了还原犯罪组织的资金流动方式，提出了深度可控的广度优先频繁链路发现方法。在真实银行数据上的实验证明，上述方法能有效解决同伙预测和资金追踪问题。

Abstract

With the proliferation of various illegal financial activities

more and more attention is paid to the research of finding criminal cues in financial network by scholars.The characteristics of the transaction data generated by bank accounts are analyzed in detail

and a general model of bank account transaction network is established.On this basis

a two-direction active edge searching method is proposed to solve the problem of evaluating the relationship strength between financial entities.And then

a breadth-first frequent path discovery algorithm with depth controlled is presented

with which the way how the financial flows is restored.Experiment results on the real bank data show that the above two methods are effective in solving the problem of peer prediction and financial tracking respectively.

关键词

Keywords

references

YU H , KAMINSKY M , Gibbons P B , et al . SybilGuard:defending against sybil attacks via social networks [J ] . IEEE/ACM Transactions on Networking , 2008 , 16 ( 3 ): 576 - 589 .

TRAN N , LI J , SUBRAMANIAN L , et al . Optimal Sybil-resilient node admission control [C ] // IEEE Infocom . 2015 .

YANG C , HARKREADER R , ZHANG J , et al . Analyzing spammers' social networks for fun and profit:a case study of cyber criminal ecosystem on twitter [C ] // International Conference on World Wide Web . 2012 .

刘枭 , 王晓国 . 基于概率图的银行电信诈骗检测方法 [J ] . 计算机科学 , 2018 , 45 ( 7 ): 122 - 128 .

LIU X , WANG X G . Probabilistic graphical model based approach for bank telecommunication fraud detection [J ] . Computer Science , 2018 , 45 ( 7 ): 122 - 128 .

丁濛濛 . 基于规则引擎的互联网金融反欺诈研究 [J ] . 电脑知识与技术 , 2018 , 14 ( 1 ): 1 - 3 .

DING M M . Internet finance anti-fraud research based on rule engine [J ] . Computer Knowledge and Technology , 2018 , 14 ( 1 ): 1 - 3 .

张成虎 , 李时 . 基于 AI 技术的反洗钱系统设计 [J ] . 中国金融电脑 , 2005 ,( 3 ): 44 - 47 .

ZHANG C H , LI S . Design of anti-money laundering system based on AI technology [J ] . Financial Computer of China , 2005 ,( 3 ): 44 - 47 .

喻炜 , 王建东 . 基于交易网络特征向量中心度量的可疑洗钱识别系统 [J ] . 计算机应用 , 2009 , 29 ( 9 ): 2581 - 2585 .

YU Y , WANG J D . Suspicious money laundering detection system based on eigenvector centrality measure of transaction network [J ] . Journal of Computer Applications , 2009 , 29 ( 9 ): 2581 - 2585 .

孙景 , 陈婧 , 万红 . 基于复杂网络的可疑金融交易识别研究 [J ] . 数字技术与应用 , 2013 , 4 ( 149 ): 206 - 207 .

SUN J , CHEN J , WAN H . Research on suspicious financial transaction identification based on complex network [J ] . Digital Technology and Application , 2013 , 4 ( 149 ): 206 - 207 .

刘丽芳 , 陶文立 , 陈延妙 . 拓扑工具在反洗钱关联账户资金流分析中的运用 [J ] . 福建金融 , 2013 , 2 : 39 - 44 .

LIU L F , TAO W L , CHEN Y M . Application of topology tools in analysis of fund flow of anti-money laundering related accounts [J ] . Fujian Finance , 2013 , 2 : 39 - 44 .

WANG S N , YANG J G . A money laundering risk evaluation method based on decision tree [C ] // International Conference on Machine Learning and Cybernetics . 2007 .

LIU Y , . Based on social network crime organization relation mining and central figure determining [C ] // IEEE 3rd International Conference on Software Engineering and Service Science . Beijing,China , 2012 .

李艳丽 , 刘阳 , 谢文波 , 等 . 大数据发现非法传销网络 [J ] . 大数据 , 2017 , 3 ( 5 ): 106 - 112 .

LI Y L , LIU Y , XIE W B , et al . Detecting illegal pyramid scheme network in big data [J ] . Big Data , 2017 , 3 ( 5 ): 106 - 112 .

Views

1173

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

No data

Related Author

No data

Related Institution

No data

⁰