金融网络频繁链路发现算法

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

Abstract

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.

Key words: two-direction active edge, frequent path, peer prediction, financial tracking

CLC Number:

TP393

Fang LYU, Fenghe TANG, Junheng HUANG, Bailing WANG. Frequent path discovery algorithm for financial network[J]. Chinese Journal of Network and Information Security, 2019, 5(5): 48-55.

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References 12

[1]	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.
[2]	TRAN N , LI J , SUBRAMANIAN L ,et al. Optimal Sybil-resilient node admission control[C]// IEEE Infocom. 2015.
[3]	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.
[4]	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.
[5]	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.
[6]	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.
[7]	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.
[8]	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.
[9]	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.
[10]	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.
[11]	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.
[12]	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.

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Frequent path discovery algorithm for financial network

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