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作 者:李致远[1,2,3] 徐丙磊 周颖仪 LI Zhiyuan;XU Binglei;ZHOU Yingyi(School of Computer Science and Communication Engineering,Jiangsu University,Zhenjiang,Jiangsu 212013,China;Jiangsu Provincial Key Laboratory of Industrial Network Security Technology,Zhenjiang,Jiangsu 212013,China;Jiangsu Province Ubiquitous Data Intelligent Perception and Analysis Application Engineering Research Center,Zhenjiang,Jiangsu 212013,China)
机构地区:[1]江苏大学计算机科学与通信工程学院,江苏镇江212013 [2]江苏省工业网络安全技术重点实验室,江苏镇江212013 [3]江苏省泛在数据智能感知与分析应用工程研究中心,江苏镇江212013
出 处:《计算机科学》2024年第7期422-429,共8页Computer Science
基 金:国家重点研发计划(2020YFB1005503);江苏省自然科学基金面上项目(BK20201415)。
摘 要:随着区块链技术的快速发展,借助虚拟货币进行非法交易的行为越来越普遍,且数量仍在快速增长。为打击该类犯罪行为,目前主要从网络分析技术和图数据挖掘等角度研究区块链交易数据,以进行区块链交易追踪。然而,现有的研究在有效性、普适性以及效率等方面存在不足,且无法对新注册地址进行有效追踪。针对上述问题,文中提出了一种基于节点影响力的账户余额模型区块链交易追踪方法NITT,旨在追踪特定目标账户模型地址的主要资金流向。相比传统方法,该方法引入时间策略,降低了图数据规模,同时采用多重权重分配策略,筛选出了更有影响力的重要账户地址。在真实数据集上进行实验,结果表明,所提方法在有效性、普适性和效率等方面具有较大的优势。With the rapid development of blockchain technology,illegal transactions with the help of virtual currencies are beco-ming increasingly common and still growing rapidly.In order to combat such crimes,blockchain transaction data are currently stu-died mainly from the perspectives of network analysis technology and graph data mining for blockchain transaction tracking.However,the existing studies are deficient in terms of effectiveness,generalizability,and efficiency,and cannot effectively track newly registered addresses.To address the above issues,a node-influence-based blockchain transaction tracking method NITT for account balance models is proposed in the paper,aiming to track the main fund flow of a specific target account model address.Compared with traditional methods,the proposed method introduces a temporal strategy to reduce the graph data size.It also filters out more influential and important account addresses by using a multiple weight assignment strategy.Experimental results on real datasets show that the proposed method has greater advantages in terms of effectiveness,generalizability and efficiency.
分 类 号:TP301[自动化与计算机技术—计算机系统结构]
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