【Author】
Wu, Yan; Tao, Fang; Liu, Lu; Gu, Jiayan; Panneerselvam, John; Zhu, Rongbo; Shahzad, Mohammad Nasir
【Source】IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
【Abstract】Popular Blockchain-based cryptocurrencies, like Bitcoin, are increasingly being used maliciously for illegal trades. In order to trace and analyze suspected Bitcoin transactions and addresses, address clustering methods and Bitcoin flow analysis methods are gaining attention recently. However, existing methods only focus on Bitcoin addresses and flow, and neglect other important information, such as transaction structure and behavior features. In order to exploit all useful features of transactions, this paper proposes a Bitcoin transaction network analytic method for facilitating Blockchain forensic investigation based on an extended safe Petri Net. The structural features and dynamic semantics of Petri net are used in our proposed model to define the static and dynamic features of Bitcoin transactions. Nineteen features have been identified to define Bitcoin transaction patterns for analyzing and finding suspected addresses. Bitcoin gene has been embedded into the Petri net transitions to trace and analyze Bitcoin flow accurately. Finally, marginal distribution analysis of Bitcoin transaction features and data visualization techniques are used to eliminate some false positive samples further and to improve the accuracy of identifying suspected addresses. The proposed Bitcoin transaction network analytic method provides a reliable forensic investigation model along with a prototype platform which is beneficial for financial security. The efficiency of our proposed method is empirically verified based on a real-life case study analysis.
【Keywords】Exponential distribution; Numerical models; Optimization; Production; Stochastic processes; Load modeling; Inspection; Bitcoin; blockchain; petri net; forensic investigation
【标题】面向未来区块链取证的比特币交易网络分析方法
【摘要】流行的基于区块链的加密货币，如比特币，越来越多地被恶意用于非法交易。为了跟踪和分析可疑的比特币交易和地址，地址聚类方法和比特币流量分析方法近年来受到关注。然而，现有的方法只关注比特币的地址和流量，而忽略了其他重要信息，如交易结构和行为特征。为了充分利用交易的所有有用特性，本文提出了一种基于扩展的安全Petri网的便于区块链取证的比特币交易网络分析方法。我们的模型使用Petri网的结构特征和动态语义来定义比特币交易的静态和动态特征。已经确定了19个特征来定义比特币交易模式，用于分析和查找可疑地址。比特币基因已经嵌入到Petri网变迁中，以准确地跟踪和分析比特币流。最后，利用比特币交易特征的边缘分布分析和数据可视化技术，进一步消除部分假阳性样本，提高可疑地址识别的准确性。提出的比特币交易网络分析方法提供了一个可靠的取证模型和一个有利于金融安全的原型平台。通过实际案例分析，实证验证了所提方法的有效性。
【关键词】指数分布;数值模型;优化;生产;随机过程;负荷建模;检查;比特币;区块链;佩特里网中;法医调查
评论