摘要
随着区块链技术的发展,以比特币为代表的虚拟货币已成为洗钱、黑客攻击、电信网络诈骗等犯罪行为的重要工具,给公民人身和财产安全带来了严重威胁,甚至威胁到国家金融市场的稳定。因此,针对基于区块链技术的虚拟货币异常交易数据检测的研究具有重要的意义。文章首先使用自定义的滑动窗口机制提取区块链交易数据特征;然后根据区块链交易数据的特点,从3个通道把数据处理成3个向量;最后对这3个特征向量进行拼接,构建区块链异常交易数据检测模型。文章使用区块链情报公司Elliptic发布的数据集验证模型的可行性和优越性,实验得出模型的准确率、召回率和F1值分别达到92.96%、85%和92.43%。实验结果表明,基于多特征融合的特征向量包含更加丰富的区块链交易信息,能够有效提升区块链异常交易检测的性能。
With the development of blockchain technology, virtual currency represented by bitcoin has become an important tool for money laundering, hacker attacks, telecommunications network fraud and other crimes, which poses a serious threat to the personal and property security of citizens, and even threatens the stability of the national financial market. Therefore,the research on abnormal transaction data detection of virtual currency based on blockchain technology is of great significance. Firstly, this paper use the custom sliding window mechanism to extract the characteristics of blockchain transaction data. Secondly, it procesed from three channels to form three feature vectors according to the characteristics of blockchain transaction data. Finally, it spliced these three feature vectors to build a blockchain abnormal transaction data detection model. This paper verified the feasibility and superiority of the model with the data set released by the blockchain intelligence company Elliptic. The precision,recall and F1 values of the model reached 92.96%, 85% and 92.43%. The experimental results show that the feature vector based on multi-feature fusion contains more abundant blockchain transaction information, which can effectively improve the performance of blockchain abnormal transaction detection.
作者
林伟
LIN Wei(Investigation Department,Fujian Police College,Fuzhou 350007,China;Criminal Investigation School,Southwest University of Political Science and Law,Chongqing 401120,China)
出处
《信息网络安全》
CSCD
北大核心
2022年第10期24-30,共7页
Netinfo Security
基金
国家自然科学基金[61871087]。
关键词
区块链
异常交易检测
滑动窗口机制
多特征融合
blockchain
abnormal transaction detection
sliding window mechanism
multi-feature fusion
