Analyzing And Detecting Abnormal Transaction Behaviors On Ethereum

As the value of blockchain virtual currency increases year by year,a series of abnormal transactions such as scams and extortion emerge one after another in the blockchain,causing serious economic losses all over the world.Ethereum has the characteristics of large number of users,large transaction scale,anonymous address and so on.Among them,the massive data and dynamic transaction need to greatly improve the performance requirements of the algorithm.The anonymization of address also causes many difficulties in tracing the transaction path.It can be seen that there are many factors that challenge the accuracy of transaction detection.The research on the detection method of abnormal transaction behavior on Ethereum is of great significance to ensure the security of Ethereum transaction and restrict Ethereum transaction behavior.The main research work is as follows:(1)Aiming at the problem that it is difficult to accurately describe the transaction behavior caused by the dynamics and anonymity of Ethereum transactions,this paper takes the analysis of the behavior characteristics of Ethereum oriented abnormal transactions as the starting point,and formulates seven behavior characteristics matching rules on Ethereum oriented abnormal transactions,including Ponzi scheme,Ethereum gambling and "dust" injection behavior,The corresponding characteristic subgraph is constructed.Matching rules and characteristic subgraphs can accurately describe the transaction behavior.Combined with the subgraph matching algorithm and rule matching algorithm proposed in this paper,it effectively solves the problem that it is difficult to detect abnormal transaction behavior in massive transaction data.(2)In view of the small scale of the experimental data set used in the current relevant research,which makes the granularity of the transaction behavior description coarse,resulting in the low detection accuracy,this paper obtains 8.97 million real Ethereum transaction data by means of crawlers,so as to ensure the integrity of the transaction data and the accuracy of the detection.Based on the above large amount of real transaction data,the detection accuracy of Ethereum oriented abnormal transaction behavior is as high as 89.97%.Through the experimental data,it is concluded that the subgraph matching algorithm can detect Ponzi scheme,Ethereum gambling and "dust" injection behavior more effectively than the rule matching algorithm.(3)Based on the above research methods and experimental evaluation results,this paper designs an Ethereum oriented abnormal transaction behavior detection system based on subgraph matching,which allows users to upload Ethereum transaction data or use system data,and realizes online detection of Ethereum abnormal transactions through the pre-designed abnormal transaction detection algorithm based on subgraph matching.The test shows that the system runs well,which proves that the subgraph matching algorithm is efficient and accurate in detecting abnormal transaction behavior on Ethereum.