基于平衡子图采样算法的区块链交易异常检测

While the development of blockchain technology brings us convenience, there are also many security problems.Blockchain trading is popular because of its anonymity, but it also leads to rampant illegal activities such as money laundering.The blockchain transaction data set has a large number of noise nodes and unbalanced label proportion, while the blockchain transaction detection technology EvolveGCN model has poor processing effect on the data set, and there are defects such as ignoring the graph structure information.Therefore, this thesisproposes a balanced subgraph sampling algorithm to solve the above problems.Specifically, the main research work of this thesisis as follows:Propose the application of subgraph sampling algorithm to reduce the time and space complexity of EvolveGCN in processing blockchain transaction data sets, and improve the balanced subgraph sampling algorithm composed of noise removal algorithm and proportional subgraph selection algorithm:The selective noise removal algorithm preserves the important unknown nodes cleared by the EvolveGCN indifference removal algorithm and adds false labels; Compared with the example graph selection algorithm, it makes full use of the graph structure information ignored when EvolveGCN selects the training set to select the subgraph.Through three groups of ablation experiments, the effectiveness of the two sub algorithms and the whole of the balanced subgraph sampling algorithm are verified respectively. Taking the F1 sorce of illegal nodes as the experimental index, the balanced subgraph sampling algorithm is improved by 10.2%. Finally, through comparative experiments, the best training set is selected, and the proportion of legal and illegal labels is 2:5.