Visible to the public Abnormal transaction detection of Bitcoin network based on feature fusion

TitleAbnormal transaction detection of Bitcoin network based on feature fusion
Publication TypeConference Paper
Year of Publication2020
AuthorsLiao, Q., Gu, Y., Liao, J., Li, W.
Conference Name2020 IEEE 9th Joint International Information Technology and Artificial Intelligence Conference (ITAIC)
Date PublishedDec. 2020
ISBN Number978-1-7281-5244-8
Keywordsanomaly detection, bitcoin, blockchain, Data analysis, feature extraction, Feature fusion, History, Human Behavior, network analysis, pubcrawl, Scalability, security, Tools

Anomaly detection is one of the research hotspots in Bitcoin transaction data analysis. In view of the existing research that only considers the transaction as an isolated node when extracting features, but has not yet used the network structure to dig deep into the node information, a bitcoin abnormal transaction detection method that combines the node's own features and the neighborhood features is proposed. Based on the formation mechanism of the interactive relationship in the transaction network, first of all, according to a certain path selection probability, the features of the neighbohood nodes are extracted by way of random walk, and then the node's own features and the neighboring features are fused to use the network structure to mine potential node information. Finally, an unsupervised detection algorithm is used to rank the transaction points on the constructed feature set to find abnormal transactions. Experimental results show that, compared with the existing feature extraction methods, feature fusion improves the ability to detect abnormal transactions.

Citation Keyliao_abnormal_2020