2016/11/12 by Thai Pham, Steven Lee, Pham, Thai +1 · 55 citations
Computer Science · #Anomaly Detection Techniques and Applications #Anomaly detection #Artificial intelligence #Cluster analysis #Computer science #Computer security #Cryptography and Security (cs.CR) #Data Stream Mining Techniques #Data mining #Database #Database transaction #FOS: Computer and information sciences #Graph #Local outlier factor #Machine learning #Network Security and Intrusion Detection #Network security #Proxy (statistics) #Social and Information Networks (cs.SI) #Theoretical computer science #Transaction data #cs.CR #cs.SI
paper · pdf · doi:10.48550/arxiv.1611.03942
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2016/11/12 · arxiv created 2017/02/24 · arxiv updated 2017/02/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The problem of anomaly detection has been studied for a long time, and many Network Analysis techniques have been proposed as solutions. Although some results appear to be quite promising, no method is clearly to be superior to the rest. In this paper, we particularly consider anomaly detection in the Bitcoin transaction network. Our goal is to detect which users and transactions are the most suspicious; in this case, anomalous behavior is a proxy for suspicious behavior. To this end, we use the laws of power degree and densification and local outlier factor (LOF) method (which is proceeded by k-means clustering method) on two graphs generated by the Bitcoin transaction network: one graph has users as nodes, and the other has transactions as nodes. We remark that the methods used here can be applied to any type of setting with an inherent graph structure, including, but not limited to, computer networks, telecommunications networks, auction networks, security networks, social networks, Web networks, or any financial networks. We use the Bitcoin transaction network in this paper due to the availability, size, and attractiveness of the data set.