Anomaly Detection in the Bitcoin System - A Network Perspective

TL;DR

This paper presents a network analysis approach for anomaly detection in the Bitcoin transaction network, utilizing degree laws, densification, and local outlier factor methods to identify suspicious users and transactions.

Contribution

It introduces a novel application of power degree laws, densification, and LOF on Bitcoin transaction graphs for anomaly detection, adaptable to various network types.

Findings

Effective detection of suspicious Bitcoin users and transactions.

Applicable methodology for different graph-structured networks.

Demonstrates the utility of combining degree laws, densification, and LOF.

Abstract

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.