# Anomaly Detection in Bitcoin Network Using Unsupervised Learning Methods

arXiv: https://arxiv.org/abs/1611.03941 · HTML: https://mlsanity.com/paper/1611.03941/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Nov 2016.

## Built on by (most cited first, 11 of 11)

- [Demystifying Fraudulent Transactions and Illicit Nodes in the Bitcoin Network for Financial Forensics](https://mlsanity.com/paper/2306.06108/) (2023)
- [Inspection-L: Self-Supervised GNN Node Embeddings for Money Laundering Detection in Bitcoin](https://mlsanity.com/paper/2203.10465/) (2022)
- [Anomaly Detection in Multiplex Dynamic Networks: from Blockchain Security to Brain Disease Prediction](https://mlsanity.com/paper/2211.08378/) (2022)
- [Graph Mining for Cybersecurity: A Survey](https://mlsanity.com/paper/2304.00485/) (2023)
- [Detecting Anomalies in Blockchain Transactions using Machine Learning Classifiers and Explainability Analysis](https://mlsanity.com/paper/2401.03530/) (2024)
- [Detecting Malicious Accounts showing Adversarial Behavior in Permissionless Blockchains](https://mlsanity.com/paper/2101.11915/) (2021)
- [New Online Communities: Graph Deep Learning on Anonymous Voting Networks to Identify Sybils in Polycentric Governance](https://mlsanity.com/paper/2311.17929/) (2023)
- [A Bytecode-based Approach for Smart Contract Classification](https://mlsanity.com/paper/2106.15497/) (2021)
- [Abnormal Trading Detection in the NFT Market](https://mlsanity.com/paper/2306.04643/) (2023)
- [Chainlet Orbits: Topological Address Embedding for the Bitcoin Blockchain](https://mlsanity.com/paper/2306.07974/) (2023)
- [Hybrid GCN-GRU Model for Anomaly Detection in Cryptocurrency Transactions](https://mlsanity.com/paper/2509.07392/) (2025)

## Abstract (arXiv)

The problem of anomaly detection has been studied for a long time. In short, anomalies are abnormal or unlikely things. In financial networks, thieves and illegal activities are often anomalous in nature. Members of a network want to detect anomalies as soon as possible to prevent them from harming the network's community and integrity. Many Machine Learning techniques have been proposed to deal with this problem; some results appear to be quite promising but there is no obvious superior method. In this paper, we consider anomaly detection particular to 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 three unsupervised learning methods including k-means clustering, Mahalanobis distance, and Unsupervised Support Vector Machine (SVM) on two graphs generated by the Bitcoin transaction network: one graph has users as nodes, and the other has transactions as nodes.