Center for Machine Learning and Intelligent Systems
About  Citation Policy  Donate a Data Set  Contact


Repository Web            Google
View ALL Data Sets

BitcoinHeistRansomwareAddressDataset Data Set
Download: Data Folder, Data Set Description

Abstract: BitcoinHeist datasets contains address features on the heterogeneous Bitcoin network to identify ransomware payments.

Data Set Characteristics:  

Multivariate, Time-Series

Number of Instances:

2916697

Area:

Computer

Attribute Characteristics:

Integer, Real

Number of Attributes:

10

Date Donated

2020-06-17

Associated Tasks:

Classification, Clustering

Missing Values?

N/A

Number of Web Hits:

5932


Source:

Cuneyt Gurcan Akcora (cuneyt.akcora '@' umanitoba.ca) University of Manitoba, Canada
Yulia Gel (ygl '@' utdallas.edu) University of Texas at Dallas, USA
Murat kantarcioglu (muratk '@' utdallas.edu) University of Texas at Dallas, USA


Data Set Information:

We have downloaded and parsed the entire Bitcoin transaction graph from 2009 January to 2018 December. Using a time interval of 24 hours, we extracted daily transactions on the network and formed the Bitcoin graph. We filtered out the network edges that transfer less than B0.3, since ransom amounts are rarely below this threshold.

Ransomware addresses are taken from three widely adopted studies: Montreal, Princeton and Padua. Please see the BitcoinHeist article for references.


Attribute Information:

Features
address: String. Bitcoin address.
year: Integer. Year.
day: Integer. Day of the year. 1 is the first day, 365 is the last day.
length: Integer.
weight: Float.
count: Integer.
looped: Integer.
neighbors: Integer.
income: Integer. Satoshi amount (1 bitcoin = 100 million satoshis).
label: Category String. Name of the ransomware family (e.g., Cryptxxx, cryptolocker etc) or white (i.e., not known to be ransomware).

Our graph features are designed to quantify specific transaction patterns. Loop is intended to count how many transaction i) split their coins; ii) move these coins in the network by using different paths and finally, and iii) merge them in a single address. Coins at this final address can then be sold and converted to fiat currency. Weight quantifies the merge behavior (i.e., the transaction has more input addresses than output addresses), where coins in multiple addresses are each passed through a succession of merging transactions and accumulated in a final address. Similar to weight, the count feature is designed to quantify the merging pattern. However, the count feature represents information on the number of transactions, whereas the weight feature represents information on the amount (what percent of these transactions’ output?) of transactions. Length is designed to quantify mixing rounds on Bitcoin, where transactions receive and distribute similar amounts of coins in multiple rounds with newly created addresses to hide the coin origin.

White Bitcoin addresses are capped at 1K per day (Bitcoin has 800K addresses daily).

Note that although we are certain about ransomware labels, we do not know if all white addresses are in fact not related to ransomware.

When compared to non-ransomware addresses, ransomware addresses exhibit more profound right skewness in distributions of feature values.


Relevant Papers:

1 - Goldsmith, D., Grauer, K., & Shmalo, Y. (2020). Analyzing hack subnetworks in the bitcoin transaction graph. Applied Network Science, 5, 1-20.
2 - Rivera-Castro, R., Pilyugina, P., & Burnaev, E. (2019, November). Topological Data Analysis for Portfolio Management of Cryptocurrencies. In 2019 International Conference on Data Mining Workshops (ICDMW) (pp. 238-243). IEEE.



Citation Request:

@article{akcora2019bitcoinheist,
title={BitcoinHeist: Topological Data Analysis for Ransomware Detection on the Bitcoin Blockchain},
author={Akcora, Cuneyt Gurcan and Li, Yitao and Gel, Yulia R and Kantarcioglu, Murat},
journal={arXiv preprint [Web Link]},
year={2019}
}


Supported By:

 In Collaboration With:

About  ||  Citation Policy  ||  Donation Policy  ||  Contact  ||  CML