@inproceedings{228afe3ea4834995b20ab4d89190e236,
title = "Disrupting Ransomware Actors on the Bitcoin Blockchain: A Graph Embedding Approach",
abstract = "Ransomware is a growing problem and significant threat to cybersecurity in the United States. One primary vector for ransomware payments is the Bitcoin network. Network science techniques are a potential approach to analyze ransomware payment networks to discover salient ransomware actors. In this study, we propose a design framework for labeling nodes in a ransomware payment network and identifying key ransomware Bitcoin addresses that can be targeted for disruption. By leveraging semi-supervised graph embedding methodology and updating the loss function of a prevailing algorithm, GraphSAGE, to manage dataset imbalance, we identify key wallets in our ransomware network. We demonstrate the utility of our approach with a case study identifying a Bitcoin wallet that has been reported as a ransomware actor as recently as December 2021 and has transferred over $450 million in Bitcoin.",
keywords = "Bitcoin, Ransomware, blockchain, graph embedding, node labeling, semi-supervised, weighted cross entropy loss",
author = "Benjamin Ampel and Kaeli Otto and Sagar Samtani and Hsinchun Chen",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 20th IEEE International Conference on Intelligence and Security Informatics, ISI 2023 ; Conference date: 02-10-2023 Through 03-10-2023",
year = "2023",
doi = "10.1109/ISI58743.2023.10297290",
language = "English (US)",
series = "Proceedings - 2023 IEEE International Conference on Intelligence and Security Informatics, ISI 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "Proceedings - 2023 IEEE International Conference on Intelligence and Security Informatics, ISI 2023",
}