# Clean Up the Mess: Addressing Data Pollution in Cryptocurrency Abuse Reporting Services

arXiv: https://arxiv.org/abs/2410.21041 · HTML: https://mlsanity.com/paper/2410.21041/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 71 references that are in the map.
- First posted on arXiv 28 Oct 2024.

## Builds on (most cited first, 14 of 14)

- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [Data mining for detecting Bitcoin Ponzi schemes](https://mlsanity.com/paper/1803.00646/) (2018)
- [Ransomware Payments in the Bitcoin Ecosystem](https://mlsanity.com/paper/1804.04080/) (2018)
- [BlockSci: Design and applications of a blockchain analysis platform](https://mlsanity.com/paper/1709.02489/) (2017)
- [On the Economic Significance of Ransomware Campaigns: A Bitcoin Transactions Perspective](https://mlsanity.com/paper/1804.01341/) (2018)
- [How to Peel a Million: Validating and Expanding Bitcoin Clusters](https://mlsanity.com/paper/2205.13882/) (2022)
- [Spams meet Cryptocurrencies: Sextortion in the Bitcoin Ecosystem](https://mlsanity.com/paper/1908.01051/) (2019)
- [SoK: Cryptojacking Malware](https://mlsanity.com/paper/2103.03851/) (2021)
- [Watch Your Back: Identifying Cybercrime Financial Relationships in Bitcoin through Back-and-Forth Exploration](https://mlsanity.com/paper/2206.00375/) (2022)
- [When the cookie meets the blockchain: Privacy risks of web payments via cryptocurrencies](https://mlsanity.com/paper/1708.04748/) (2017)
- [Characterizing Cryptocurrency Exchange Scams](https://mlsanity.com/paper/2003.07314/) (2020)
- [Cybercrime Bitcoin Revenue Estimations: Quantifying the Impact of Methodology and Coverage](https://mlsanity.com/paper/2309.03592/) (2023)
- [Conning the Crypto Conman: End-to-End Analysis of Cryptocurrency-based Technical Support Scams](https://mlsanity.com/paper/2401.09824/) (2024)

## Abstract (arXiv)

Cryptocurrency abuse reporting services are a valuable data source about abusive blockchain addresses, prevalent types of cryptocurrency abuse, and their financial impact on victims. However, they may suffer data pollution due to their crowd-sourced nature. This work analyzes the extent and impact of data pollution in cryptocurrency abuse reporting services and proposes a novel LLM-based defense to address the pollution. We collect 289K abuse reports submitted over 6 years to two popular services and use them to answer three research questions. RQ1 analyzes the extent and impact of pollution. We show that spam reports will eventually flood unchecked abuse reporting services, with BitcoinAbuse receiving 75% of spam before stopping operations. We build a public dataset of 19,443 abuse reports labeled with 19 popular abuse types and use it to reveal the inaccuracy of user-reported abuse types. We identified 91 (0.1%) benign addresses reported, responsible for 60% of all the received funds. RQ2 examines whether we can automate identifying valid reports and their classification into abuse types. We propose an unsupervised LLM-based classifier that achieves an F1 score of 0.95 when classifying reports, an F1 of 0.89 when classifying out-of-distribution data, and an F1 of 0.99 when identifying spam reports. Our unsupervised LLM-based classifier clearly outperforms two baselines: a supervised classifier and a naive usage of the LLM. Finally, RQ3 demonstrates the usefulness of our LLM-based classifier for quantifying the financial impact of different cryptocurrency abuse types. We show that victim-reported losses heavily underestimate cybercriminal revenue by estimating a 29 times higher revenue from deposit transactions. We identified that investment scams have the highest financial impact and that extortions have lower conversion rates but compensate for them with massive email campaigns.