Significant Digits: Using Large-Scale Blockchain Data to Predict Fraudulent Addresses
Cryptography and Security
2023-01-06 v1
Abstract
Blockchain systems and cryptocurrencies have exploded in popularity over the past decade, and with this growing user base, the number of cryptocurrency scams has also surged. Given the graphical structure of blockchain networks and the abundance of data generated on these networks, we use graph mining techniques to extract essential information on transactions and apply Benford's Law to extract distributional information on address transactions. We then apply a gradient-boosting tree model to predict fraudulent addresses. Our results show that our method can detect scams with reasonable accuracy and that the features generated based on Benford's Law are the most significant features.
Cite
@article{arxiv.2301.01809,
title = {Significant Digits: Using Large-Scale Blockchain Data to Predict Fraudulent Addresses},
author = {Jared Gridley and Oshani Seneviratne},
journal= {arXiv preprint arXiv:2301.01809},
year = {2023}
}
Comments
Accepted at the IEEE Big Data 2022 Conference