# Identifying malicious accounts in Blockchains using Domain Names and associated temporal properties

arXiv: https://arxiv.org/abs/2106.13420 · HTML: https://mlsanity.com/paper/2106.13420/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 56 references that are in the map.
- First posted on arXiv 25 Jun 2021.

## Builds on (most cited first, 5 of 5)

- [Malicious URL Detection using Machine Learning: A Survey](https://mlsanity.com/paper/1701.07179/) (2017)
- [A Survey on Malicious Domains Detection through DNS Data Analysis](https://mlsanity.com/paper/1805.08426/) (2018)
- [Detecting Malicious Accounts in Permissionless Blockchains using Temporal Graph Properties](https://mlsanity.com/paper/2007.05169/) (2020)
- [Detecting Malicious Accounts showing Adversarial Behavior in Permissionless Blockchains](https://mlsanity.com/paper/2101.11915/) (2021)
- [Intercepting Hail Hydra: Real-Time Detection of Algorithmically Generated Domains](https://mlsanity.com/paper/2008.02507/) (2020)

## Built on by (most cited first, 2 of 2)

- [Machine Learning on Blockchain Data: A Systematic Mapping Study](https://mlsanity.com/paper/2403.17081/) (2024)
- [DNS based In-Browser Cryptojacking Detection](https://mlsanity.com/paper/2205.04685/) (2022)

## Abstract (arXiv)

The rise in the adoption of blockchain technology has led to increased illegal activities by cyber-criminals costing billions of dollars. Many machine learning algorithms are applied to detect such illegal behavior. These algorithms are often trained on the transaction behavior and, in some cases, trained on the vulnerabilities that exist in the system. In our approach, we study the feasibility of using metadata such as Domain Name (DN) associated with the account in the blockchain and identify whether an account should be tagged malicious or not. Here, we leverage the temporal aspects attached to the DNs. Our results identify 144930 DNs that show malicious behavior, and out of these, 54114 DNs show persistent malicious behavior over time. Nonetheless, none of these identified malicious DNs were reported in new officially tagged malicious blockchain DNs.