# Detecting Financial Bots on the Ethereum Blockchain

arXiv: https://arxiv.org/abs/2403.19530 · HTML: https://mlsanity.com/paper/2403.19530/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 52 references that are in the map.
- First posted on arXiv 28 Mar 2024.

## Builds on (most cited first, 15 of 15)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Non-Fungible Token (NFT): Overview, Evaluation, Opportunities and Challenges](https://mlsanity.com/paper/2105.07447/) (2021)
- [SoK: Decentralized Finance (DeFi)](https://mlsanity.com/paper/2101.08778/) (2021)
- [Quantifying Blockchain Extractable Value: How dark is the forest?](https://mlsanity.com/paper/2101.05511/) (2021)
- [SoK: Transparent Dishonesty: front-running attacks on Blockchain](https://mlsanity.com/paper/1902.05164/) (2019)
- [An Empirical Study of DeFi Liquidations: Incentives, Risks, and Instabilities](https://mlsanity.com/paper/2106.06389/) (2021)
- [Frontrunner Jones and the Raiders of the Dark Forest: An Empirical Study of Frontrunning on the Ethereum Blockchain](https://mlsanity.com/paper/2102.03347/) (2021)
- [When are Deep Networks really better than Decision Forests at small sample sizes, and how?](https://mlsanity.com/paper/2108.13637/) (2021)
- [Cyclic Arbitrage in Decentralized Exchanges](https://mlsanity.com/paper/2105.02784/) (2021)
- [Extracting Godl [sic] from the Salt Mines: Ethereum Miners Extracting Value](https://mlsanity.com/paper/2203.15930/) (2022)
- [GraphSense: A General-Purpose Cryptoasset Analytics Platform](https://mlsanity.com/paper/2102.13613/) (2021)
- [Detecting Bot Activity in the Ethereum Blockchain Network](https://mlsanity.com/paper/1810.01591/) (2018)
- [Assessing the Solvency of Virtual Asset Service Providers: Are Current Standards Sufficient?](https://mlsanity.com/paper/2309.16408/) (2023)

## Built on by (most cited first, 6 of 6)

- [How Agentic Is Agentic Commerce? A Population-Scale Measurement of x402 Adoption and Authenticity](https://mlsanity.com/paper/2607.12575/) (2026)
- [Decoding RWA Tokenized U.S. Treasuries: Functional Dissection and Address Role Inference](https://mlsanity.com/paper/2507.14808/) (2025)
- [Modeling Decisions in Blockchain Analytics: A Leakage-Aware Evaluation of Tree-Based vs. Sequential Models](https://mlsanity.com/paper/2607.27350/) (2026)
- [Compression-Based Behavioral Similarity for Open-World Sybil Discovery on Ethereum](https://mlsanity.com/paper/2607.27370/) (2026)
- [Discovering Persistent Behavioural Patterns for Interpretable Blockchain Forensics](https://mlsanity.com/paper/2608.12864/) (2026)
- [If It Walks Like an Arbitrage: Protocol-Agnostic Detection with Decidable Structural Equivalence](https://mlsanity.com/paper/2608.20377/) (2026)

## Abstract (arXiv)

The integration of bots in Distributed Ledger Technologies (DLTs) fosters efficiency and automation. However, their use is also associated with predatory trading and market manipulation, and can pose threats to system integrity. It is therefore essential to understand the extent of bot deployment in DLTs; despite this, current detection systems are predominantly rule-based and lack flexibility. In this study, we present a novel approach that utilizes machine learning for the detection of financial bots on the Ethereum platform. First, we systematize existing scientific literature and collect anecdotal evidence to establish a taxonomy for financial bots, comprising 7 categories and 24 subcategories. Next, we create a ground-truth dataset consisting of 133 human and 137 bot addresses. Third, we employ both unsupervised and supervised machine learning algorithms to detect bots deployed on Ethereum. The highest-performing clustering algorithm is a Gaussian Mixture Model with an average cluster purity of 82.6%, while the highest-performing model for binary classification is a Random Forest with an accuracy of 83%. Our machine learning-based detection mechanism contributes to understanding the Ethereum ecosystem dynamics by providing additional insights into the current bot landscape.