# Simple Perturbations Subvert Ethereum Phishing Transactions Detection: An Empirical Analysis

arXiv: https://arxiv.org/abs/2408.03441 · HTML: https://mlsanity.com/paper/2408.03441/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 35 references that are in the map.
- First posted on arXiv 6 Aug 2024.

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

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Certified Adversarial Robustness via Randomized Smoothing](https://mlsanity.com/paper/1902.02918/) (2019)
- [RobustBench: a standardized adversarial robustness benchmark](https://mlsanity.com/paper/2010.09670/) (2020)
- [Unlabeled Data Improves Adversarial Robustness](https://mlsanity.com/paper/1905.13736/) (2019)
- [Feature Denoising for Improving Adversarial Robustness](https://mlsanity.com/paper/1812.03411/) (2018)
- [Opportunities and Challenges in Deep Learning Adversarial Robustness: A Survey](https://mlsanity.com/paper/2007.00753/) (2020)
- [Anomaly Detection with Generative Adversarial Networks for Multivariate Time Series](https://mlsanity.com/paper/1809.04758/) (2018)
- [Adversarial Attacks for Tabular Data: Application to Fraud Detection and Imbalanced Data](https://mlsanity.com/paper/2101.08030/) (2021)
- [Fence GAN: Towards Better Anomaly Detection](https://mlsanity.com/paper/1904.01209/) (2019)
- [Adversarial Attacks on Deep Models for Financial Transaction Records](https://mlsanity.com/paper/2106.08361/) (2021)
- [Defending Against Adversarial Attacks Using Random Forests](https://mlsanity.com/paper/1906.06765/) (2019)
- [Improving Detection of Credit Card Fraudulent Transactions using Generative Adversarial Networks](https://mlsanity.com/paper/1907.03355/) (2019)
- [AdvKnn: Adversarial Attacks On K-Nearest Neighbor Classifiers With Approximate Gradients](https://mlsanity.com/paper/1911.06591/) (2019)
- [Generative Adversarial Networks for Bitcoin Data Augmentation](https://mlsanity.com/paper/2005.13369/) (2020)

## Built on by (most cited first, 1 of 1)

- [Fishing for Phishers: Learning-Based Phishing Detection in Ethereum Transactions](https://mlsanity.com/paper/2504.17953/) (2025)

## Abstract (arXiv)

This paper explores the vulnerability of machine learning models, specifically Random Forest, Decision Tree, and K-Nearest Neighbors, to very simple single-feature adversarial attacks in the context of Ethereum fraudulent transaction detection. Through comprehensive experimentation, we investigate the impact of various adversarial attack strategies on model performance metrics, such as accuracy, precision, recall, and F1-score. Our findings, highlighting how prone those techniques are to simple attacks, are alarming, and the inconsistency in the attacks' effect on different algorithms promises ways for attack mitigation. We examine the effectiveness of different mitigation strategies, including adversarial training and enhanced feature selection, in enhancing model robustness.