Anomaly Detection in ZkSync Transactions with Unsupervised Machine Learning

Kamil Kaczyński, Aleksander Wiącek

2025

Abstract

This work proposes an anomaly detection model that consists of two different machine learning algorithms, One Class Support Vector Machine and Isolation Forest. The chosen dataset is a publicly available ZkSync data dump. Although there are several articles on anomaly detection using machine learning in blockchains, this one is the first to focus on an Ethereum ZkSync rollup. There were two tasks set. One was to find suspicious transactions in a snippet of the dataset, and the second was to detect possible DDOS attacks, where one vector corresponds to one day of the life of the network. Evaluation of models was based on calculation of accuracy, synthetic accuracy, ROC AUC and PR AUC metrics. The models were fine-tuned on synthetically generated data. The proposed designs show reasonably good performance. The paper can be used as an inspiration to conduct more research on zero-knowledge rollups, as they may have slightly different user behavior than on Ethereum. In addition, the paper provides valuable insight into feature engineering and data processing, which can be useful to some researchers.

Paper Citation

in Harvard Style

Kaczyński K. and Wiącek A. (2025). Anomaly Detection in ZkSync Transactions with Unsupervised Machine Learning. In Proceedings of the 22nd International Conference on Security and Cryptography - Volume 1: SECRYPT; ISBN 978-989-758-760-3, SciTePress, pages 565-570. DOI: 10.5220/0013441600003979

in Bibtex Style

@conference{secrypt25,
author={Kamil Kaczyński and Aleksander Wiącek},
title={Anomaly Detection in ZkSync Transactions with Unsupervised Machine Learning},
booktitle={Proceedings of the 22nd International Conference on Security and Cryptography - Volume 1: SECRYPT},
year={2025},
pages={565-570},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013441600003979},
isbn={978-989-758-760-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 22nd International Conference on Security and Cryptography - Volume 1: SECRYPT
TI - Anomaly Detection in ZkSync Transactions with Unsupervised Machine Learning
SN - 978-989-758-760-3
AU - Kaczyński K.
AU - Wiącek A.
PY - 2025
SP - 565
EP - 570
DO - 10.5220/0013441600003979
PB - SciTePress