# Anomaly Detection in Blockchain Networks: A Comprehensive Survey

arXiv: https://arxiv.org/abs/2112.06089 · HTML: https://mlsanity.com/paper/2112.06089/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2021.

## Built on by (most cited first, 16 of 16)

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- [Anomaly Detection and Generation with Diffusion Models: A Survey](https://mlsanity.com/paper/2506.09368/) (2025)
- [Anomaly Detection in Multiplex Dynamic Networks: from Blockchain Security to Brain Disease Prediction](https://mlsanity.com/paper/2211.08378/) (2022)
- [Detecting Anomalies in Blockchain Transactions using Machine Learning Classifiers and Explainability Analysis](https://mlsanity.com/paper/2401.03530/) (2024)
- [A Survey on Secure and Private Federated Learning Using Blockchain: Theory and Application in Resource-constrained Computing](https://mlsanity.com/paper/2303.13727/) (2023)
- [Starlit: Privacy-Preserving Federated Learning to Enhance Financial Fraud Detection](https://mlsanity.com/paper/2401.10765/) (2024)
- [A Deep Learning Approach to Anomaly Detection in High-Frequency Trading Data](https://mlsanity.com/paper/2504.00287/) (2025)
- [Machine Learning for Blockchain Data Analysis: Progress and Opportunities](https://mlsanity.com/paper/2404.18251/) (2024)
- [BlockScan: Detecting Anomalies in Blockchain Transactions](https://mlsanity.com/paper/2410.04039/) (2024)
- [Linear Laws of Markov Chains with an Application for Anomaly Detection in Bitcoin Prices](https://mlsanity.com/paper/2201.09790/) (2022)
- [Collaborative Learning for Cyberattack Detection in Blockchain Networks](https://mlsanity.com/paper/2203.11076/) (2022)
- [Machine Learning on Blockchain Data: A Systematic Mapping Study](https://mlsanity.com/paper/2403.17081/) (2024)
- [Early Rug Pull Warning for BSC Meme Tokens via Multi-Granularity Wash-Trading Pattern Profiling](https://mlsanity.com/paper/2603.13830/) (2026)
- [An Improved Anomaly Detection Model for Automated Inspection of Power Line Insulators](https://mlsanity.com/paper/2312.11470/) (2023)
- [Can Artificial Intelligence solve the blockchain oracle problem? Unpacking the Challenges and Possibilities](https://mlsanity.com/paper/2507.02125/) (2025)
- [Mastering the Minority: An Uncertainty-guided Multi-Expert Framework for Challenging-tailed Sequence Learning](https://mlsanity.com/paper/2603.15708/) (2026)

## Abstract (arXiv)

Over the past decade, blockchain technology has attracted a huge attention from both industry and academia because it can be integrated with a large number of everyday applications of modern information and communication technologies (ICT). Peer-to-peer (P2P) architecture of blockchain enhances these applications by providing strong security and trust-oriented guarantees, such as immutability, verifiability, and decentralization. Despite these incredible features that blockchain technology brings to these ICT applications, recent research has indicated that the strong guarantees are not sufficient enough and blockchain networks may still be prone to various security, privacy, and reliability issues. In order to overcome these issues, it is important to identify the anomalous behaviour within the actionable time frame. In this article, we provide an in-depth survey regarding integration of anomaly detection models in blockchain technology. For this, we first discuss how anomaly detection can aid in ensuring security of blockchain based applications. Then, we demonstrate certain fundamental evaluation metrics and key requirements that can play a critical role while developing anomaly detection models for blockchain. Afterwards, we present a thorough survey of various anomaly detection models from the perspective of each layer of blockchain. Finally, we conclude the article by highlighting certain important challenges alongside discussing how they can serve as future research directions for new researchers in the field.