# Block Hunter: Federated Learning for Cyber Threat Hunting in Blockchain-based IIoT Networks

arXiv: https://arxiv.org/abs/2204.09829 · HTML: https://mlsanity.com/paper/2204.09829/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Apr 2022.

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

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- [OpCode-Based Malware Classification Using Machine Learning and Deep Learning Techniques](https://mlsanity.com/paper/2504.13408/) (2025)
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- [Types of Approaches, Applications and Challenges in the Development of Sentiment Analysis Systems](https://mlsanity.com/paper/2303.11176/) (2023)
- [Securing the Future: Proactive Threat Hunting for Sustainable IoT Ecosystems](https://mlsanity.com/paper/2406.14804/) (2024)
- [Scalable APT Malware Classification via Parallel Feature Extraction and GPU-Accelerated Learning](https://mlsanity.com/paper/2504.15497/) (2025)
- [Fishing for Phishers: Learning-Based Phishing Detection in Ethereum Transactions](https://mlsanity.com/paper/2504.17953/) (2025)
- [PCDM: A Diffusion-Based Data Poisoning Attack Against Federated Learning Systems](https://mlsanity.com/paper/2605.16098/) (2026)

## Abstract (arXiv)

Nowadays, blockchain-based technologies are being developed in various industries to improve data security. In the context of the Industrial Internet of Things (IIoT), a chain-based network is one of the most notable applications of blockchain technology. IIoT devices have become increasingly prevalent in our digital world, especially in support of developing smart factories. Although blockchain is a powerful tool, it is vulnerable to cyber attacks. Detecting anomalies in blockchain-based IIoT networks in smart factories is crucial in protecting networks and systems from unexpected attacks. In this paper, we use Federated Learning (FL) to build a threat hunting framework called Block Hunter to automatically hunt for attacks in blockchain-based IIoT networks. Block Hunter utilizes a cluster-based architecture for anomaly detection combined with several machine learning models in a federated environment. To the best of our knowledge, Block Hunter is the first federated threat hunting model in IIoT networks that identifies anomalous behavior while preserving privacy. Our results prove the efficiency of the Block Hunter in detecting anomalous activities with high accuracy and minimum required bandwidth.