# Ransomware Detection Using Federated Learning with Imbalanced Datasets

arXiv: https://arxiv.org/abs/2311.07760 · HTML: https://mlsanity.com/paper/2311.07760/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2023.

## Built on by (most cited first, 2 of 2)

- [Enabling Trustworthy Federated Learning in Industrial IoT: Bridging the Gap Between Interpretability and Robustness](https://mlsanity.com/paper/2409.02127/) (2024)
- [Swarm Intelligence-Driven Client Selection for Federated Learning in Cybersecurity applications](https://mlsanity.com/paper/2411.18877/) (2024)

## Abstract (arXiv)

Ransomware is a type of malware which encrypts user data and extorts payments in return for the decryption keys. This cyberthreat is one of the most serious challenges facing organizations today and has already caused immense financial damage. As a result, many researchers have been developing techniques to counter ransomware. Recently, the federated learning (FL) approach has also been applied for ransomware analysis, allowing corporations to achieve scalable, effective detection and attribution without having to share their private data. However, in reality there is much variation in the quantity and composition of ransomware data collected across multiple FL client sites/regions. This imbalance will inevitably degrade the effectiveness of any defense mechanisms. To address this concern, a modified FL scheme is proposed using a weighted cross-entropy loss function approach to mitigate dataset imbalance. A detailed performance evaluation study is then presented for the case of static analysis using the latest Windows-based ransomware families. The findings confirm improved ML classifier performance for a highly imbalanced dataset.