Federated RNN-Based Detection of Ransomware Attacks: A Privacy-Preserving Approach

The rise of ransomware as a predominant cybersecurity threat has necessitated the development of innovative detection mechanisms capable of adapting to the rapidly evolving nature of such attacks. In response to this challenge, federated learning, combined with Recurrent Neural Networks (RNNs), offers a novel approach to ransomware detection that preserves data privacy while maintaining high detection accuracy. The research presented explores the implementation of a federated learning framework, where RNN models are trained across decentralized datasets without sharing sensitive data, ensuring compliance with privacy regulations. Through comprehensive experiments, the study demonstrates that the federated RNN model achieves comparable performance to centralized models, with the added benefit of enhanced data security. The results demonstrate the potential of federated learning as a scalable and robust solution for cybersecurity applications, particularly in environments where data confidentiality is paramount. The findings further highlight the broader implications of adopting federated learning techniques in the development of privacy-preserving machine learning models, paving the way for future advancements in secure and effective ransomware detection.