A Decentralized Approach to Threat Intelligence using Federated Learning in Privacy-Preserving Cyber Security

Abstract

The need to protect data privacy and improve threat intelligence in the constantly changing field of cybersecurity has prompted the investigation of novel approaches. This research presents, within the scope of privacy-preserving cyber security, a decentralized method of threat intelligence using Federated Learning (FL). Sensitive threat intelligence data is maintained locally thanks to the decentralized structure of the suggested solution, which reduces the dangers associated with centralized repositories. The cornerstone is federated learning, which permits cooperative model training between dispersed entities without disclosing raw data. Differential privacy and homomorphic encryption are two privacy-preserving strategies that are combined to protect personal information while learning collaboratively. Updates to the model are safely combined and added to a global threat intelligence model without jeopardizing the privacy of the entities involved. The article delves into the nuances of this decentralized strategy, with a focus on building strong security and governance frameworks, being flexible enough to respond to new threats, and continuously improving through feedback loops. This decentralized method offers a viable model for threat intelligence in the future of cyber security by encouraging cooperation, protecting privacy, and strengthening the group's protection against cyberattacks.