Federated Learning for Privacy-Preserving Threat Intelligence Sharing

Abstract

The growing sophistication of cyber threats has necessitated the need for more collaborative approaches to threat intelligence sharing. However, the sharing of sensitive data across organizations raises significant privacy and security concerns. Federated learning (FL), a distributed machine learning (ML) approach, offers a promising solution to this challenge by enabling organizations to collaboratively train models on decentralized data without exchanging the raw data itself. This paper explores the use of federated learning for privacy-preserving threat intelligence sharing, focusing on its potential to enhance the detection and mitigation of cyber threats while maintaining the confidentiality of sensitive organizational data. By leveraging federated learning, organizations can contribute to a collective intelligence network that detects emerging threats, shares insights, and improves cybersecurity without compromising privacy. This paper delves into the technical aspects of federated learning, its application in threat intelligence sharing, and the benefits and challenges of implementing such systems in real-world scenarios.