Abstract

The rapid evolution of cyber threats necessitates innovative approaches to enhance global cybersecurity collaboration. Federated Learning (FL) has emerged as a decentralized machine learning paradigm that enables distributed threat intelligence sharing while maintaining data privacy and security. This chapter explores the application of FL for large-scale cybersecurity networks, addressing critical challenges in scalability, security, and communication efficiency. The focus is on optimizing secure aggregation techniques to enable efficient and privacy-preserving model updates across heterogeneous and resource-constrained environments. Key solutions such as hierarchical aggregation, sparse model updates, and blockchain-based enhancements are discussed to mitigate the computational and communication overheads inherent in federated systems. the chapter investigates the integration of advanced cryptographic methods, including homomorphic encryption and differential privacy, to strengthen the security of federated networks against adversarial attacks. By leveraging FL’s potential, organizations can share threat intelligence across global networks without compromising sensitive data, significantly improving real-time cyber threat detection and response. The chapter concludes by identifying future research directions for overcoming existing challenges and further optimizing federated models in cybersecurity.  

Introduction

The rapid escalation of cyber threats across the globe has transformed cybersecurity into an urgent and ongoing priority for governments, businesses, and individuals. Traditional cybersecurity defense mechanisms, such as firewalls, intrusion detection systems, and antivirus software, are often unable to keep up with the sophistication and evolving nature of cyberattacks. Attackers are becoming increasingly adept at evading detection and exploiting vulnerabilities across diverse, interconnected digital infrastructures. To combat these threats, it has become crucial for organizations to collaborate in sharing threat intelligence, which includes valuable data on attack vectors, tactics, techniques, and procedures (TTPs) used by adversaries. However, traditional methods of centralized data sharing often encounter significant challenges related to data privacy, regulatory constraints, and the risks associated with a single point of failure. Federated Learning (FL) offers a novel decentralized solution that enables collaborative machine learning model training while ensuring the confidentiality and privacy of the data shared across participants.

Federated Learning (FL) is a machine learning paradigm that allows multiple participants (such as organizations or security entities) to collaboratively train a model without sharing raw data. Instead of pooling data into a central repository, each participant trains a local model on their own data and then shares only the model updates (i.e., weights and gradients) with a central server. This decentralized approach ensures that sensitive information remains within the local domain, providing an inherent layer of privacy preservation. FL has shown great promise in a variety of domains, such as healthcare, finance, and mobile applications, where privacy is a significant concern. In the context of cybersecurity, FL offers an innovative solution to the growing demand for cross-organization threat intelligence sharing while mitigating the risks associated with exposing sensitive security data. By enabling organizations to share threat intelligence without compromising data privacy, FL can contribute to the collective defense against cyber adversaries, enhancing the overall resilience of the global cybersecurity ecosystem.