Rademics Research Institute

Abstract

This book chapter explores the integration of Federated Learning (FL) with cybersecurity, focusing on its potential to enhance privacy-preserving detection of cyberattacks in cross-domain networks. As cyber threats evolve in complexity and scale, traditional centralized approaches face limitations in terms of data privacy and scalability. FL offers a decentralized framework where local models are trained on distributed data, ensuring privacy while enabling real-time threat detection. This chapter delves into the core concepts of FL, its relevance to cybersecurity, and its application in mitigating cyber risks across mobile, edge, and IoT devices. Key topics include improving model accuracy, reducing false positives, and integrating FL with advanced threat intelligence platforms for proactive defense. Challenges such as scalability, communication overhead, and the integration of diverse security techniques are discussed. The chapter provides insights into how FL can revolutionize modern cybersecurity frameworks, ensuring robust and adaptive defense mechanisms in complex digital environments.

Introduction

Federated Learning (FL) has emerged as a transformative approach in machine learning, particularly in addressing the growing concerns around privacy, scalability, and data security [1-3]. In the context of cybersecurity, FL offers a decentralized method for training models without the need for centralizing sensitive data [4]. This was especially crucial as the volume of data generated across various domains, including IoT devices, mobile networks, and edge computing, continues to expand [5]. Traditional cybersecurity systems, which rely on centralized data collection and model training, often face significant privacy risks and inefficiencies when handling sensitive information [6]. Federated Learning mitigates these concerns by allowing individual devices or nodes to train local models on their data, while only sharing aggregated updates with a central server [7,8]. This approach significantly reduces the risk of data breaches and privacy violations, making it a critical technology for privacy-preserving cybersecurity [9,10].

The main advantage of Federated Learning in cybersecurity lies in its ability to enhance privacy without sacrificing model accuracy or performance [11,12]. In conventional machine learning systems, centralized data storage often leads to data aggregation challenges and vulnerabilities [13,14]. By decentralizing the learning process, FL ensures that data remains local, and only model parameters are exchanged, protecting user privacy [15-18]. FL allows for continuous learning and real-time updates, which was essential in detecting emerging cyber threats [19]. This was particularly important in dynamic environments where cyberattack techniques evolve rapidly, and traditional models struggle to keep up [20]. FL enables faster adaptation to new threats, as local devices can quickly incorporate new information without waiting for central updates [21]. Thus, Federated Learning provides a robust solution to evolving security challenges while maintaining compliance with stringent privacy regulations such as GDPR [22].