Название: Federated Cyber Intelligence: Federated Learning for Cybersecurity
Автор: Hamed Tabrizchi, Ali Aghasi
Издательство: Springer
Серия: SpringerBriefs in Computer Science
Год: 2025
Страниц: 119
Язык: английский
Формат: pdf (true), epub (true), mobi
Размер: 10.1 MB
This book offers a detailed exploration of how Federated Learning can address critical challenges in modern cybersecurity. It begins with an introduction to the core principles of Federated Learning (FL). Then it highlights a strong foundation by exploring the fundamental components, workflow, and algorithms of Federated Learning, alongside its historical development and relevance in safeguarding digital systems. The subsequent sections offer insight into key cybersecurity concepts, including confidentiality, integrity, and availability. It also offers various types of cyber threats, such as malware, phishing, and advanced persistent threats. This book provides a practical guide to applying Federated Learning in areas such as intrusion detection, malware detection, phishing prevention, and threat intelligence sharing. It examines the unique challenges and solutions associated with this approach, such as data heterogeneity, synchronization strategies and privacy-preserving techniques.
