Federated Cyber Intelligence
Federated Learning for Cybersecurity
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Beschreibung
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. 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.
This book concludes with discussions on emerging trends, including blockchain, edge computing and collaborative threat intelligence. This book is an essential resource for researchers, practitioners and decision-makers in cybersecurity and AI.
Provides a practical guide to federated learning and the ever-evolving needs of cybersecurity professionals Offers insights into modern cyber threats, while proposing federated learning-based solutions Explores the interactions of federated learning and advances in cybersecurity and collaborative intelligence
Autor*in
Hamed Tabrizchi
Themen in »Federated Cyber Intelligence«
Federated Learning Cybersecurity Data Privacy Artificial Intelligence in Cybersecurity Decentralized Machine Learning Security Architecture Data Protection Threat Intelligence Cyber Threats Distributed Computing Privacy Preservation Threat Intelligence Sharing Collaborative Threat Intelligence Cyber Attacks Privacy-Preserving AI