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Federated cyber intelligence = feder...

Tabrizchi, Hamed.

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Federated cyber intelligence = federated learning for cybersecurity /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Federated cyber intelligence/ by Hamed Tabrizchi, Ali Aghasi.
Reminder of title:	federated learning for cybersecurity /
Author:	Tabrizchi, Hamed.
other author:	Aghasi, Ali.
Published:	Cham :Springer Nature Switzerland : : 2025.,
Description:	ix, 111 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Preface -- Chapter 1 Introduction to Federated Learning -- Chapter 2 Core Concepts of Federated Learning -- Chapter 3 Fundamentals of Cybersecurity -- Chapter 4 Cyber Security Intelligent Systems Based on Federated Learning -- Chapter 5 Closing Thoughts, and Future Directions in Federated Cyber Intelligence.
Contained By:	Springer Nature eBook
Subject:	Computer security. -
Online resource:	https://doi.org/10.1007/978-3-031-86592-3
ISBN:	9783031865923

Federated cyber intelligence = federated learning for cybersecurity /
Tabrizchi, Hamed.

Federated cyber intelligencefederated learning for cybersecurity /[electronic resource] :by Hamed Tabrizchi, Ali Aghasi. - Cham :Springer Nature Switzerland :2025. - ix, 111 p. :ill. (some col.), digital ;24 cm. - SpringerBriefs in computer science,2191-5776. - SpringerBriefs in computer science..

Preface -- Chapter 1 Introduction to Federated Learning -- Chapter 2 Core Concepts of Federated Learning -- Chapter 3 Fundamentals of Cybersecurity -- Chapter 4 Cyber Security Intelligent Systems Based on Federated Learning -- Chapter 5 Closing Thoughts, and Future Directions in Federated Cyber Intelligence.

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.

ISBN: 9783031865923

Standard No.: 10.1007/978-3-031-86592-3doiSubjects--Topical Terms:

540555
Computer security.

LC Class. No.: QA76.9.A25

Dewey Class. No.: 005.8

Federated cyber intelligence = federated learning for cybersecurity /
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505 0 $a Preface -- Chapter 1 Introduction to Federated Learning -- Chapter 2 Core Concepts of Federated Learning -- Chapter 3 Fundamentals of Cybersecurity -- Chapter 4 Cyber Security Intelligent Systems Based on Federated Learning -- Chapter 5 Closing Thoughts, and Future Directions in Federated Cyber Intelligence.
520 $a 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.
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