國立虎尾科技大學 |

Federated cyber intelligence = federated learning for cybersecurity /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Federated cyber intelligence/ by Hamed Tabrizchi, Ali Aghasi.
其他題名:	federated learning for cybersecurity /
作者:	Tabrizchi, Hamed.
其他作者:	Aghasi, Ali.
出版者:	Cham :Springer Nature Switzerland : : 2025.,
面頁冊數:	ix, 111 p. :ill. (some col.), digital ; : 24 cm.;
Contained By:	Springer Nature eBook
標題:	Artificial Intelligence. -
電子資源:	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:

646849
Artificial Intelligence.

LC Class. No.: QA76.9.A25

Dewey Class. No.: 005.8

Federated cyber intelligence = federated learning for cybersecurity /
LDR:02666nam a2200361 a 4500 001 1161798
003 DE-He213
005 20250424124716.0
006 m d
007 cr nn 008maaau
008 251029s2025 sz s 0 eng d
020 $a 9783031865923 $q (electronic bk.)
020 $a 9783031865916 $q (paper)
024 7 $a 10.1007/978-3-031-86592-3 $2 doi
035 $a 978-3-031-86592-3
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.A25
072 7 $a UR $2 bicssc
072 7 $a UTN $2 bicssc
072 7 $a COM053000 $2 bisacsh
072 7 $a UR $2 thema
072 7 $a UTN $2 thema
082 0 4 $a 005.8 $2 23
090 $a QA76.9.A25 $b T114 2025
100 1 $a Tabrizchi, Hamed. $3 1488747
245 1 0 $a Federated cyber intelligence $h [electronic resource] : $b federated learning for cybersecurity / $c by Hamed Tabrizchi, Ali Aghasi.
260 $a Cham : $c 2025. $b Springer Nature Switzerland : $b Imprint: Springer,
300 $a ix, 111 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a SpringerBriefs in computer science, $x 2191-5776
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.
650 2 4 $a Artificial Intelligence. $3 646849
650 1 4 $a Data and Information Security. $3 1365785
650 0 $a Federated learning (Machine learning) $3 1487729
650 0 $a Computer security. $3 557122
700 1 $a Aghasi, Ali. $3 1488748
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
830 0 $a SpringerBriefs in computer science. $3 883114
856 4 0 $u https://doi.org/10.1007/978-3-031-86592-3
950 $a Computer Science (SpringerNature-11645)