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.
Die Inhaltsangabe kann sich auf eine andere Ausgabe dieses Titels beziehen.
Hamed Tabrizchi earned his Bachelor's and Master's degrees in Computer Science from Shahid Bahonar University of Kerman, Iran, in 2017 and 2019, respectively. At the present time, he is a Ph.D. candidate and university lecturer in the computer science department at the University of Tabriz. As a person who was awarded as a talented student of the University of Tabriz in 2020 and a national elite in 2023. In addition to being an experienced artificial intelligence researcher, Hamed has served as a consultant in fields such as cloud computing and cloud computing security for various startups and industrial projects since 2017. Beyond his research endeavors, he has lectured at universities, led workshops, and contributed extensively to leading scientific journals, amassing over 900 citations. Furthermore, he has reviewed more than 300 papers as verified in Web of Science for international journals and conferences.
Ali Aghasi holds a PhD in Computer Engineering from the University of Isfahan, with a research focus on optimizing energy consumption in cloud data centers using AI-driven methods. Currently serving as the Cybersecurity Manager at the IT Center of Shahid Bahonar University of Kerman, Ali Aghasi leads innovative projects deploying artificial intelligence to detect malicious activities and enhance the resilience of IT infrastructures. His expertise bridges the domains of AI, cybersecurity, and energy-efficient computing, making him a leader in advancing secure, efficient systems.
„Über diesen Titel“ kann sich auf eine andere Ausgabe dieses Titels beziehen.
EUR 2,28 für den Versand innerhalb von/der USA
Versandziele, Kosten & DauerEUR 3,78 für den Versand von Vereinigtes Königreich nach USA
Versandziele, Kosten & DauerAnbieter: PBShop.store UK, Fairford, GLOS, Vereinigtes Königreich
PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000. Bestandsnummer des Verkäufers S0-9783031865916
Anzahl: 1 verfügbar
Anbieter: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irland
Zustand: New. Bestandsnummer des Verkäufers V9783031865916
Anzahl: 15 verfügbar
Anbieter: GreatBookPrices, Columbia, MD, USA
Zustand: New. Bestandsnummer des Verkäufers 50117457-n
Anzahl: 15 verfügbar
Anbieter: Kennys Bookstore, Olney, MD, USA
Zustand: New. Bestandsnummer des Verkäufers V9783031865916
Anzahl: 15 verfügbar
Anbieter: Grand Eagle Retail, Bensenville, IL, USA
Paperback. Zustand: new. Paperback. 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. This book offers a detailed exploration of how federated learning can address critical challenges in modern cybersecurity. This book provides a practical guide to applying federated learning in areas such as intrusion detection, malware detection, phishing prevention, and threat intelligence sharing. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Bestandsnummer des Verkäufers 9783031865916
Anzahl: 1 verfügbar
Anbieter: California Books, Miami, FL, USA
Zustand: New. Bestandsnummer des Verkäufers I-9783031865916
Anzahl: Mehr als 20 verfügbar
Anbieter: GreatBookPrices, Columbia, MD, USA
Zustand: As New. Unread book in perfect condition. Bestandsnummer des Verkäufers 50117457
Anzahl: 15 verfügbar
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
Zustand: New. In. Bestandsnummer des Verkäufers ria9783031865916_new
Anzahl: Mehr als 20 verfügbar
Anbieter: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Deutschland
Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 124 pp. Englisch. Bestandsnummer des Verkäufers 9783031865916
Anzahl: 2 verfügbar
Anbieter: Books Puddle, New York, NY, USA
Zustand: New. Bestandsnummer des Verkäufers 26403933027
Anzahl: 4 verfügbar