Isbn: 9786630199413 - deep federated learning for intelligent ddos intrusion detection: a secure, scalable, and privacy-preserving framework for ddos intrusion detection using deep federated learning (8 risultati)

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Paperback. Condizione: new. Paperback. The rapid growth of fog-edge computing has introduced new cybersecurity challenges, particularly the increasing threat of Distributed Denial-of-Service (DDoS) attacks. Traditional intrusion detection systems often struggle to provide accurate, scalable, and privacy-preserving solutions in decentralized environments. This book presents a deep federated learning framework that enables collaborative model training without sharing sensitive data, ensuring enhanced privacy and robust threat detection. It explores the integration of deep learning techniques with federated learning to identify DDoS attacks efficiently across distributed fog-edge networks. The proposed framework emphasizes intelligent intrusion detection, improved detection accuracy, reduced communication overhead, and secure distributed learning. This book is intended for researchers, postgraduate students, cybersecurity professionals, and practitioners interested in artificial intelligence, network security, federated learning, and fog-edge computing, providing valuable insights into next-generation AI-driven cybersecurity solutions. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 60 pp. Englisch.

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The rapid growth of fog-edge computing has introduced new cybersecurity challenges, particularly the increasing threat of Distributed Denial-of-Service (DDoS) attacks. Traditional intrusion detection systems often struggle to provide accurate, scalable, and privacy-preserving solutions in decentralized environments. This book presents a deep federated learning framework that enables collaborative model training without sharing sensitive data, ensuring enhanced privacy and robust threat detection. It explores the integration of deep learning techniques with federated learning to identify DDoS attacks efficiently across distributed fog-edge networks. The proposed framework emphasizes intelligent intrusion detection, improved detection accuracy, reduced communication overhead, and secure distributed learning. This book is intended for researchers, postgraduate students, cybersecurity professionals, and practitioners interested in artificial intelligence, network security, federated learning, and fog-edge computing, providing valuable insights into next-generation AI-driven cybersecurity solutions.…

Paperback. Condizione: new. Paperback. The rapid growth of fog-edge computing has introduced new cybersecurity challenges, particularly the increasing threat of Distributed Denial-of-Service (DDoS) attacks. Traditional intrusion detection systems often struggle to provide accurate, scalable, and privacy-preserving solutions in decentralized environments. This book presents a deep federated learning framework that enables collaborative model training without sharing sensitive data, ensuring enhanced privacy and robust threat detection. It explores the integration of deep learning techniques with federated learning to identify DDoS attacks efficiently across distributed fog-edge networks. The proposed framework emphasizes intelligent intrusion detection, improved detection accuracy, reduced communication overhead, and secure distributed learning. This book is intended for researchers, postgraduate students, cybersecurity professionals, and practitioners interested in artificial intelligence, network security, federated learning, and fog-edge computing, providing valuable insights into next-generation AI-driven cybersecurity solutions. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The rapid growth of fog-edge computing has introduced new cybersecurity challenges, particularly the increasing threat of Distributed Denial-of-Service (DDoS) attacks. Traditional intrusion detection systems often struggle to provide accurate, scalable, and privacy-preserving solutions in decentralized environments. This book presents a deep federated learning framework that enables collaborative model training without sharing sensitive data, ensuring enhanced privacy and robust threat detection. It explores the integration of deep learning techniques with federated learning to identify DDoS attacks efficiently across distributed fog-edge networks. The proposed framework emphasizes intelligent intrusion detection, improved detection accuracy, reduced communication overhead, and secure distributed learning. This book is intended for researchers, postgraduate students, cybersecurity professionals, and practitioners interested in artificial intelligence, network security, federated learning, and fog-edge computing, providing valuable insights into next-generation AI-driven cybersecurity solutions. 60 pp. Englisch.…

Taschenbuch. Condizione: Neu. Deep Federated Learning for Intelligent DDoS Intrusion Detection | A Secure, Scalable, and Privacy-Preserving Framework for DDoS Intrusion Detection Using Deep Federated Learning | Manoranjitham S | Taschenbuch | Englisch | 2026 | LAP LAMBERT Academic Publishing | EAN 9786630199413 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand.…