Deep Federated Learning for Intelligent DDoS Intrusion Detection: A Secure, Scalable, and Privacy-Preserving Framework for DDoS Intrusion Detection Using Deep Federated Learning - Tapa blanda

Sinopsis

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.

"Sinopsis" puede pertenecer a otra edición de este libro.