Strategic Approaches to Intrusion Detection in Cloud-IoT Ecosystem

Preface

The rapid growth of Cloud Computing and the Internet of Things (IoT) has transformed various industries by enabling real-time data collection, processing, and automation. However, this increasing interconnectivity also introduces significant security challenges, including data breaches, unauthorized access, and cyber threats. Ensuring the security and privacy of Cloud-IoT environments requires advanced intrusion detection mechanisms, privacy-preserving strategies, and efficient resource management.

Intrusion Detection Systems (IDS) play a crucial role in securing Cloud-IoT ecosystems by identifying suspicious activities and mitigating potential threats. Various approaches, including Machine Learning (ML), Federated Learning (FL), Blockchain, and Hybrid Detection Models, have been explored to enhance intrusion detection capabilities. In addition to IDS, memory management techniques, anomaly detection frameworks, and encryption methods contribute to strengthening security. These aspects are discussed throughout the book, with a focus on AI-driven security mechanisms, behavioral profiling, and energy-efficient solutions for Cloud-IoT systems. By exploring different threat detection strategies, risk mitigation techniques, and cybersecurity frameworks, this book aims to provide a comprehensive understanding of modern security solutions in Cloud-IoT environments.

This book is organized into two parts. The first provides a detailed exploration of security mechanisms essential ...