Future-proof your digital infrastructure with this essential book, which provides a comprehensive exploration of both traditional and advanced machine and deep learning models to implement resilient and intelligent intrusion detection systems for securing complex cloud-IoT environments. The rapid growth of cloud computing and the Internet of Things has transformed industry 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. This book explores various advanced methods to achieve these goals, including machine and deep learning models, to protect cloud-IoT systems against cyber threats. This book covers both traditional and advanced techniques to implement intrusion detection systems and provides detailed comparative analysis. By offering practical insights, readers will gain a deeper understanding of how to effectively implement intelligent security solutions, ensuring resilience, privacy, and protection against evolving cyber threats in cloud-IoT environments. Readers will find the volume: Provides comprehensive coverage of topics like machine and deep learning for intelligent security; Explores cyber-IoT systems and intrusion detection systems for identifying suspicious activities and mitigating potential threats; Discusses various security mechanisms to safeguard the cloud-IoT environment and implement various techniques to detect intrusions early on. Audience Research scholars and industry professionals in information technology, artificial intelligence and cybersecurity looking to innovate cybersecurity for cloud computing and IoT.