Latest research in Intrusion Detection and Prevention in Cloud focuses on developing advanced frameworks and methodologies to detect, prevent, and mitigate cyber threats in cloud environments. Studies highlight the integration of machine learning, deep learning, and anomaly detection techniques to identify malicious activities, unauthorized access, and unusual patterns in real-time. Research also emphasizes hybrid and distributed IDS/IPS architectures that combine network-based and host-based monitoring, improving scalability, accuracy, and response time in multi-tenant cloud infrastructures. Additionally, these approaches address challenges such as high data volume, dynamic resource allocation, and evolving attack vectors, aiming to enhance the security, reliability, and resilience of cloud computing platforms.