Research on Intrusion Detection Systems (IDS) with Event Logging in Cloud Computing focuses on strengthening cloud security through intelligent, log-driven, and time-aware detection mechanisms. Modern approaches integrate machine learning and deep learning models to analyze event logs, lifecycle data, and time-series patterns for identifying abnormal activities across multi-cloud and IoT-cloud environments. Event logging plays a crucial role in correlating system activities and detecting anomalies in real time, while hybrid models enhance detection accuracy and scalability. Many frameworks explore time-aware analysis to capture behavioral deviations using event timestamps and process logs. Current trends emphasize building IDS architectures that combine event correlation, anomaly detection from distributed audit logs, and adaptive learning through federated or edge-based systems to achieve secure, efficient, and resilient cloud environments.