A Survey of Intrusion Detection Systems Based on Machine Learning for Cloud Security

The fast growth of cloud computing has resulted in greater dependence on scalable and agile infrastructure. Nevertheless, this change has also brought about serious cybersecurity issues, especially intrusion detection. Conventional Intrusion Detection Systems (IDS) are challenged by detecting new attacks, dealing with massive cloud environments, and keeping up with real-time threat detection. Machine Learning (ML) has shown great potential to improve IDS functionality by automating anomaly detection, enhancing accuracy, and adjusting to changing threats. This survey presents a complete overview of ML-based IDS for cloud security, emphasizing supervised, unsupervised, and deep learning methods. It investigates the benefits and weaknesses of current methods, emphasizing their detection performance, scalability, and computation load. Moreover, this study examines widely utilized datasets, touches upon adversarial attacks and privacy issues, and explores upcoming trends such as Explainable AI, Zero Trust Architecture, and adaptive IDS models. By filling the gap between research and real-world implementation, this survey seeks to inform future developments in cloud environment security against advanced cyber threats.