Abstract

The fast implementation of cloud computing in the various industries has revolutionized the digital world but at the same time posed sophisticated security issues. Cloud environments are dynamic in nature (distributed architecture, short life time resources, variable workload). Static security systems that act upon and refer to static rules as well as systems that are signature-based have become outdated in safeguarding against dynamic cyber attacks that are real-time. Following the trend of attackers using sophisticated approaches such as polymorphic malware, intelligent intrusion, and automated attack framework, the requirement of intelligent, scalable, and responsive security frameworks has become essential. Adaptive Machine Learning (AML) frameworks become an intriguing approach, as it allows to detect threats in real-time by learning and adjusting to the changes of patterns, behaviors and threats environment in cloud environments. This paper entails an in-depth examination of the AML technique which shall be applied to real-time cloud security. The review explores the most salient components of AML which include incremental learning, online learning, ensemble modeling, and drift detection, which can help AML systems stay steady over known and emerging risks. The common challenges of application of AML such as concept drift, high false positives, data privacy limitations as well as scalability issues are also addressed in the paper. In addition, it examines industry trends, new opportunities such as federated learning and explainable AI, and presents upcoming research topics, such as quantum-enhanced machine learning to be used in cybersecurity. The results reaffirm that AML is no longer a mere reactive measure but also a proactive, mandatory component used to protect present-day cloud architectures against a dynamically changing cyber-threat environment.