Machine Learning and AI-based Approaches To Detect Anomalous Behaviour In The Cloud

Abstract

The quick uptake of cloud computing has made it more difficult to guarantee the dependability and security of cloud systems. The dynamic and dispersed nature of cloud infrastructures frequently makes it difficult for traditional monitoring tools to keep up, therefore identifying unusual activity is essential to averting possible security breaches, resource abuse, and system breakdowns. This work investigates the use of artificial intelligence (AI) and machine learning (ML)-based techniques to identify unusual activity in cloud environments. The suggested framework detects anomalies in user behavior, network traffic, and resource usage patterns by utilizing supervised, unsupervised, and semi-supervised learning approaches. To increase detection accuracy and reduce false positives, sophisticated techniques including ensemble models, deep learning, and reinforcement learning are used. Feature engineering techniques and explainable AI (XAI) tools are integrated to improve model interpretability and trustworthiness. The study also addresses the challenges of scalability, real-time detection, and adapting to evolving attack vectors. Results from experiments show how effective the suggested methods are on actual datasets, underscoring their potential to completely transform cloud security by facilitating proactive anomaly detection and mitigation techniques.