Intelligent Fraud Detection in Cloud Computing Using AI-Enabled Machine Learning and Network Security Analytics

Abstract

Cloud computing platforms host a vast array of services and large-scale transactions, making them attractive targets for a spectrum of fraudulent activities — from account takeover and payment fraud to lateral movement and exfiltration. Traditional rule-based defenses and siloed network controls struggle to keep up with adversaries who exploit scale, automation, and new service models. This paper examines intelligent fraud detection at the intersection of machine learning (ML) and network security within cloud environments. We survey threat surfaces unique to clouds, characterize the data sources available (logs, network flows, API calls, telemetry), and review ML approaches — supervised, unsupervised, semi-supervised, and deep learning — that are applicable to cloud fraud detection. We present an end-to-end research methodology for building cloud-aware fraud detection systems, including data collection and labeling, feature engineering at multiple abstraction levels, model selection, concept-drift handling, online evaluation, interpretability, and deployment considerations (scalability, privacy, cost). Advantages (enhanced detection, automated adaptation) and disadvantages (data imbalance, adversarial evasion, privacy concerns, operational cost) are discussed. Results from representative experiments and literature benchmarks show that hybrid architectures combining network-level anomaly detection with transaction-level classification produce the best trade-offs between precision and recall in cloud settings. We conclude with recommendations and a prioritized agenda for future work: federated learning for multi-tenant privacy, adversarially robust models, and standardized evaluation benchmarks for cloud fraud.