Cloudy with a Chance of Anomalies: Dynamic Graph Neural Network for Early Detection of Cloud Services' User Anomalies

In today's digital landscape, ensuring the security of cloud environments is critical for organizational resilience, growth, and operational efficiency. As cloud services become more prevalent, so do sophisticated attacks targeting cloud users, making early detection essential. This paper introduces a novel time-based embedding approach for Cloud Services Graph-based Anomaly Detection (CS-GAD) that leverages a Graph Neural Network (GNN) to detect anomalous user behavior. We propose a dynamic tripartite graph to model interactions among users, actions, and cloud services over time. Using behavioral patterns, our GNN generates user embeddings to enable early detection of anomalies. We evaluate this approach on a novel dataset simulating five real-world attacks: cryptojacking, billing abuse, lateral movement, monitor exploitation, and service targeting. The dataset comprises 107,116 Application Programming Interface (API) calls over 32 days, tracking 79 AWS services, with attacks embedded within legitimate cloud traffic. Our results demonstrate that the proposed method achieves a lower false positive rate and higher detection accuracy than a prevailing method, as evidenced by improved accuracy, precision, recall, and F1-score.