Malicious Cloud Service Traffic Detection Based on Multi-Feature Fusion

With the rapid growth of cloud computing, malicious attacks targeting cloud services have become increasingly sophisticated and prevalent. To address the limitations of traditional detection methods—such as reliance on single-dimensional features and poor generalization—we propose a novel malicious request detection model based on multi-feature fusion. The model adopts a dual-branch architecture that independently extracts and learns from statistical attributes (e.g., field lengths, entropy) and field attributes (e.g., semantic content of the requested fields). To enhance feature representation, an attention-based fusion mechanism is introduced to dynamically weight and integrate field-level features, while a Gini coefficient-guided random forest algorithm is used to select the most informative statistical features. This design enables the model to capture both structural and semantic characteristics of cloud service traffic. Extensive experiments on the benchmark CSIC 2010 dataset and a real-world labeled cloud service dataset demonstrated that the proposed model significantly outperforms existing approaches in terms of accuracy, precision, recall, and F1 score. These results validate the effectiveness and robustness of our multi-feature fusion approach for detecting malicious requests in cloud environments.