Wanru Shao

The paper proposes an ensemble learning framework combined with SHAP-based Explainable AI (XAI) to achieve robust and interpretable anomaly detection for network traffic in embedded systems.

The paper proposes MVRAF, a data-driven framework that quantifies vulnerability risk in large-scale cloud infrastructure by integrating multiple attack attributes and analyzing cumulative risk distributions.

The paper introduces an open-source security framework that significantly improves cloud infrastructure security assessment by unifying identity and resource data, reducing false positives, and automating remediation.