Wanru Shao

Wanru Shao

Misconfiguration, excessive privilege, and tool fragmentation remain the main reasons why enterprise cloud environments are breached. Recent reports on cloud-native application protection note that most incidents can be traced back to configuration or identity errors rather than platform flaws, and that organizations still need separate tools to watch Kubernetes, OpenStack, and infrastructure-as-code. To address this gap, this paper presents an open-source cloud-infrastructure security framework built with a microservice architecture. The framework integrates four core services: 1) identity and access control unification, 2) configuration-baseline intelligent checking over Kubernetes and OpenStack assets, 3) real-time threat monitoring based on Falco-style runtime rules and ELK-based analytics, and 4) automated remediation that consumes Terraform plans and Checkov/OPA policy results to roll back or harden resources. It provides automated deployment, supports 50-200-node clusters, and exposes uniform REST and gRPC interfaces for extension. In an enterprise-grade testbed, vulnerability-assessment time was reduced from 120 min as baseline toolchain to 18 min, with false-positive rate below 5%. After continuous deployment, the number of observable security events dropped by 62%. The project is released under Apache 2.0 to lower entry cost by about 40% for small and medium teams.

Wanru Shao

Network security threats in embedded systems pose significant challenges to critical infrastructure protection. This paper presents a comprehensive framework combining ensemble learning methods with explainable artificial intelligence (XAI) techniques for robust anomaly detection in network traffic. We evaluate multiple machine learning models including Random Forest, Gradient Boosting, Support Vector Machines, and ensemble methods on a real-world network traffic dataset containing 19 features derived from packet-level and frequency domain characteristics. Our experimental results demonstrate that ensemble methods achieve superior performance, with Random Forest attaining 90% accuracy and an AUC of 0.617 on validation data. Furthermore, we employ SHAP (SHapley Additive exPlanations) analysis to provide interpretable insights into model predictions, revealing that packet_count_5s,inter_arrival_time, and spectral_entropy are the most influential features for anomaly detection. The integration of XAI techniques enhances model trustworthiness and facilitates deployment in security-critical embedded systems where interpretability is paramount.

Wanru Shao

The exponential growth of Common Vulnerabilities and Exposures (CVE) disclosures poses significant challenges for enterprise security management, necessitating automated and quantitative risk assessment methodologies. Existing vulnerability analysis approaches suffer from three critical limitations: (1) lack of systematic severity quantification models that integrate heterogeneous attack attributes, (2) insufficient exploration of latent correlations among risk factors, and (3) absence of cumulative risk distribution analysis for prioritized remediation. To address these challenges, we propose MVRAF (Multi-dimensional Vulnerability Risk Assessment Framework), a comprehensive data-driven framework for large-scale CVE security analysis. Our framework introduces three key innovations: (1) a Vulnerability Severity Quantification Model that transforms CVSS attributes into normalized risk metrics through weighted aggregation of exploitability and CIA impact scores, (2) a Risk Factor Correlation Analysis module that captures statistical dependencies among attack vectors, complexity, and privilege requirements via correlation matrices, and (3) an Empirical Risk Distribution mechanism that enables cumulative threat assessment for resource allocation optimization. Extensive experiments on 1,314 real-world CVE records from the National Vulnerability Database demonstrate that our framework effectively identifies risk hotspots, with 46.2% of network-based vulnerabilities classified as high-risk and strong correlations observed between CIA impacts and overall severity scores.