Scholarlink

This study introduces a novel security architecture within application server middleware, specifically designed for microservices in cloud environments. Addressing the inadequacy of generic cloud security services for microservice-specific threats, the research leverages machine learning to develop advanced vulnerability detection and traffic analysis subsystems. Key contributions include using machine learning for identifying new SQL injection types and analyzing bytecode for security flaws. The STRIDE threat model guides the design principles, balancing security, performance, and user experience. This approach not only advances middleware security research but also holds significant economic potential in the burgeoning microservices market, particularly in high-security-demand sectors like finance and energy.

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Enhancing Microservice Security Through Middleware Architecture: A Machine Learning Approach

  • 会议论文

  • 发表日期：

    2025年

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  • Yong Deng

摘要

This study introduces a novel security architecture within application server middleware, specifically designed for microservices in cloud environments. Addressing the inadequacy of generic cloud security services for microservice-specific threats, the research leverages machine learning to develop advanced vulnerability detection and traffic analysis subsystems. Key contributions include using machine learning for identifying new SQL injection types and analyzing bytecode for security flaws. The STRIDE threat model guides the design principles, balancing security, performance, and user experience. This approach not only advances middleware security research but also holds significant economic potential in the burgeoning microservices market, particularly in high-security-demand sectors like finance and energy.