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<p>The widespread adoption of microservice architectures has given rise to a new set of software security challenges. These challenges stem from the unique features inherent in microservices. It is important to systematically assess and address these software security issues through effective security risk assessments. However, existing risk assessment approaches, such as expert-based manual assessment, prove inefficient in accurately evaluating the security risks of microservices. Furthermore, the absence of security vulnerability metrics hampers the evaluation of these risks. To address these issues, we propose <i>CyberWise Predictor</i>, a framework designed for predicting and assessing security risks associated with microservice architectures. Our framework employs transformers, which are deep learning-based natural language processing models, to analyze descriptions of vulnerabilities for predicting vulnerability metrics to assess security risks. Our experimental evaluation shows the effectiveness of <i>CyberWise Predictor</i>, achieving an average accuracy of <b>92%</b> in automatically predicting vulnerability metrics for their risk assessment.</p>

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Towards deep learning enabled cybersecurity risk assessment for microservice architectures

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月16日

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  • Majid Abdulsatar,
  • Hussain Ahmad,
  • Diksha Goel,
  • Faheem Ullah

摘要

The widespread adoption of microservice architectures has given rise to a new set of software security challenges. These challenges stem from the unique features inherent in microservices. It is important to systematically assess and address these software security issues through effective security risk assessments. However, existing risk assessment approaches, such as expert-based manual assessment, prove inefficient in accurately evaluating the security risks of microservices. Furthermore, the absence of security vulnerability metrics hampers the evaluation of these risks. To address these issues, we propose CyberWise Predictor, a framework designed for predicting and assessing security risks associated with microservice architectures. Our framework employs transformers, which are deep learning-based natural language processing models, to analyze descriptions of vulnerabilities for predicting vulnerability metrics to assess security risks. Our experimental evaluation shows the effectiveness of CyberWise Predictor, achieving an average accuracy of 92% in automatically predicting vulnerability metrics for their risk assessment.