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<p>Kubernetes has emerged as the backbone of modern cloud-native environments, enabling efficient orchestration of containerized applications. However, its dynamic nature exposes it to sophisticated cyber threats, including privilege escalation, reconnaissance, and denial-of-service attacks. This paper presents a novel framework that combines real-time multi-class threat detection with adaptive deception to enhance Kubernetes security. The framework integrates KServe for scalable machine learning-based threat classification, CICFlowMeter for feature extraction, and KubeDeceive for dynamic deployment of decoys, all governed by the MAPE-K loop for continuous adaptation. Evaluations demonstrate high detection accuracy (up to 91%), efficient resource utilization, and effective attacker engagement, with decoy success rates reaching 93%. The results underscore the framework’s ability to proactively mitigate threats, maintain system resilience, and provide actionable intelligence. This unified approach represents a scalable and adaptable defense mechanism for Kubernetes environments, catering to the needs of dynamic and resource-intensive cloud infrastructures.</p>

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Real-time multi-class threat detection and adaptive deception in Kubernetes environments

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月15日

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  • Abdelrahman Aly,
  • Ahmed M. Hamad,
  • Mirvat Al-Qutt,
  • Mahmoud Fayez

摘要

Kubernetes has emerged as the backbone of modern cloud-native environments, enabling efficient orchestration of containerized applications. However, its dynamic nature exposes it to sophisticated cyber threats, including privilege escalation, reconnaissance, and denial-of-service attacks. This paper presents a novel framework that combines real-time multi-class threat detection with adaptive deception to enhance Kubernetes security. The framework integrates KServe for scalable machine learning-based threat classification, CICFlowMeter for feature extraction, and KubeDeceive for dynamic deployment of decoys, all governed by the MAPE-K loop for continuous adaptation. Evaluations demonstrate high detection accuracy (up to 91%), efficient resource utilization, and effective attacker engagement, with decoy success rates reaching 93%. The results underscore the framework’s ability to proactively mitigate threats, maintain system resilience, and provide actionable intelligence. This unified approach represents a scalable and adaptable defense mechanism for Kubernetes environments, catering to the needs of dynamic and resource-intensive cloud infrastructures.