SAFER-D: A Self-Adaptive Security Framework for Distributed Computing Architectures

• URL: http://arxiv.org/abs/2506.16545v1
• Date: Thu, 19 Jun 2025 19:02:35 GMT
• Title: SAFER-D: A Self-Adaptive Security Framework for Distributed Computing Architectures
• Authors: Marco Stadler, Michael Vierhauser, Michael Riegler, Daniel Waghubinger, Johannes Sametinger,
• Abstract summary: The rise of the Internet of Things and Cyber-Physical Systems has introduced new challenges on ensuring secure and robust communication.<n>We present our holistic self-adaptive security framework which combines different adaptation strategies to create comprehensive and efficient defense mechanisms.
• Score: 6.145115042061793
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The rise of the Internet of Things and Cyber-Physical Systems has introduced new challenges on ensuring secure and robust communication. The growing number of connected devices increases network complexity, leading to higher latency and traffic. Distributed computing architectures (DCAs) have gained prominence to address these issues. This shift has significantly expanded the attack surface, requiring additional security measures to protect all components -- from sensors and actuators to edge nodes and central servers. Recent incidents highlight the difficulty of this task: Cyberattacks, like distributed denial of service attacks, continue to pose severe threats and cause substantial damage. Implementing a holistic defense mechanism remains an open challenge, particularly against attacks that demand both enhanced resilience and rapid response. Addressing this gap requires innovative solutions to enhance the security of DCAs. In this work, we present our holistic self-adaptive security framework which combines different adaptation strategies to create comprehensive and efficient defense mechanisms. We describe how to incorporate the framework into a real-world use case scenario and further evaluate its applicability and efficiency. Our evaluation yields promising results, indicating great potential to further extend the research on our framework.

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