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

TL;DR

This paper introduces SAFER-D, a self-adaptive security framework designed to enhance the resilience and response capabilities of distributed computing architectures against cyber threats, especially in IoT and cyber-physical systems.

Contribution

It proposes a holistic, self-adaptive security framework that integrates multiple adaptation strategies to improve defense mechanisms in distributed computing environments.

Findings

Framework demonstrates effective defense in real-world scenarios

Evaluation shows significant potential for enhancing security resilience

Promising results suggest further research opportunities

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.