.png){kind=icon}
Return
SAFER-D: A Self-adaptive Security Framework for Distributed Computing Architectures
.png){kind=icon}
PRE
AI
DOI：10.1007/978-3-032-02138-0_13.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
EDGE
SYSTEMS
Journal
S
IF:
0
Papers:
27
Citations:
0
Organization
Cited Papers
ShareSaveEdge Computing for Autonomous Driving: Opportunities and Challenges.png)
PROCEEDINGS OF THE IEEE
PROCEEDINGS OF THE IEEEIF25.9
PREAI
ShareSave
ShareSaveAiming at methods' wider adoption: Applicability determinants and metricsCOMPUTER SCIENCE REVIEW
COMPUTER SCIENCE REVIEWIF12.7
PREAI
ShareSaveShareSaveShareSaveShareSave
View more