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Learning-driven Zero Trust in Distributed Computing Continuum Systems

2023-11-29 08:41:06
Ilir Murturi, Praveen Kumar Donta, Victor Casamayor Pujol, Andrea Morichetta, Schahram Dustdar

Abstract

Converging Zero Trust (ZT) with learning techniques can solve various operational and security challenges in Distributed Computing Continuum Systems (DCCS). Implementing centralized ZT architecture is seen as unsuitable for the computing continuum (e.g., computing entities with limited connectivity and visibility, etc.). At the same time, implementing decentralized ZT in the computing continuum requires understanding infrastructure limitations and novel approaches to enhance resource access management decisions. To overcome such challenges, we present a novel learning-driven ZT conceptual architecture designed for DCCS. We aim to enhance ZT architecture service quality by incorporating lightweight learning strategies such as Representation Learning (ReL) and distributing ZT components across the computing continuum. The ReL helps to improve the decision-making process by predicting threats or untrusted requests. Through an illustrative example, we show how the learning process detects and blocks the requests, enhances resource access control, and reduces network and computation overheads. Lastly, we discuss the conceptual architecture, processes, and provide a research agenda.

Abstract (translated)

将零信任(ZT)与学习技术相结合可以解决分布式计算连续系统(DCCS)中的各种操作和安全挑战。将集中式ZT架构视为不适用于计算连续(例如,具有有限连接性和可见性的计算实体等)的观点。同时,在计算连续中实现分散式ZT需要理解基础设施限制以及增强资源访问管理决策的新方法。为了克服这些挑战,我们提出了一个专为DCCS设计的全新学习驱动ZT概念架构。我们的目标是通过引入轻量学习策略(如表示学习(ReL))来提高ZT架构的服务质量。通过一个示例,我们证明了学习过程能够检测并阻止请求,提高资源访问控制,并降低网络和计算开销。最后,我们讨论了概念架构、过程以及研究议程。

URL

https://arxiv.org/abs/2311.17447

PDF

https://arxiv.org/pdf/2311.17447.pdf


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