Contextual Intelligence for Anomaly Detection in Zero Trust Based Architectures

The Edge-Cloud Continuum stands out as a promising paradigm that brings computation closer to the data source to improve efficiency and reduce latency. However, deploying edge devices with limited resources poses significant challenges, as they play a vital role in the continuum and require trusted interactions for secure and reliable data exchange. The Zero Trust Architecture (ZTA) paradigm has emerged as a potential solution to address trust issues, operating on the principle of ‘never trust, always verify’. Yet, ZTAs increase overhead due to the increased computational demands of its dynamic policies. To balance the benefits of heightened security with potential performance overhead, this work proposes TRUDI, a technique to enhance Trust via RepUtation management and anomaly DetectIon in zero trust-based networks. TRUDI combines a performance-based trust assessment mechanism with an anomaly detection mechanism, allowing more consecutive sessions without resource-intensive authentication and authorisation, while maintaining system integrity. TRUDI has been evaluated in edge computing environments with limited resources, with simulation results confirming its effectiveness in zero trust settings.